COMPETITIVE BALANCE IN THE NATIONAL HOCKEY LEAGUE AFTER THE SALARY CAP

A THESIS

Presented to

The Faculty of the Department of Economics and Business

The Colorado College

In Partial Fulfillment of the Requirements for the Degree

Bachelor of Arts

By

Meryn Grant

May/2008 COMPETITIVE BALANCE IN THE NHL AFTER THE SALARY CAP

Meryn Grant

May, 2008

Economics and Business

Abstract

Previous studies have proposed that a salary cap may be the only measure that could promote competitive balance and maintain the financial viability of professional sport leagues. As a result of the 2004-05 lockout and the New Collective Bargaining Agreement a salary cap was implemented in the National Hockey League (NHL).Theory behind the salary cap suggests that the budget restraint will cause the redistribution of top talent, and a depression of salaries. This thesis hypothesizes that changes in team success that occurred after the 2004-05 lockout were the result of the implementation of the salary cap and the redistribution of top talent. This thesis uses an adjusted measure of team standard deviation of win percentage to investigate if the implementation of the salary cap changed team success and competitive balance. The variation in top talent and the implementation of the salary cap were found to be the primary determinants of changes in team success over the period investigated. In order to substantiate these findings, another model was estimated using conference rank as a dependent variable. The presence of top talent and median salary were found to be the primary determinants of conference rank. These findings, in conjunction with previous research, suggest that the salary cap has changed the competitive balance in the NHL with its initial implementation, through a redistribution of top ranked talent.

KEYWORDS: (National Hockey League, Salary Cap, Competitive Balance) ON MY HONOR, I HAVE NEITHER GIVEN NOR RECEIVED UNAUTHORIZED AID ON THIS THESIS

Signature TABLE OF CONTENTS

I INTRODUCTION 1

II LITERATURE REVIEW 8 2.1 General Theory of Sports Leagues...... 10 2.2 Uncertainty of Outcome Hypothesis (UOH) and Competitive Balance...... 15 2.3 Coase Theorem...... 21 2.4 Subsidization Measures...... 28 2.5 Salary Caps...... 31 2.6 Pay and Performance...... 35 2.7 Salary Determination...... 39 2.8 Summary...... 45

III THEORY 49 3.1 Measures of Competitive Balance...... 49 3.1.1 Dispersion of Win Percentages...... 50 3.1.2 Within Season Standard Deviation...... 51 3.1.3 Herfindahl-Hirschman Index...... 53 3.1.4 Team Standard Deviation and the Competitive Balance Ratio...... 56 3.2 Determinants of Competitive Balance...... 66 3.2.1 The Presence of Top Talent...... 68 3.2.2 Coaching Ability...... 71 3.2.3 Team Chemistry and Composition...... 72 3.2.4 Summary of the Theoretical ModeL...... 74 3.3 Conclusion...... 75 IV METHODOLOGY 76 4.1 Data Set...... 77 4.2 Dependent Variables...... 79 4.2.1 Team Standard Deviation...... 80 4.2.2 Conference Rank...... 82 4.3 Empirical Models ...... 85 4.4 Independent Variables...... 88 4.4.1 Presence of Top Talent Variables ...... 88 4.4.2 Coaching Variables...... 95 4.4.3 Team Chemistry and Composition Variables...... 98 4.4.4 Salary Cap Variable...... 100 4.4.5 Division Variable...... 103 4.5 Conclusion...... 106

V RESULTS AND CONCLUSIONS 107 5.1 Model One Results...... 108 5.1.2 Model One Conclusions...... 120 5.2 Model Two Results...... 119 5.2.1 Model Two Conclusions...... 127 5.3 Limits and Further Research...... 128 5.4 Conclusion...... 129

Appendix...... 134

Sources Consulted...... 136 LIST OF TABLES

2.1 Chapter Outline and Important Conclusions...... 9

3.1 Standard Deviation of Winning Percentages in the NHL...... 60

3.2 NHL Attendance Totals...... 61

3.3 Team Standard Deviation of Win Percentage Comparison...... 63

4.1 Eastern and Western Conference Teams in the NHL...... 84

4.2 Independent Variable Definitions...... 87

4.3 Measures of Talent by Position...... 89

4.4 Points Assigned for Top Forwards and Defense...... 90

4.5 Points Assigned for Top Goalies...... 91

4.6 Division Assignments...... 103

5.1 Model One Regression Results ...... '" . 109

5.2 Model One Regression Statistics...... 110

5.3 Elasticity of Top Talent Coefficients for Model One...... 112

5.4 Model Two Regression Results...... 121

5.5 Model Two Regression Statistics...... 122

5.6 Elasticity of Top Talent Coefficients for Model Two...... 123 LIST OF FIGURES

3.1 Equation for Within Season Standard Deviation of Win Percentages...... 51

3.2 Equation for Ideal Within Season Standard Deviation of Win Percentages.... 52

3.3 Equation for Herfindahl-Hirschman Index...... 54

3.4 Equation for Deviations in the Herfindahl-Hirschman Index...... 55

3.5 Equation for Humphreys' (2002) Team Standard Deviation...... 57

3.6 Equation for Average Variation in Teams' Win Percentages...... 58

3.7 Equation for Average Variation in Win Percentage in Each Season...... 58

3.8 Equation for Humphreys' (2002) Competitive Balance Ratio...... 58

3.9 Equation for Adjusted Team Standard Deviation...... 65

3.10 Determinants of Competitive Balance...... 67

4.1 Equation for Adjusted Team Standard Deviation...... 80

4.2 General Function For Model One...... 85

4.3 Variable Specification for Model One .. " ..... , ...... '" ...... 86

4.4 Variable Specifications for Model One with TOPTALENTSD...... 86

4.5 General Function for Model Two...... 86

4.6 Variable Specifications for Model Two...... 86

4.7 Sample Calculation of TOPOFF Variable...... 90

4.8 Equation for TOPOFFSD...... 93 4.9 Equation for TOPDEFSD...... 93

4.10 Equation for TOPGOALlESD...... 93

4.11 Equation for TOPT ALENTSD...... 94

4.12 Equation for TOPT ALENT...... 94

4.13 Equation for Salary Range Variable...... 98

4.14 Division Variable Assignments...... 103

5.1 Division Variable Assignments...... 115 ACKNOWLEDGEMENTS

I would like to thank my teacher, advisor and friend Julie Chesley, for her encouragement and insight throughout this entire project. Your support and guidance gave me the confidence to explore, discover and learn more than I thought possible. I would also like to thank Aju Fenn, for his contributions and direction that pushed me to achieve. Additionally I would like to thank my parents, who have been more supportive throughout this project and my college experience than I could have hoped or imagined. I will always cherish everything you have taught me through your wisdom and unwavering support. CHAPTER I

INTRODUCTION

In the 2004-05 the National Hockey League (NHL) became the first professional sports league to lose an entire season. The NHL now also holds records for the most games lost due to a work stoppage (1230) and the longest lasting shutdown (310 days) in sports history. 1 As Paul Staudohar, a Sports economist renowned for his writings on sports league conflicts states: "the future of the league [was] threatened by the frequent wrangling over money and power".2 During the lockout the National Hockey League

Players Association (NHLPA) represented by union head Bob Goodenow and the

National Hockey League, comprised of club owners lead by Commissioner Gary

Bettman, struggled to create an agreeable Collective Bargaining Agreement. This chapter will discuss the causes and major issues on the bargaining table for the lockout, and the new collective bargaining agreement. The implementation of the salary cap will be the focus ofthis investigation as it was central to the conflict and has major implications for the future of the NHL and other professional sports in North America.

Team owners in sports leagues have used lockouts to put pressure on players to acquiesce to their demands. Players' unions in professional sports have also used strikes

I Paul D. Staudohar, "The Hockey Lockout 0[2004 -- 05," Monthly Labor Review 128, no. 12 (12 2005): 23-29.

2 Ibid, 2.

1 2 to persuade the team owners to accede to their demands; these are all part of a "frequent wrangling over money and power".3 In 1992, Bob Goodenow led the NHLPA to their first ever strike and adopted an adversarial position to the owners. The league responded by hiring Gary Bettman from the National Basketbal Association (NBA) who designed and implemented basketball's salary cap - the first in modem day sports. 4 The NHL had a previous lockout in 1994-95 when half a season was lost as the league pursued "cost certainty". The league sought to limit salary spending by requiring large revenue teams to contribute to revenue sharing with low revenue teams, so these teams could compete more effectively in the attainment of top quality players. Players, however, were not open to the idea of having their salaries limited. The lockout ended in mid January when the owners dropped the payroll tax idea but gained a salary cap for rookies whose salaries had been rising rapidly. The league was able to salvage part of a season by cutting its regular season 84 games to 48 games.

In the period between the 1994-95 lockout and the 2004-05 lockout, the financial problems of the league escalated, raising the stakes for the new collective bargaining agreement. In 2004, average salaries in the NHL had almost tripled since the 1993-94 season, however revenues had not kept pace causing substantial league wide losses. 5

There have been two different studies on the profitability of the NHL. The NHL hired

Arthur Levitt to examine its finances and he found that the league lost $273 million in the

3 Ibid.

4 Ibid, 2.

5 Ibid. 3

2002-03 season, with 19 teams losing money and 11 teams being profitable.6 Later

Forbes magazine also did a study of the NHL and found conflicting results: in 2002-03 the report found that teams lost $123 million. The discrepancy was attributed to "creative accounting" regarding what the league considers revenue. The example used in the article was that the Chicago Blackhawks claimed no revenue from the 212 suites in its home arena, however rent was paid to Blackhawks' owner William Wirtz who owned half the arena in a separate corporation.7 These discrepancies in revenue are important as the league contended in negotiations that it spend 76% of revenues on player salaries, roughly 20% more than other professional leagues. There is no dispute that the NHL is losing money, however the issue of "creative accounting" is important as it demonstrates the reason for the players' lack oftrust in the league's willingness to contribute a fair share of revenue to salaries.

Similar to the lockout in 1994-95, in 2004-05 there were numerous issues on the bargaining table, but above all was the league's desire for "cost certainty" to promote financial viability of the league. The numerous issues on the bargaining table included higher fines for misbehavior, reducing the schedule of games, minimum salaries, playoff bonuses for players, free agency, operation of the salary arbitration process, and revenue sharing. The central issue that remained throughout the conflict was the league's insistence that a salary cap be implemented while the player's union was equally adamant that it wanted salaries based on market conditions and would not agree to cap payrolls as

6 Ibid.

7 Ibid. 4 they could not trust the owner's revenue reporting methods.8 Rising salaries, combined with attendance and television ratings trending lower as the perceived competitiveness of the league declined, contributed to the league's immobile stance in negotiations.

Finally after 310 days, on July 13, 2005, the NHL and the NHLPA reached an agreement that would change the NHL forever. Through the lockout teams lost an estimated $2 billion in revenue from tickets, media, sponsorship and concessions, and players lost an estimated $1 billion in salaries.9 The NHL also risked losing its spot among the major professional sports in the US, as fans quickly tired of what seemed like endless squabbling over power and money, not the love of the game that is idolized by so many fans. Although both sides lost a lot in the lengthy work stoppage, economist Paul

Staudohar who has written on several professional sports lockouts states that "the hockey lockout is notable in that the owners achieved such a dominant outcome". 10 The players' union appeared to have underestimated the need for economic restructuring and the commitment and financial resources of the Gary Bettman and the owners. The owners implemented a 'hard salary cap' and achieved a 24% percent reduction in all salary contracts. Several other changes were made in the interest of increasing the competitiveness and excitement of the game to increase attendance and promote television ratings. The players received less than previous offers made in February as the league attempted to preserve a part of the 2004-05 season.

While definitions and restrictions for specific players in the Salary Cap agreement

8 Ibid.

9 Ibid.

10 Ibid. 5

are extensive, for the purpose of this study the major structure of the salary cap will be covered. The salary cap was set at $39 million for the 2005-06 season and the salary floor was set at $21.5 million. I I The NHL also took several measures to ensure that teams could not cheat. Teams may not circumvent the salary cap through side deals with players including the provisions of gifts, or payment for endorsement deals. In future years, the players are guaranteed 54 percent of "hockey-related" revenues, and the salary cap will be adjusted each year to coincide with that figure. 12 The players' share of "hockey-related revenue" increases if revenues rise. They get 55 percent when NHL revenues hit $2.2 billion, 56 percent at $2.4 billion, and 57 percent at $2.7 billion. \3 To ensure the correct revenue sharing between players and owners, a percentage of player salaries could be placed in escrow. 14 When total league revenues are determined at the end of the season, the escrow account will be divided among players and owners to ensure that the target has been met. The NHL will evaluate payrolls several times during the season to determine whether the escrow account is necessary.

There are also several regulations dedicated to individual salaries. The minimum salary a player can earn in a year was raised to $450,000, and no player can earn more than 20% of the team salary cap.15 Entry-level players can earn a maximum of $850,000

11 The Salary Cap figure includes salaries and bonuses and is measured in real dollars paid out in the applicable calendar year.

12 "Collective Bargaining Agreement F AQs," in National Hockey League Enterprises [database online]. [cited 2008]. Available from http://www.nhl.com/nhlhq/cba/index.html.

13 "Understanding the NHL Salary Cap," in The New York Times Company [database online]. [cited 2008]. Available from http://proicehockey.about.comlodlthenewnhl/a/salarycapexpl.htm. Anonymous, Collective Bargaining Agreement F AQs

14 Ibid.

15 Ibid. 6 per year. Signing bonuses are also capped at 10% of the player's salary for entry level players. Perfonnance bonuses, including those paid out by the league are also limited for various groups of players and values. 16

Despite their dominant outcome the owners continue to contend that a salary cap was necessary to maintain financial viability, and promote competition in the league to drive attendance and revenue. Payroll limits have existed in the NBA since the 1984-85 season and in the National Football League (NFL) since 1994. However the NHL 'hard salary cap' is markedly different, as teams may not under any circumstances circumvent the cap through signing bonus or luxury taxes and it involved the largest single cut to player salaries. However many players maintain that the salary cap is simply a measure to reduce the player's share of revenue and increase profits for the owners.

Sports economists acknowledge that Competitive Balance - a tenn used to refer to the competitiveness of the league, ultimately drives attendance and revenue.

Competitive Balance refers to the relative quality of teams in a league that makes the outcome of games unpredictable, and variations in team success that makes the outcome of the league unpredictable. Literature on competitive balance is dedicated to finding how changes in league structure and rules have changed the competitive balance within the leagues. Several sports leagues have implemented various measures including salary caps to promote financial viability and competitive balance among their teams. The NHL salary cap is significant as it represents the result of the longest lockout in professional sports and is widely recognized as a victory for the owners. It is not known what effects the salary cap has had on the league other than to reduce the cost of salaries for owners, and the share of revenue for players. The salary cap may be justifiable if it will promote

16 Ibid. 7

the competitiveness needed to increase revenues and attendance in the NHL. This study

will investigate what effect the salary cap has had on the competitiveness of the league.

The salary cap will only be successful in improving the financial viability of the league by changing the competitive balance of the league.

As a result of the salary cap several changes in the league are observable. The distribution of top players changed dramatically as teams were forced to reduce their payrolls to comply with the cap. These changes include the Colorado Avalanche missing the playoffs for the first time in franchise history, Edmonton, the eighth seeded team in the playoffs, making it to the Stanley Cup Finals, Buffalo making the playoffs for the first time in three years as a home seed, Carolina winning the Stanley Cup after finishing 11 th in their conference the year before, as well as a significant rank shake-up for almost every team. 17 These observations justify a hypothesis that the salary cap has changed the competitiveness of the NHL.

This investigation will proceed as follows: Chapter II will present previous literature on competitive balance, Chapter III will outline the theory behind the model used in this investigation, Chapter IV will discuss the specific methodology for the model, and Chapter V will present the results of the empirical study and its implications for professional sports.

17 "NHL Stats Machine Control Panel." in National Hockey League Enterprises [database online]. [cited 2007]. Available from www.nh1.comlsuperstats. CHAPTER II

LITERATURE REVIEW

The chapter will first introduce the roots of the General Theory of Sports Leagues,

to provide a proper background for the rest of this investigation. Next, literature will be

presented on the importance of competitive balance and the Uncertainty of Outcome

Hypothesis. This will be integral to understanding why the pursuit of competitive balance has been so widely studied and why business measures promoting it are of such great importance to the health of sports leagues. Then, literature on the Coase Theorem and determinants of competitive balance will be presented including quantitative studies on the effect of several league measures directed at the promotion of competitive balance.

This will provide a historical and theoretical background for the idea of a salary cap.

Finally salary cap theory will be presented along with an investigation of it's two major assumptions: 1) That Pay and Performance are related. 2) That salaries are a direct reflection of production and/or talent. Table 2.1 provides a summary ofthe major research streams and their contributions as they relate to this salary cap investigation and competitive balance.

8 9

TABLE 2.1

Chapter Outline and Important Conclusions

Section Title Important Conclusions The General - Team revenues are driven simultaneously by uncertainty of Theory of outcome and winning. Sports Leagues - Profit Maximization and differing revenue functions that are a result of differences in market size, result in consistently unequal playing strengths between teams, or a lack of competitive balance. - The primary source of the lack of competitive balance is a difference in market size or revenue potential - Competitive Imbalance is detrimental to the financial viability and survival of Professional Sports Leagyes Uncertainty of - Lack of Competitive balance and predictable outcomes result Outcome in lack of interest and attendance creating financially unviable Hypothesis and leagues. Competitive - Notions of competitive balance are solely important as they Balance create the uncertainty of outcome, which _generates interest. Coase Theorem - Labor market restrictions have been consistently shown to have little effect on competitive balance - The concentration of talent is detrimental to competitive balance. Subsidization - Many subsidization measures have been implemented in Measures leagues with little success - Revenue sharing agreements are found to actually decrease competitive balance by decreasing winning incentives for small clubs. - Salary cap may be the only measure to protect profit incentives while improving competitive balance Salary Cap - Salary caps theoretically restrict the amount of talent that can Theory be acquired by large market teams, and depress salaries, resulting in a more even distribution of talent - There is a very limited amount of empirical research on salary caps, and none on a salary cap 'tout court' as implemented in theNHL Pay and - There is a causal link in many sports leagues between team Performance payroll and performance as salary cap theory predicts. - No study applies directly to the NHL.

Salary - Salaries in the NHL are primarily determined by skill. This Determination condition is necessary for the success of the salary cap. in the NHL 10

General Theory of Sports Leagues

Professional Sports leagues are in the business of winning. Organizations within

sports leagues are markedly different from the traditional businesses that economic profit

maximization theory focuses on, as they have to be concerned not only with profit

maximization, but with winning and the health of the league as a whole. In order for a

league to succeed in the long run, it must establish a certain level of competition (a.k.a.

competitive balance) to retain fan interest and thus revenue. This body ofliterature

examines the importance of competitive balance to the league as a whole. This section

will display the progression of the field of competitive balance in sport economics as

authors strove to build models of professional sports leagues to explain club behavior in

economic terms.

Simon Rottenberg (1956) pioneered the idea that revenue was driven by uncertainty of outcome and winning. He was the first to acknowledge that teams with greater talent will generate more revenue and be able to invest in more talent, decreasing the competitiveness ofleague. 1 EI-Hodiri and Quirk (1971) quantified this idea by incorporating differences in market size and revenue functions. 2 Fort and Quirk (1992) acknowledged strong evidence of competitive imbalance in all American professional sports leagues, as a result of differences in revenue potential. 3 Vrooman (1995) built on

I Simon Rottenberg, "The Baseball Players' Labor Market," The Journal ofPolitical Economy 64, no. 3 (June 1956): 242.

2 M El-Hodiri and J Quirk, "The Economic Theory of a Professional Sports League," Journal ofPolitical Economy 79, no. 6 ((November-December) 1971): 1302-1319.

3 James P. Quirk and Rodney D. Fort, Pay Dirt: The Business ofProfessional Team Sports (Princeton, N.J.: Princeton University Press, 1992),538. 11

EI-Hodiri and Quirk's model to create a more dynamic model of the sports league.4 All

of these studies acknowledge that the major source of competitive imbalance is a

difference in market size, resulting in different revenue functions, and revenue potential.

Simon Rottenberg was one of the first authors to explore the concept of

competitive balance in his 1956 work on Sports Leagues as businesses. He acknowledged that the biggest attraction of professional sports was the uncertainty regarding their

outcome. It was this uncertainty that drove demand and revenue. In 1956 gate receipts generated almost all revenue. A team that always had a chance of winning would have greater attendance receipts than less talented teams.s Rottenberg (1956) contends that this will ultimately lead to an unequal investment in talent: teams with greater talent will generate more revenue and be able invest in more talent, increasing the gap between teams. 6 The lack of talent on lower revenue teams will cause the outcome of contests to be predictable and make the overall product in the league less appealing.7 Rottenberg

(1956) stated that ultimately "no team can be successful unless its competitors also survive" and prosper. 8 This idea spurred the argument for the reserve clause, which put a limitation on the labor market in baseball to prevent high caliber talent from migrating to large revenue teams. Rottenberg (1956) performed a test to look at the concentration of pennants in Major League Baseball (MLB) from 1920-1951, and found an unequal

4 John Vrooman, "A General Theory of Professional Sports Leagues," Southern Economic Journal 61, no. 4 (041995): 971-990.

5 Rottenberg, The Baseball Players' Labor Market, 242.

6 Ibid.

7 Ibid.

8 Rottenberg, The Baseball Players' Labor Market, 108. 12

distribution of pennants even with the invention of the reserve clause.9 To fix the

competitive balance problem in MLB, Rottenberg's (1956) most plausible solutions

include revenue sharing and a ceiling for spending on salaries (a salary cap). IO

EI-Hodiri and Quirk (1971) created the first formal modeling of a professional

sports league. Their most critical finding was that individual team profit maximization

conflicts with equal playing strengths among teams except in cases of identical team

revenue functions. II They concluded that any rules structure of professional sports is

relatively ineffective in balancing playing strengths. The imbalance was attributed to the

differences in drawing potentials of clubs. 12 Larger clubs have larger markets to draw

from and can generate more revenue to spend on talent. Small clubs with a lack of

revenue potential will not be able to afford similar talent, and will be unable to compete.

In his groundbreaking work on pay and performance in baseball, Scully (1989)

found that "team revenues are directly related to the clubs win percentage and to the size of the market from which it draws fans.,,13 Scully (1989) proposed that market size and 14 team performance independently affect team revenue. Scully (1989) pioneered the idea that team revenues are directly related to team win percentages, and that each player's productivity contributes a certain amount to the win percentages and thus contributes

9 Ibid.

10 Rottenberg, The Baseball Players' Labor Market, 256.

II Mohamed EI-Hodiri and James Quirk, "An Economic Model of a Professional Sports League," The Journal a/Political Economy 79, no. 6 (November 1974): 1302.

12 Ibid.

13 Gerald Scully, The Business a/Major League Baseball (Chicago: University of Chicago Press, 1989), 154.

14 Ibid. 13

directly to the team's revenue. IS In his model Scully (1989) uses players'marginal

revenue products - MRPs, (the additional revenue a player provides to a team, because

extra talent contributes to additional wins) to investigate the influence of talent on

revenue. While Rottenberg theorized that "if [ owners] behaved as rational maximizers

playing talent will be more or less equally distributed,,16, Scully contends that larger

markets value talent and wins more than small markets, and thus may acquire more talent

in an unrestricted situation, driving competitive imbalance. 17

Fort and Quirk (1992) find "ample evidence oflong-tenn competitive imbalance

in each of the 5 professional leagues" (National Football League, National Basketball

Association, National Hockey League, National League and American League) despite

changes in business approaches designed to equalize team strengths. IS Following

Rottenberg's 1956 argument, Fort and Quirk cite differences in the revenue potential of

markets (or market size) as the primary deterrent of competitive balance. 19 Market size

generates different marginal revenue curves because teams with higher revenue potential

derive greater increases in revenue from each additional increase in winning percentage.

Profit maximizing teams will only purchase talent until the cost of an additional unit of

talent is equal to its marginal revenue.20 This means that at market equilibrium a high

15 Ibid.

16 Rottenberg, The Baseball Players' Labor Market, 255.

17 Scully, The Business ofMajor League Baseball

18 Quirk and Fort, Pay Dirt: The Business ofProfessional Team Sports, 270.

19 Quirk and Fort, Pay Dirt: The Business ofProfessional Team Sports, 538.

20 Ibid. 14

revenue potential team will demand more talent than a team with lower revenue potential,

creating competitive imbalance.21

Vrooman (1995) added several specific propositions based on profit maximization

theory to add to previous literature. First he establishes that the level of dominance or

competitive balance will vary depending on the revenue elasticity of winning for large

and small market teams.22 However he still concludes that large market teams will always

dominate small market teams to some degree.23 He adds that even ifmarket size is a

significant determinant ofteam revenue, a small market team can still be competitive if

fans have a higher elasticity of demand for winning than fans in large markets.24

Competition will be more balanced as the cost elasticity of winning increases. Finally

competition will become more balanced as diseconomies of market size increase to

counteract revenue advantages?5 These conclusions are not in disagreement with

previous theory but simply expand and attempt to quantify the notions of earlier theorists.

These authors establish that profit maximizing teams in a Sports League will not

be of equal strength because of differences in revenue potential and revenue functions.

Profit maximizing teams will consume talent until the marginal cost of talent is equal to

the marginal revenue generated by talent. 26 In general, large market teams have greater

revenue potential and thus it may be profitable for these teams to consistently acquire

21 Ibid.

22 Vrooman, A General Theory ofProfessional Sports Leagues, 975.

23 Vrooman, A General Theory ofProfessional Sports Leagues, 987.

24 Vrooman, A General Theory ofProfessional Sports Leagues, 975.

25 Vrooman, A General Theory ofProfessional Sports Leagues, 971-990.

26 Quirk and Fort, Pay Dirt.' The Business ofProfessional Team Sports, 538. 15

more talent than small market teams. This results in competitive imbalance, where teams

are consistently not of relatively equal strength.

Uncertainty of Outcome Hypothesis (UOH) and Competitive Balance

This section further explores the importance of competitive balance as it relates to

the uncertainty of outcome and financial viability. Essentially it captures why competitive

balance is so important and worthy of attention. Several authors that wrote about the

General Theory of Sports Leagues found that a certain level of competition is necessary

to drive revenue and interest. This section attempts to quantify the importance of

competitive balance by examining the Uncertainty of Outcome Hypothesis. The

Uncertainty of Outcome Hypothesis (UOH) predicts that a certain level of uncertainty of

outcome in a contest is needed to drive attendance, where a contest is a single game or

end of the season placement in a league. Rottenberg (1956) argues that one-sided contests

and playoff races would indeed drive away fans causing diminishing marginal revenues

of winning percentages. EI-Hodiri and Quirk (1971) found that predictable outcomes

depress attendance.

Fort and Quirk (1992) cite the continued success of the Cleveland Browns

winning the AFC championship 4 years in a row as a primary example for the UOH.27 In

the first year of Cleveland's streak it outdrew every team in the NFL except the New

York Giants with 57,000 fans per home game. However in the fourth year of its

dominance the Browns were drawing under 30,000 per home game, posing financial

27 Ibid. 16

viability problems for the entire NFL.28 The lack of uncertainty caused by competitive

imbalance diminished fan interest.29 Several other studies establish the relationship that

as uncertainty of outcome in a contest increases, attendance and interest generated

revenues also increase, including Zimbalist (2001) who argues that revenues are much

more sensitive to team performance and uncertainty than 40 or 50 years ago. 30

Competitive balance is therefore increasingly important because it produces the

uncertainty of outcome necessary for league longevity.

Fort and Maxcy (2003) provide a comment on the important of Uncertainty of

Outcome Hypothesis research to the field of competitive balance. They acknowledge two

major branches of literature - the Analysis of Competitive Balance (ACB) which looks at

determinants and changes over time in competitive balance, and the Uncertainty of

Outcome Hypothesis (UOH) literature which looks at the effect of competitive balance

on fan interest/welfare. They argue that changes in the ACB branch of literature may

coincide with importance changes in UOH analysis but UOH analysis may find

significant changes in fan preferences toward competitive balance that ACB analysis

does not capture.31 This is because articles directed toward the Analysis of Competitive

Balance examine changes in competitive balance using measures of relative strength and

dispersion of team success, however these measures only represent mathematical notions

28 Ibid.

29 Ibid.

30 V fOoman, A General Theory ofProfessional Sports Leagues, 971-990. Brad R. Humphreys, "Alternative Measures of Competitive Balance in Sports Leagues," Journal ofSports Economics 3, no. 2 (2002): 133.

31 Rodney Fort and Maxcy Joel, "Competitive Balance in Sports Leagues: An Introduction," Journal of Sports Economics 4, no. 2 (2003): 154. 17 of balance. Fort and Maxcy (2003) argue that the study of competitive balance is inconsequential without UOH, because the objective of business measures to improve competitive balance is to generate greater revenue and fan interest. 32

This thesis takes the uncertainty of outcome hypothesis as generally established and presents studies that advance the explanation of competitive balance as they relate to league attendances. Studies that examine changes in competitive balance from the ACB branch of literature will only be examined insofar as they relate to league measures to improve competitive balance. This chapter and the theory chapter will also present critical ideas about the measurement of competitive balance in relation to the UOH.

Humphreys (2002) presents an innovative measure of competitive balance for the

UOH field ofliterature: the Competitive Balance Ratio (CBR), that relates competitive balance and the uncertainty of outcome to fan interest. Humphreys advocates that fan interest is ultimately important in measures of competitive balance because "if a league lacks competitive balance, fan interest in the weaker teams will fall and, eventually, fan interest in the stronger teams will also decline. ,,33 In contrast with other measures of competitive balance, the CBR attempts to capture season-to-season changes in relative standings. He suggests that it is not only the closeness of a single contest but the uncertainty about the final rankings in a league that matter to fan interest.34 To show the application ofthe CBR, Humphreys (2002) compares it to several alternative measures currently in use in ACB literature. He shows that the CBR captures decreases in

32 Ibid.

33 Humphreys, Alternative Measures a/Competitive Balance in Sports Leagues, 133.

34 Ibid. 18 competitive balance in the 1910s, 1920s and 1990s, that other measures do not capture because they fail to distinguish between periods of high and low turnover in successful teams, and focus solely on the relative strength of teams in a season. 35 Humphreys (2002) suggests that the CBR could be the most useful measure of competitive balance to UOH literature, not only because it shows as competitive balance declines the attendance also declines, but also it captures the change in attendance that results when teams continually dominate a league.36 Ultimately Humphreys (2002) argues that measures of competitive balance are only important as they relate to fan interest, as he establishes that competitive balance is only important to the league as it affects fan interest and revenues.

Eckard (2001) uses evidence of declining attendance during winning or

'contending' streaks as a basis for an investigation on the effect of Free-Agency in MLB.

He argues that teams will experience diminishing marginal returns with each year they are in contention for American League and National League pennants.37 He acknowledges that "less [competitive] balance within a league means teams with good, middling and poor records tend to repeat [their record] year after year".38 Using a measure that combines the relative strength of teams within a season and the variation in team specific records over time, he finds that Free Agency has caused an increase in competitive balance in MLB. He observed that the concentration of pennant winners has decreased, the variance in team winning percentages over time has increased and the

35 Humphreys, Alternative Measures o/Competitive Balance in Sports Leagues, 147.

36 Ibid.

37 E. Woodrow Eckard, "Free Agency, Competitive Balance and Diminishing Returns to Pennant Contention," Journal o/Economic Inquiry 39, no. 3 (2001): 430.

38 Ibid, 433. 19

within season variance of win percentages has decreased.39 In an additional section

Eckard (2001) also investigates the effect that winning streaks have on attendance. The

primary variable of years since start of streak supports the hypothesis of diminishing

returns (in revenue/attendance) to additional years of pennant contention.4o Using this

support, he argues that incentives should be reduced for teams to bid continually for the

services of top players, and free agency allows players to move away from pennant

contenders.41 He finds that free-agency has increased competitive balance in Major

League Baseball. This is contrary to Analysis of Competitive Balance (ACB) literature

which generally finds free-agency to reduce or have no effect on competitive balance.

Contrary to most UOH literature that advocates the necessity of business measures to improve competitive balance, Forrest et al (2005) contend that business measures that intend to equalize playing talent across teams, may actually decrease the uncertainty of outcome. Forrest et al (2005) assert that the Uncertainty of Outcome literature has ignored the phenomenon of home advantage.42 These authors believe that the closeness of a contest depends not only on the relative strengths of home and away teams, but also on the strength of home advantage. According to the authors "where home advantage is strong the most evenly poised matches will be those between a relatively weak team playing at home and a relatively strong team playing away".43

39 Ibid.

40 Ibid.

41 Ibid.

42 David Forrest et aI., "Home Advantage and the Debate about Competitive Balance in Professional Sports Leagues," Journal a/Sports Sciences 23, no. 4 (04 2005): 439-445.

43 Ibid. 20

Through quantitative analysis they find support for their hypothesis, as the ratio of probability of a home win to an away win is high.44 In a further simulation using data from the English Football League, the authors find that equalizing relative team strength variables, the probability ratio would increase to favor the home team disproportionately, and decrease uncertainty of outcome, decreasing attendance by about 26%.45 This study acknowledges that uncertainty of outcome is necessary for attendance and interest, but contends that measures directed towards equalizing team strengths will actually hurt attendance due to the importance of home advantage.

In summary, UOH literature emphasizes the importance of competitive balance as it relates to fan interest. The models used by Eckard (2001) and Humphreys (2002) show that the relative strength of teams in a season, and variation of a team's win record over time are important to fan interest.46 Humphreys (2002) and Eckard (2001) both assert that measures of competitive balance should only be considered insofar as they reflect changes in fan interest. 47 Eckard (2001) and Humphreys (2002) both conclude that measures of team variation in win percentage is important in generating interest and attendance for sports leagues. 48 Forrest et al (2005) provide an alternative argument to main-stream UOH arguments that call for the equalization of team strengths by exploring

44 Ibid.

45 Ibid.

46 Humphreys, Alternative Measures o/Competitive Balance in Sports Leagues, 133.; Eckard, Free Agency, Competitive Balance and Diminishing Returns to Pennant Contention, 430.

47 Humphreys, Alternative Measures o/Competitive Balance in Sports Leagues, 133.; Eckard, Free Agency, Competitive Balance and Diminishing Returns to Pennant Contention, 430.

48 Humphreys, Alternative Measures o/Competitive Balance in Sports Leagues, 133.; Eckard, Free Agency, Competitive Balance and Diminishing Returns to Pennant Contention, 430. 21 the influence of horne advantage on the outcome of contests.49 They argue that equalization will actually decrease the uncertainty of outcome, yet they provide no comment for situations where strong teams have horne advantage further decreasing uncertainty of outcome. 50 From these studies, one can conclude that Uncertainty of

Outcome is essential to providing financial viability to teams, and changes in relative standings within leagues (and thus competitive balance) explain changes in interest that were previously unexplained in other studies on changes in competitive balance.

Coase Theorem

The previous sections have established the importance of competitive balance and the primary source of the lack of competitive balance. This section will discuss business measures that create labor market restrictions undertaken by leagues in an effort to improve competitive balance. Several studies have investigated the effect of these measures including the reserve clause, free-agency, and reverse order amateur draft on competitive balance.

After Rottenberg's (1956) study on the importance of competitive balance, the implementation of the reserve clause in the MLB attempted to limit the movement of high caliber talent to larger revenue teams. The reserve clause, contained in all standard player contracts, stated that, upon the contract's expiration the rights to the player were to be retained by the team to which he had been signed. 51 The reserve clause was intended

49 Forrest et ai., Home Advantage and the Debate about Competitive Balance in Professional Sports Leagues, 439-445.

50 Ibid.

51 Eckard, Free Agency, Competitive Balance and Diminishing Returns to Pennant Contention, 430. 22 to benefit small or less successful teams who received first pick of players with the reverse-order entry draft. If smaller revenue teams could be permitted to have exclusive rights to players they received in a reverse order draft even upon the expiration of their contract, it could eliminate the migration of talent to large revenue teams. However this gave owners tremendous bargaining power in salary and contract negotiations. If players wanted to play, then they were required to sign a contract and play for the team that drafted them or have their contract rights sold to another team. Rottenberg (1956) found that despite the presence of the reserve rule there was an unequal distribution of talent.

He finds that the reserve rule "cannot be expected to equalize the distribution of players among teams more than a market in which there is perfect freedom".52 Rottenberg

(1956) states that teams in larger markets will be able to buy more talent regardless of the reserve rule, by offering higher prices for player contracts from other teams.

The Coase Theorem captures Rottenberg's (1956) conclusion about the rights to player talent. It states that the distribution of playing talent (and competitive balance) will be independent of initial rights to players, because in the assumed absence of transaction costs and freedom of information, players will end up on the team that values their services most. 53 Essentially any restrictions on the labor market will have no effect on the distribution of playing talent, and thus only have the effect of transferring rents between players and owners. 54 After the removal of the reserve clause in the MLB and

52 Rottenberg, The Baseball Players' Labor Market, 248.

53 Aju J. Felll1 et aI., "The Influence of Structural Changes and International Players on Competitive Balance in the NHL," Atlantic Economic Journal 33, no. 2 (06 2005): 215-224.

54 Vrooman, A General Theory ofProfessional Sports Leagues, 971-990. 23 similar changes in other professional leagues, there have been several studies investigating the effect of these changes on competitive balance. Support for the Coase

Theorem means that changes in league structure and/or property (labor) rights regulations would not affect competitive balance.

Fort and Quirk (1992) find further support for Rottenberg's (1956) conclusions and the Coase Theorem by examining the effect of implementation of free-agency and the reserve clause on competitive balance. They acknowledge that the presence of the reserve clause does not change the market equilibrium outcome (of competitive imbalance) because it does not affect the profit incentives or marginal revenue curves of teams. 55 Large market teams will have higher marginal revenue curves and at the profit maximizing condition where marginal revenue equals marginal cost they will demand more talent than teams in smaller markets. 56 Using various measures of competitive balance before and after the implementation of free agency in the National League,

American League and NBA, Fort and Quirk (1992) found that there is no significant difference in any of the measures of competitive balance before or after free agency. 57

Fishman (2003) also investigated the application of the Coase Theorem after the abolition of the reserve clause. In contrast with previous studies, Fishman (2003) found that the higher the number of free agents the higher the dispersion in team winning

58 percentages, indicating a lower level of competitive balance . He surmises that the

55 Quirk and Fort, Pay Dirt: The Business ofProfessional Team Sports, 538.

56 Ibid.

57 Ibid.

58 Peter Fishman, "Competitive Balance and Free Agency in Major League Baseball," American Economist 47, no. 2 (Fall 2003): 86-91. 24 reason the Coase Theorem did not hold was due to transaction costs or economic distortions. 59

In contrast with previous studies Surdham (2006) looks at the changes in player movements that have resulted from changes in labor market restrictions such as free- agency and the amateur draft. The Coase Theorem implies that changes in property rights restrictions will have no effect on individual team's long run win percentage as well as overall dispersion of win percentages of the league. Over time, he finds a decrease in the dispersion of talent between the top and bottom quartiles of teams indicating a possible increase in competitive balance.6o He also finds that the team producing the most talent also changed over time, which supports a possible increase in competitive balance. 61

Surdham (2006) further concludes that changes in property rights did not significantly affect the net distribution of player talent, which supports the Coase Theorem.62 Free- agency increased only the pace of player movement (not the distribution of talent) as players sought to increase the rent received for their abilities. 63

In a ground breaking study Depken (1999) uses a new measure of competitive balance to study the effect of free-agency and the Coase Theorem on Major League

Baseball. In his model Depken (1999) uses MLB data from 1920-1996 and attempts to

59 Ibid.

60 David G. Surdham, "The Coase Theorem and Player Movement in Major League Baseball," Journal of Sports Economics 7, no. 2 (May 2006): 201.

61 Ibid.

62 Ibid.

63 Ibid. 25 relate them to player-talent distribution and structural changes in MLB.64 He finds that

Free- Agency had a negative effect on competitive balance in the American League and no effect on competitive balance in the National League.65 He also concludes that the greater the concentration of offensive talent (runs scored) the greater the competitive imbalance in the league.66 Depken (1999) contends that free-agency alone is not a detriment to competitive balance as advocates of revenue sharing and salary caps argue due to it's upward pressure on salaries.67 He acknowledges the need for further work on the effects of revenue sharing and salary caps on competitive balance.

After Depken's (1999) conclusion that concentration of talent reduces competitive balance, Schmidt and Berri (2005) investigated the relationship between the distribution of playing talent and the overall population of baseball talent. This investigation was spurred by the Gould (1996) hypothesis that athletic ability is normally distributed.68

However, athletic ability has a biomechanicallimit and those players in the far right tail of the normal distribution will exhibit relatively equal athletic ability.69 Schmidt and

Berri's (2005) study uses the assumption that as MLB has expanded its search for players

(geographically), this geographic diversity should be seen in the player talent pool.

Schmidt and Berri (2005) find that the compression of talent across time responds to

64 Craig Depken, "Free-Agency and the Competitiveness of Major League Baseball," Review ofIndustrial Organization 14, no. 3 (1999): 205.

65 Ibid.

66 Ibid.

67 Ibid, 216.

68 S. J. Gould, Full House: The Spread ofExcellence from Plato to Darwin (New York: Three Rivers, 1996)

69 Ibid. 26

changes in geographic diversity.70 That is to say as the population of players in MLB

becomes more diverse, the compression of talent increases. As the compression of talent

increases the probability that a 'weaker team' beats a stronger team increases, indicating 7l an increase in competitive balance. While not joining the debate on whether free-

agency affects competitive balance, this study explains the trend of an increase in

competitive balance over time that many studies acknowledge. It also assumes that the

dispersion in the concentration of playing talent, that is caused by differences in revenue

potential to be a major determinant of competitive balance.

In another study on the Coase Theorem, Fenn et al (2005) looked at competitive

balance in the NHL, with respect to structural changes and international players. The

study looks at the introduction of limited Free-Agency, the entrance of substantial

numbers of European born players, competition with rival leagues, institution of the

amateur draft, and expansion years as qualitative shift variables reflecting changes in the

NHL.72 Fenn et al use two models with different measures of competitive balance, one that measured relative strength of teams and another to measure the turnover in standings

over time, to investigate the impact of changes in the NHL. They find that increases in distribution of talent (or a decrease in concentration of talent) especially on defense effects competitive balance. Fenn et al (2005) also find support for the Coase Theorem in that neither free agency nor the amateur draft is significant in changing competitive

70 Martin B. Schmidt and David J. Berri, "Concentration of Playing Talent: Evolution in Major League Baseball," Journal o/Sports Economics 6, no. 4 (112005): 418.

71 Ibid, 413.

72 Fenn et al., The Influence o/Structural Changes and International Players on Competitive Balance in the NHL, 215-224. 27

balance. 73 However, they do find that the arrival of European players appears to have

increased competitive balance, which contradicts the assumed perfect inelasticity of

supply in Coase Theorem literature. 74 This conclusion supports Schmidt and Berri's

(2005) finding that the compression of talent increases with greater geographic diversity

of talent. Interestingly to this current investigation of the collective bargaining

agreements in NHL, Fenn et al (2005) also concluded that changes in the previous

collective bargaining agreements actually reduced competitive balance. 75

In conclusion, most of these studies found support for the Coase Theorem in

relation to business measures that have attempted to increase competitive balance such as

free-agency, the reserve clause and the amateur draft. Fishman (2003) was the only

author to find evidence counter to the Coase theorem with respect to free-agency or the

amateur draft. Schmidt and Berri (2005) and Fenn et al (2005) found that an increase in

foreign born players (or the overall talent pool) resulted in a compression of talent that

increased competitive balance. Fenn et al (2005) and Depken (1999) among others

provided quantitative evidence for the notion that the distribution of talent and

competitive balance are positively related. This is an important conclusion as a salary

attempts to redistribute talent, and reduce the concentrations in talent that are detrimental to competitive balance. In summary, these studies found that business approaches that attempt to alter competitive balance through labor market restrictions are not effective.

However these studies do provide insight as to some variables that do affect competitive balance including an increase in the talent pool, and the distribution of talent. It may be

73 Ibid.

74 Ibid,222.

75 Ibid. 28

inferred that business measures that can influence these variables will affect competitive

balance.

Subsidization Measures

After liberalization of labor market restrictions in many professional sports

leagues, several other business approaches have been implemented to promote the

competitive balance. Professional leagues have implemented various cross-subsidization

measures in order to reduce the effect of differences in revenue potential that has been

established as a primary source of competitive imbalance. These measures are intended to

improve competitive balance between large and small market teams by supporting

comparable expenditures on talent. This group of studies looks at the effect of these

measures including revenue-sharing agreements, franchise relocations, salary caps,

luxury taxes and league expansion on club incentives and competitive balance. These

investigations are important to professional sports leagues today as most of the leagues

employ several of these measures, however the NHL is the only one of the 4 major

professional sports (MLB, NBA, NFL) to not employ a revenue sharing agreement.

Szymanski and Kesenne (2004) theoretically explore the changes that occur in a

league as a result of a revenue sharing agreement and find that revenue sharing

agreements may decrease competitive balance. 76 Szymanski and Kesenne (2004)

acknowledge the conclusion from the General Theory of Sports Leagues that teams with stronger drawing markets will dominate leagues in equilibrium because each additional

76 Stefan Kesenne, "Improving the Competitive Balance and the Salary Distribution in Professional Team Sports," in Transatlantic Sport: The Comparative Economics o/North American and European Sports Cheltenham, u.K. and Northampton, Mass.:; Elgar; distributed by American International Distribution Corporation, Williston, Vt, 2002), 95-108. 29

win is worth more in marginal revenue than wins for its rivals. 77 Szymanski and Kesenne

(2004) use a two club model, with weak drawing and strong drawing profit maximizing

teams and a constant talent supply to show the theoretical effect of a gate revenue sharing

agreement. This study finds that gate revenue sharing does in fact reduce competitive

balance by reducing incentives to win. 78 The dulling of incentives to win is greater for the

weak drawing team, and gate revenue drops for both teams as the contest becomes more

unbalanced. 79 This difference in findings is attributed to the assumption that teams are

non-cooperative in their profit maximization.

The findings of Kesenne (2005) provide further support for the conclusion that

revenue sharing may decrease competitive balance. Kesenne (2005) expands Szymanski

and Kesenne's (2004) model by using an n-club model (with unlimited amount of clubs)

with flexible talent supply to study how revenue sharing effects the distribution of

playing talent. 80 Kesenne (2005) uses a variable talent supply because other studies have

found talent supply to effect competitive balance (and the distribution of playing talent), independent of revenue sharing. He also uses a pooled revenue sharing agreement to

8 theoretically show the shift in teams' demand curves for talent. ! A pool-sharing agreement consists of a contribution of a fixed percentage of a club's total season

77 Rottenberg, The Baseball Players' Labor Market, 242. Quirk and Fort, Pay Dirt: The Business of Professional Team Sports, 538. Scully, The Business ofMajor League Baseball

78 Stefan Szymanski and Stefan Kesenne, "Competitive Balance and Gate Revenue Sharing in Team Sports," Journal ofIndustrial Economics 52, no. 1 (032004): 165-177.

79 Ibid.

80 Stefan Kesenne, "Revenue Sharing and Competitive Balance: Does the Invariance Proposition Hold?" Journal ofSports Economics 6, no. 1 (022005): 98-106.

81 Ibid. 30

revenue, that is redistributed equally among all teams. Kesenne (2005) finds that revenue

sharing causes all clubs to reduce their demand for playing talent because they have to

share the marginal revenue received for each unit of playing talent. 82 While

acknowledging that the effect of revenue sharing depends on the objectives of clubs and

their marginal revenue curves, Kesenne finds that a pool-sharing agreement worsens the

competitive balance in a league. 83

In a comprehensive analysis on subsidization measures, Fort and Quirk (1995)

review the basic economics and incentive effects of various measures adopted by sports

leagues. Fort and Quirk (1995) acknowledge that a major problem in sports leagues is

maintaining financial viability for teams located in weak drawing markets, and many

business measures have attempted to promote competitive balance while protecting the

financial viability of all teams.84 The business measures they review include: the reserve

option clause, salary caps, the rookie draft, competition with rival leagues, expansion,

movement of franchises, gate and local TV revenue sharing and national TV revenue

sharing. 85 Underlying their analysis are the assumptions of profit maximizing behavior

by teams, income maximizing behavior by players and market equilibrium outcomes.

They find that if teams have revenues that aren't shared, limited revenue sharing worsens

competitive balance. 86 Fort and Quirk (1995) also find that with the exception of a salary

82 Ibid.

83 Stefan Kesenne, "Revenue Sharing and Competitive Balance: Does the Invariance Proposition Hold?" Journal o/Sports Economics 6, no. 1 (022005): 98-106.

84 Rodney Fort and James Quirk, "Cross-Subsidization, Incentives, and Outcomes in Professional Team Sports Leagues," Journal o/Economic Literature 33, no. 3 (09 1995): 1265-1299.

85 Ibid, 1266.

86 Ibid. 31

cap, the business approaches in use provide no profit incentives for improving

competitive balance.87 While the salary cap promotes competitive balance and may

provide financial stability, they acknowledge important enforcement problems with the

salary cap.88 These problems stem from the fact that teams are led to choices that fail to

maximize revenues and players receive incomes less than a market equilibrium value, so

there are multiple incentives to cheat. 89 Fort and Quirk (1995) provide the theoretical

basis for further empirical analysis of salary caps.

Salary Caps

A salary cap is intended to restrict the amount of talent that may be acquired by

large markets (with higher revenue potentials). Given Depken (1999) and Fenn et aI's

(2005) conclusion that high concentrations of talent are detrimental to competitive

balance, the salary cap may have the potential to work if it can alter the distribution of

talent. While many articles have advocated for the salary cap theoretically, there has

been very little empirical research done on the effect of the salary cap on competitive

balance. This section presents the theoretical work on the effect of salary caps on

competitive balance and one study that simultaneously looks as the effect of the salary

cap and free-agency in the NFL.

Fort and Quirk (1995) surmise that if all teams end up spending an equal amount on salaries, then the league would end up with all teams having roughly the same playing

87 Ibid.

88 Ibid.

89 Ibid. 32

talent.90 According to Fort and Quirk (1992, 1995) this may be the only way to achieve

competitive balance between large and small market clubs.91 The salary cap will inhibit

talent from flowing to larger markets, similar to a reserve clause, without the

monopsonistic exploitation of players that the reserve clause creates.92 Vrooman (1995)

disagrees. He acknowledges that under a salary cap the marginal costs of winning for

individual teams will equal zero, and profits will be maximized with league revenues, yet

he maintains that distribution of talent may not be affected by a salary cap.93 He argues

that by eliminating marginal costs of talent, teams will behave collusively to maximize

revenues. 94 Thus all teams have an interest in wins that cause the greatest increase in

league revenues. This will occur where the demand is highest such as in large markets or

with previously talented teams that have built strong devoted followings. 95 However,

Vrooman (1995) assumes that a salary cap is associated with a revenue sharing

agreement as is seen in the NBA. The NHL salary cap however, was implemented

without a revenue sharing agreement. These studies have no quantitative backing and are

at odds as to the true effect of a salary cap.

Kesenne (2000) investigates the salary cap 'tout court', meaning a hard salary cap

where teams cannot overspend in any circumstance, and in the absence of additional

revenue sharing or subsidization agreements. The salary cap conditions he investigates

90 Ibid.

91 Quirk and Fort, Pay Dirt,' The Business ofProfessional Team Sports, 287.

92 Ibid.

93 Vrooman, A General Theory ofProfessional Sports Leagues, 971-990.

94 Ibid.

95 Ibid. 33

are identical to the salary cap implemented in the NHL. Kesenne (2000) uses a Fort and

Quirk two-club model adjusted for two types of players: top players and regular players

and assumes that clubs will be profit maximizers.96 Kesenne (2000) finds that a salary

cap reduces the large market club's demand for top players while leaving the small

market clubs demand unchanged. 97 He also states that the salary difference between top

players and regular players will be smaller, creating a more equal distribution.98 Recall

that Fenn et al (2005) and Depken (1999) found that concentrations in talent were

negatively related to competitive balance. 99 Their conclusions supports Kesenne's (2000)

findings.

In regards to the financial viability of a salary cap Kesenne (2000) argues that

profits for big and small market clubs can go up under a salary cap because of the

depressing effect it has on salaries. 100 However total league revenues will decrease. He

argues that a salary cap may still be necessary to correct a negative externality or market

failure in sports leagues, where at market equilibrium big market clubs demand too many

top players. 101 This over-consumption will eventually cause the decline of the league, as

interest (and revenue) decline with the lack of competitive balance. 102 Kesenne (2000)

96 Stefan Kesenne, "The Impact of Salary Caps in Professional Team Sports," Scottish Journal ofPolitical Economy 47, no. 4 (09 2000): 422-430.

97 Ibid.

98 Ibid,426.

99 Fenn et ai., The Influence ofStructural Changes and International Players on Competitive Balance in the NHL, 215-224. Depken, Free-Agency and the Competitiveness ofMajor League Baseball, 205.

100 Kesenne, The Impact ofSalary Caps in Professional Team Sports, 422-430.

101 Ibid,428.

102 Ibid. 34 concludes that a salary cap is necessary to reach a "pareto optimum" state in sports leagues.

Fenn et al (2006) provide one of the only empirical studies on salary caps. They investigate the simultaneous institution of free agency and a salary cap in the NFL.

However the salary cap in the NFL is not a 'tout court' salary cap, meaning teams can exceed the cap but pay penalties to a revenue sharing agreement. In their investigation they control for strikes, expansion, changes in schedule length, changes in the number of playoff spots, franchise relocations and new stadiums. \03 They also account for the concentration of player talent. Fenn et al (2006) found that the number of free agents that changed teams was significant in explaining the variations in the concentration of player talent. 104 Fenn et al (2006) conclude that there is some evidence that free agency and a salary cap have increased competitive balance in the NFL, and similar to Depken (1999) the concentration of playing talent strongly influences competitive balance. lOS

The theoretical and empirical work on salary caps alone is very limited. Vrooman

(1995) contends that a salary cap will not change competitive balance, while Fort and

Quirk (1992 and 1995) argue that a salary cap will improve competitive balance. Kesenne

(2000) extends this by arguing for the financial viability and necessity of a salary cap to correct market failure. 106 Fenn et al (2006) study the simultaneous influence of a salary

!O3 Aju J. Fenn, Andrew Larsen, and Erin Leanne Spenner, "The Impact of Free Agency and the Salary Cap on Competitive Balance in the National Football League," Journal ofSports Economics 7, no. 4 (November 2006): 374.

104 Ibid.

105 Ibid.Depken, Free-Agency and the Competitiveness ofMajor League Baseball, 205.

106 Kesenne, The Impact ofSalary Caps in Professional Team Sports, 422-430. 35

cap and free agency and find some support for them both. 107 However none of these

studies provide an empirical analysis of a salary cap 'tout court' as is seen in the NHL.

Pay and Performance

Studies on pay and performance support and expand the General Theory of Sports

Leagues and provide support for the assumptions made in salary cap theory. Salary Cap

theory assumes that there is a causal link between team payroll and performance. It

assumes that differences in teams' payrolls (caused by differing revenue potentials) result

in unequal talent distribution, which has been established as the primary source of

competitive imbalance. A salary cap will theoretically limit a large revenue team's ability

to purchase top talent and it will theoretically change competitive balance in a league. 108

This link between team payroll and competitive balance plays a central role in the theory

ofteam sports, but has been seldom investigated empirically. 109 It is important to explore this assumption of causality, for if it is not true or complete in nature, the implementation of a salary cap will not translate into competitive balance.

In an article that builds upon the conclusions of Scully (1989), Hall et al

(2002) investigate the presence of a causal link between team payroll and performance in both Major League Baseball and English Premier League soccer. 110 The authors used regular season winning percentages as a measure of performance, and payroll data

107 Fenn, Larsen, and Spenner, The Impact ofFree Agency and the Salary Cap on Competitive Balance in the National Football League, 374.

108 Kesenne, The Impact ofSalary Caps in Professional Team Sports, 422-430.

109 Leo H. Kahane, "Team and Player Effects on NHL Player Salaries: A Hierarchical Linear Model Approach," Applied Economics Letter 8, no. 9 (September 2001): 629.

110 Ibid. 36

between 1980-2000. They found that increased player spending causes improved

performance in the English Premier League. III While the MLB displayed no statistical

evidence of a causal relationship between 1980-1994, a causal link: was present after 1995

in both directions. I 12 This means that payroll was responsible for increased performance,

and in tum increased performance was responsible for increases in payroll. I 13 This double

causality suggests that imbalance in a league may be self-perpetuating. I 14

According to the Hall et al (2002), the payroll-performance link: may not be

visible statistically in all leagues, because the causal mechanism between payroll and

performance works imperfectly. I IS They identify several factors that can distort this

mechanism including player complementarities, managerial talent, injuries, luck,

inefficient salary determination and restrictive labor market. I 16 The authors suggest that

the more liberal labor market of English Soccer compared to MLB allows for a closer

relationship between salaries and the marginal revenue productivity of players (MRPs ).117

This stronger relationship in tum allows a stronger causal relationship between payroll

and performance. Furthermore they contend that in light of their findings competitive

III Ibid.

112 Ibid.

113 Ibid.

114 Ibid.

115 Ibid.

116 Ibid.

117 Ibid. 37 balance may be derived from convergence of revenue curves, and thus incentives to pursue talent. I 18

Burger and Walters (2003) extend Scully's (1989) framework by allowing market size and team performance to interact, in an effort to determine the marginal value of an additional win and differences in players marginal revenue products (MRPs). The authors suppose that revenue will be sensitive to the total number of fans and the intensity with which they follow the team. The model, therefore accounts for two types of fans - purists who follow the home franchise regardless of performance and bandwagoners who follow the home franchise only when it is in contention (for a playoff spot or other reward). I 19

The authors' findings support the assumption that market size "crucially affects the way teams value players", and therefore the distribution of playing talent. 120 The marginal revenue derived from additional wins is up to six times higher in large markets, meaning that baseball teams in large markets may value a given player up to six times more than those in smaller markets. 121 According to the authors larger market teams "are not just able to spend more for talent because they are blessed with bigger budgets, but are more willing to do so because extra wins have far greater value in such markets.,,122

Furthermore, there is no such thing as a fair market value for any player, because the marginal revenue productivity (price ceiling for a player's salary) is different in each

118 Ibid.

119 J. D. Burger and S. J. K. Walters, "Market Size, Pay, and Performance. A General Model and Application to Major League Baseball," Journal ofSports Economics 4, no. 2 (052003): 108-125.

120 Ibid, 1 18.

121 Ibid.

122 Ibid, 1 18. 38

market. 123 From their conclusions Burger and Walters argue that the key to the success to

any strategy to promote competitive balance will be the convergence of marginal win

values (between large and small markets). 124

Scully (1989) and Burger and Walters (2003), found that teams in large markets

value wins and talent differently. In conjunction with the causality between payroll and

performance theorized by Hall et al (2002), one could conclude that large market teams

may always dominate; indicating that the market failure that Kesenne (2000) and others have argued must be corrected. Kesenne (2002) believed the solution was the salary cap;

however these studies argue that the only way to achieve competitive balance will be the convergence of revenue curves and/or marginal values of additional wins. It is unclear

from these studies whether the institution of a salary cap will alter the competitive balance in a league. These studies provide support for the assumptions in salary cap theory established by Kesenne (2000) insofar as they establish that greater payrolls can cause greater team success. This is a critical conclusion as the problem of competitive imbalance stems from differences in the payrolls, and incentives to acquire talent.

However none of these studies are focused on the NHL. To find studies that support the assumption that greater payrolls produce greater team success, one must examine the determinants of individual salaries.

123 Ibid.

124 Ibid. 39

Salary Determination

In order for payroll to translate to perfonnance as studies in the Pay and

Perfonnance section advocate, (and a salary cap to be effective), the assumption is made that player productivity correlates to their salary. Depken (1999) and Fenn et al (2005) conclude that high concentrations oftalent are detrimental to competitive balance, the salary cap may have the potential to work if it can alter the distribution of talent. This section will investigate if salaries are directly related to talent and thus provides insight on the potential of the salary cap to alter competitive balance. This group of studies on

Salary Detennination provides support for the assumptions made by salary cap advocates such as Kesenne (2000) that pay translates into perfonnance because salaries primarily reflect differences in levels of talent and not other external factors. This group of studies focus solely on salary conditions in the NHL to provide a basis for the hypothesis of this investigation that the salary cap has improved competitive balance in the NHL.

Scully (1989) originated the idea that team revenues are directly related to team win percentages, and that each player's productivity contributes a certain amount to the win percentages and thus contributes to directly to the team's revenue. 125 These studies will look at the marginal revenue productivity of athletes, that is their worth in relation to franchise productivity, and the detenninants of salary in the NHL. Various detenninants of salary will include team specific effects on salaries and interactive effects on salary. It is important to examine articles directly related to the NHL, as different leagues value athletic skills, and measures of perfonnance very differently. This section also addresses the effect that monopoly and monopsony conditions may have on salaries which is

125 Scully, The Business ofMajor League Baseball, 242. 40 particularly important since the argument against the salary cap is that it is simply a way for teams to cut costs and exploit athletes.

In their 1988 study, Jones and Walsh attempt to quantify the extent to which differences in skills and monopoly-monopsony conditions determined salaries in the

NHL. Using data from the 1977-78 season, this study separates salary determination by position. The authors find that franchise characteristics are unimportant or offsetting for defense and goaltender salaries. 126 For forwards, salaries are positively related to franchise revenue. 127 In terms of skill variables, experience, star status (measured by trophy nominations), and star potential (measured by first round draft picks), are

l28 significant in determining both defense and forwards' salaries . Points per game appear to be the primary determinant in forwards salaries. 129 Other physical attributes, including weight and height, or intensity variables such as penalty minutes, appear to be valued as well. 130 While salary levels have increased exponentially since 1978, and restrictions on the movements of players have changed, the Jones and Walsh (1988) conclusion that player skills are the primary determinant of salaries is an integral part to salary cap theory. 131 If this is true and skills are the primary determinant of salaries then the salary cap has the foundation to significantly alter competitive balance in the NHL.

126 Jones, J. C. H. and William D. Walsh, "Salary Determination in the National Hockey League: The Effects of Skills, Franchise Characteristics, and Discrimination," Industrial and Labor Relations Review 41, no. 4 (July 1988): 592.

127 Ibid.

128 Ibid.

129 Ibid.

130 Ibid.

131 Ibid. 41

Kahane's (2001) findings add another element to Jones and Walsh's (1988)

conclusion and show that franchise characteristics (monopoly conditions) may be another

important determinant to all salaries. He uses a hierarchical linear model to show that

there are significant differences in mean salaries and rewards to performance across

teams. 132 His model shows that these differences can be partially explained by team

revenues. 133 This means that players who increase their performance benefit from a

greater salary increase if they are on a team with larger revenues. 134 This is a logical

following of the article by Burger and Walters (2003) that concluded that larger market

teams value wins and talent more than smaller market teams. Kahane's (2001) ultimate

conclusion is that there are both player and team specific components of salary

determination. J35

Idson and Kahane (2000) add yet another aspect to the determinants of salaries by

investigating the effects of coworker productivity on individual salaries in the National

Hockey League. They hypothesize that there may be complementarities between skill

inputs, such that "individual productivity may vary to the extent that coworkers

[teammates] offer different degrees of assistance". 136 The goal of the study is to evaluate

whether coworker attributes: 1) affect individual salaries, 2) affect how individual skills

132 Kahane, Team and Player Effects on NHL Player Salaries: A Hierarchical Linear Model Approach, 629.

133 Ibid.

134 Ibid.

135 Ibid.

136 Todd L. Idson and Leo H. Kahane, "Team Effects on Compensation: An Application to Salary Detennination in the National Hockey League," Economic Inquiry 38, no. 2 (April 2000): 345. 42

or attributes are valued by the employer, 3) are complementary inputs to the attributes of

13 other individuals. ? Idson and Kahane (2000) also find that franchise revenues and

coaching quality are both positively related to salary.138 They also find that an

individual's productivity will be higher when he plays on team with higher-quality

teammates - meaning player inputs are complementary. 139 Thus, they posit that team

performance measures will have a positive effect on player salaries. 140 In terms of

specific variables they find that points scored and plus minus rating are complementary

inputs (and have a positive effect on salary), while such team variables as penalties and

height have a negative effect on salary. 141 Idson and Kahane (2000) argue that penalties

and height have diminishing returns such that the management will value these less, if

teammates also hold these attributes. 142. This study suggests that the addition of one or two top players could significantly increase the production of an entire team, which

supports the hypothesis that a salary cap could increase competitive balance in the NHL.

It also suggests the importance of managerial or coaching skill in finding player talent complementarities.

Richardson (2000) relates salaries to performance in his article entitled "Pay,

Performance and Competitive Balance in the National Hockey League". 143 He compares

137 Ibid.

138 Ibid, 353.

139 Ibid.

140 Ibid.

141 Ibid.

142 Ibid,354.

143 David H. Richardson, "Pay, Perfonnance and Competitive Balance in the National Hockey League," Eastern Economic Journal 26, no. 4 (Fall 2000): 393. 43 marginal revenue products of a player with the actual salary paid to that player, using marginal revenue products estimation from Scully (1989). A player's marginal revenue product (MRP) is estimated as the product of the marginal revenue and marginal product that the player earns the team. 144 These values are derived from the team revenue and production functions. This is thought to be the best measure of the economic value ofthe player to the team, and thus should be directly associated with the player's salary, assuming the club exhibits profit maximizing behavior. 145 However it is important to realize that salaries are set well before the season and are simply an estimate of the players worth to the team. Richardson (2000) finds that there is a general correlation between MRP and the amount of salary a player earns above the average of possible replacements not on a roster (called reserve players ).146 He also finds an average surplus pay of only 1.6% over marginal revenue products, indicating that players are in fact paid very close to their 'worth' .147 This is significant because it means that the current player reservation system does not result in monopsonistic exploitation, as some players argue. 148

These studies all seem to conclude that in general in the NHL, salaries are determined primarily by an individual's skills. This is the logical following from imbalance based in differences in payrolls that a salary cap strives to eliminate. Idson and

Kahane (2000) add valuable insight that while the primary determinant of salary is player

144 Ibid.

145 Ibid.

146 Ibid, 400.

147 Ibid.

148 lbid,413. 44 skill, player production (related to MRP), may be complementary and thus team members could influence one another's salaries. This suggests that the addition of one or two players could significantly increase the production and revenue of an entire team. It also suggests the importance of managerial skill in finding player talent complementarities. It is also established that salaries are subject to team or club specific variables. In general Richardson (2000) and others find that players seem to be worth (in terms of marginal revenue product) the amounts they are paid by each team. Richardson

(2000) also acknowledges that unless a salary cap will significantly change competitive balance, the argument that it is simply a cost cutting tactic may be validated, as it will act

149 as a significant depressant on salaries • However these studies provide a valuable foundation for the prospect of success for the salary cap in the NHL. The salary cap will limit the amount of money spent on salary by large market teams. Since salary is primarily determined by skill, it will limit the amount of talent on large market teams and may translate into the redistribution of not only salary spending but talent as well.

149 Ibid. 45

Summary

Simon Rottenberg (1956) stated that no team can be successful unless its

competitors also survive. 150 The profit incentives associated with winning can drive

teams to pursue outcomes that are not consistent with competitive balance, and unhealthy

to the league as a whole. As John Vrooman (1995) states, "as long as market size is a

significant factor the league will not reach parity" due to differences in the revenue

elasticity of winning. 151 Since wins are more valuable to large market teams, they will

acquire talent to the detriment of balance in the league. 152

One group of articles explored the Coase Theorem testing whether overall

distribution oftalent and competitive balance will be unaffected by changes in

restrictions on property rights of players. However, Depken (1999) and Fenn et al (2005)

critically conclude that the concentration of talent coincides with decreases in

competitive balance, as well as some evidence that free agency translated into greater

imbalance. 153 This conclusion is applicable to this study because the salary cap strives to

redistribute talent and reduce talent concentration on large market teams. Depken (1999)

and Fenn et aI's (2005) conclusions provide a preliminary foundation for a hypothesis

supporting the success of the salary cap, if the salary cap can redistribute talent. Studies

by Fenn et al (2005) and Schmidt and Berri (2005) explain improvements in competitive

150 Rottenberg, The Baseball Players' Labor Market, 242.

151 Vrooman, A General Theory ofProfessional Sports Leagues, 971-990.

152 Vrooman, A General Theory ofProfessional Sports Leagues, 971-990.; Quirk and Fort, Pay Dirt,' The Business ofProfessional Team Sports, 538.; Rottenberg, The Baseball Players' Labor Market, 242.; Kesenne, The Impact ofSalary Caps in Professional Team Sports, 422-430.

153 Depken, Free-Agency and the Competitiveness ofMajor League Baseball, 205. Fenn et al., The Influence ofStructural Changes and International Players on Competitive Balance in the NHL, 215-224. 46 balance over time by demonstrating that an elastic talent supply (the introduction of foreign born players) causes a convergence of talent levels and increased competitive balance. 154

Several articles are dedicated to investigating measures implemented in professional leagues to correct this market failure including revenue sharing, free-agency, reserve clauses and salary caps. Fort and Quirk (1995) suggest that a salary cap will be the only measure that can change competitive balance in a league, although league wide revenues are not maximized under a cap. ISS Vrooman (1995) says that the only way competitive balance can coincide with maximized revenues is if revenue functions for teams are similar. 156 Vrooman (1995) acknowledges this is impossible given that it is not plausible to change differences in market size while maintaining perceptions of true competItIOn... 157

It is essential to understand that competitive balance is solely important as it relates to fans, because fan interest is critical to the ultimate success ofleagues as a whole. Competitive balance is only important as it relates to the uncertainty of outcome in a single sporting contest or the outcome of the league that will maximize league revenue an d d nve. atten d ance. 158

154 . Felll1 et al., The Influence ofStructural Changes and International Players on Competitive Balance in the NHL, 215-224.; Schmidt and Berri, Concentration ofPlaying Talent: Evolution in Major League Baseball,412-419.

155 Fort and Quirk, Cross-Subsidization, Incentives, and Outcomes in Professional Team Sports Leagues, 1265-1299.

156 Vrooman, A General Theory ofProfessional Sports Leagues, 971-990.

157 Ibid.

158 Humphreys, Alternative Measures of Competitive Balance in Sports Leagues, 133. 47

There was limited research presented on the theory of how a salary cap will affect competitive balance in a league. Kesenne (2000) showed that salary caps will theoretically limit the amount of talent acquired by large market teams and salaries will be depressed allowing small market teams to acquire greater talent without spending beyond their budget. 159 However while Kesenne (2000) and Fort and Quirk (1995) predict the salary cap 'tout court' - as in the NHL to be successful there is very little empirical research on salary caps. 160 It was therefore necessary to explore the basic assumptions of salary cap theory, in order to provide the foundation for a hypothesis about the success of the salary cap in the NHL.

While Depken (1999) and Fenn et aI's (2005) conclusions provide the preliminary foundation for a hypothesis supporting the success of the salary cap, other assumptions of the salary cap were explored. Salary cap theory assumes that there is causality between team salaries and team perfonnance. This causality was shown in the pay and perfonnance section. In order for a relationship between pay and perfonnance to hold it follows that salaries are an accurate measure of talent or player production, as was shown in the salary detennination section. However articles in the pay and perfonnance section did not establish this causality in the NHL, so an additional section on salary detennination in the NHL was included. This section established that skills are a primary detenninant of salaries in the NHL

The research on the assumptions of salary cap theory has been presented here, as well as other business measures that have attempted to change competitive balance. What

159 Kesenne, The Impact ofSalary Caps in Professional Team Sports, 422-430.

160 Kesenne, The Impact ofSalary Caps in Professional Team Sports, 422-430.; Fort and Quirk, Cross­ Subsidization, Incentives, and Outcomes in Professional Team Sports Leagues, 1265-1299. 48 is not clear is how a salary cap 'tout court' will or should change the NHL? Will it change competitive balance? Or will it simply depress salaries? The implications of this question could change many ofthe assumptions made in previous literature and salary cap theory. This investigation will provide early insight to the discussion on whether a salary cap does indeed contribute to the competitive balance of the league or simply cut costs in the interests of franchise profits. CHAPTER III

THEORY

Research in the field of competitive balance is relatively abundant. In this

research there are a variety of different methods to measure competitive balance. This

chapter will begin by reviewing some of the most commonly used measures, and

focus on measures most pertinent to this investigation. The chapter will then explore

some statistical justifications for this study, and the use of a team specific dependent

variable to capture changes in ranks that are applicable to competitive balance.

Finally, the chapter will discuss the factors affecting competitive balance as will be

measured in this study.

Measures of Competitive Balance

This section will present a number of possible measures of competitive

balance that have been widely used in previous studies. It will first discuss the

Dispersion of Win Percentages as an introduction to basic measures of competitive

balance because this measure provides a valuable starting point before delving into more complicated measures. The second measure that will be presented is the Within

Season Standard Deviation which is a useful measure of the relative quality of all teams in a season. However Within Season Standard Deviation fails to account for

49 50

the season to season turnover in rankings or success that is ultimately important to fan

interest. The third measure that will be discussed is a measure of concentration, the

Herfindahl-Hirschmann Index, which measures the distribution of a variable between

all teams for a period of time. Finally, Humphreys' (2002) Competitive Balance

Ratio, and the idea of Team Standard Deviation will be presented. Here the ultimate

importance of measures as they relate to fan interest will be discussed.

Dispersion of Win Percentages

One common measure of competitive balance is the dispersion of win

percentage among teams. This measure is used widely for baseball and basketball,

sports in which ties do not occur, but can be adjusted to dispersion of total points for

other sports. Dispersion can be calculated by taking the range of win percentages in a

single season. Simply, dispersion of win percentage finds the difference between the

highest ranked team's win percentage and the lowest ranked team's win percentage.

According to Fort and Quirk (1992) one drawback with this measure however, is that

it only takes into account the two extremes of the league: the highest and lowest

winning teams, and leaves out the middle pack of teams. 1 The distribution of the

teams in between the highest and lowest winning teams is an important element of

competitive balance.2 Whether the middle teams are relatively even, or spread evenly

between the extremes is an important determinant of fan interest and uncertainty of

outcome.

I James P. Quirk and Rodney D. Fort, Pay Dirt,' The Business ofProfessional Team Sports (Princeton, N.J.: Princeton University Press, 1992),538.

2 Ibid. 51

Within Season Standard Deviation

One of the most widely used measures of competitive balance is the Within

Season Standard Deviation of Win Percentages (SEASONSD). Standard deviations

can also be used for other measures of success including wins and points. Standard

deviation is a statistic that describes the average distance that observations lie from

the mean of the observations in the single data set. 3 Within Season Standard

Deviation measures the relative quality of teams over a single season, by capturing

the dispersion of winning percentages across all teams in a single season.4 The

formula for standard deviation of winning percentages in a single season is:

N L(WPC~,t -.500)2 i=1 aN,! = (3.1) N

where WPCT is the winning percent of the ith team in the league in year t, .500 is the

average winning percentage of all teams for the year, and N is the number of teams in

the league.5 Since there is always one winner and one loser the league wide average

winning percentage will always equal .500. The larger the standard deviation, the

greater the dispersion of winning percentages and the less balance there is in the

league. In terms of fan interest if the dispersion between team winning percentages becomes too high, then the uncertainty of outcome of games is compromised.6 This

3 Michael A. Leeds and Peter Von AHmen, The Economics ofSports (Boston: Pearson Addison Wesley, 2005), 159.

4 Ibid.

5 Ibid.

6 Ibid, 158. 52

measure can be useful because it captures the relative quality of all teams in the

league.

The Within Season Standard Deviation is also useful because it can be

compared to an ideal Within Season Standard Deviation. This ideal standard

deviation is derived from a situation where all teams have equal playing strengths. 7

Intuitively even if all teams had equal playing strengths, they would not all have the

same winning percentages, similar to flipping a coin 10 times, the result will not

always be 5 heads and 5 tails.8 The ideal standard deviation is calculated by:

.5 0"[ = IN (3.2)

where N is the number of games each team plays. It can be seen from the equation that the larger the number of games, the smaller the ideal standard deviation. 9 To study competitive balance in a single season, many studies take the ratio of the actual standard deviation to the ideal standard deviation as first seen in Scully (1989) and later in Quirk and Fort (1992).10 This ratio can then be compared across a number of years to describe changes in the relative quality of teams in the league.

One problem with using standard deviation of winning percentages in a single season is that it does not capture the changes in relative standings ofteams that are important to fans. While the Within-Season Standard Deviation may stay the same,

7 Ibid.

8 Gerald Scully, The Business a/Major League Baseball (Chicago: University of Chicago Press, 1989)

9 Leeds and Von A11men, The Economics a/Sports, 475.

10 Scully, The Business a/Major League Baseball 53

the first place team one year may drop to last place, or teams may simply stay in the

same relative standings year after year. Zimbalist argues that the problem with this

index of competitive balance is that fans do not experience it. II Fans want to be

uncertain about what place their team will finish in, if they will make the playoffs, or

even ifthey will win against a team that has consistently ranked number lover the

years. This uncertainty is what drives their interest, and it is not captured through the

comparison of Within Season Standard Deviation of Win Percentages with its ideal. 12

The rank and/or success of teams will no longer be uncertain if relative standings

remain the same year after year, and fan interest will dwindle, according to the

Uncertainty of Outcome Hypothesis. 13 The weakness of this measure is then that it

does not capture between season variations for teams, which for the purpose of this

study, is ultimately important.

Herfindahl-Hirschman Index

To account for this weakness several other studies have used concentration- measures, which describe the distribution of some variable across all teams. 14 One widely used measure of concentration or distribution is the Herfindahl-Hirschman

Index (HHI) which is the quadratic summation of all firm market shares in an industry shown by:

II Brian Milner, "NHL Attendance Problems: It's Too Early to Stop Worrying," The Globe and Mail, October 24 2006, 113

12 Brad R. Humphreys, "Alternative Measures of Competitive Balance in Sports Leagues," Journal of Sports Economics 3, no. 2 (2002): 133.

I3 Ibid.

14 Craig Depken, "Free-Agency and the Competitiveness of Major League Baseball," Review of Industrial Organization 14, no. 3 (1999): 205. 54

(3.3) where MSj is the market share of the ith firm on a scale of 0 to 1Y The market share can be calculated using various success variables such as championships, first place conference finishes, points, wins, etc. Theoretically ideal competitive balance is achieved when each team has an equal market share of wins, or HHI = lIN where N is the number of teams in the league. 16 Depken (1999) acknowledges that it requires the market shares of N-1 firms and has potentially prohibitive data requirements. 17

However in sports leagues this data is usually relatively easy to find. This measure is naturally bounded from below by lIN where there is perfect parity among the N teams, and from above by 1, when there is a pure monopoly. In sports teams this pure monopoly seems impossible, for the league could not profitably reach a point where one team continually wins a championship over many years. It is also impossible using points or wins, because there are more than two teams in the league, and thus it is impossible for one team to win every game played in the league. IS One problem with this measure is that the actual HHI is often skewed by the number of firms in the industry, meaning that with expansions in sports leagues, HHI may artificially decrease, when in fact competitive balance has not changed.

Depken (1999) uses deviations ofHHI to control for the artificial decrease created by expansions in sports leagues. Deviations in HHI is calculated by:

15 Leeds and Von AHmen, The Economics o/Sports, 475.

16 Depken, Free-Agency and the Competitiveness 0/ Major League Baseball, 205.

17 Ibid.

18 Leeds and Von Allmen, The Economics o/Sports, 475. 55

dHHI = HHI -II N (3.4)

Theoretically ideal competitive balance is achieved when each team has an equal market share of wins, or HHI = lIN where N is the number of teams in the league. By using the deviations in HHI compared to the ideal, Depken (1999) eliminated distortions to HHI caused by the number of firms or expansion in the leagues. Depken (1999) contends that standard deviation of wins is a nonlinear transformation of dHHL Standard deviation is positively related to the number of games played each season, the number of teams in the league and the difference between ideal HHI and actual HHL!9 The weakness that Depken cites for dHHI is that it is possible for it to be statistically influenced by an exogenous variable whereas standard deviation would not. 20 For example while dHHI arithmetically accounts for the expansion of a league, it does not control for any statistical influences of expansion on the league's competitive balance?! Depken's measure (dHHI) is one of the most influential developments in measuring competitive balance, and considered extremely effective. 22 However the measure still does not account for individual teams' changes in success over time, especially as they relate to fan interest.

Others of studies still have used the distribution of life-time win percentages,

Variance Decompositions (Eckard (1998, 2001a, 2001b), GINI coefficients and

19 Depken, Free-Agency and the Competitiveness o/Major League Baseball, 205.

20 Ibid.

21 Ibid.

22 Leeds and Von Allmen, The Economics o/Sports, 475. 56

Lorenz curves which measure the distributions of championships and average spreads of win percentages, as a way to gauge competitive balance in leagues.23 Humphreys

(2002) acknowledges that "each of these measures has its strengths and weaknesses, but none ofthem directly addresses the shortfall of O"L,,?4 Essentially, none of these measures can account for changes in relative standing for teams across seasons. For this reason that these measures are not appropriate for this thesis.

Team Standard Deviation and the Competitive Balance Ratio

Humphreys (2002) acknowledges the importance of using team standard deviation in any attempt to measure competitive balance. He argues that competitive balance is only important as it relates to the fans. He acknowledges that "if a league lacks competitive balance, fan interest in the weaker teams will fall and, eventually, fan interest in the stronger teams will also decline,,?5 Humphreys reasons that fans care as much about performance mobility (in terms of win percentage and rank) of teams over time as they do about the imbalance across teams in a particular year. 26

Since the possibility ofleague failure is ultimately why competitive balance is important he maintains that the best measure of competitive balance is the one to which the consumers show the greatest sensitivity.27 Noll (1974) and others also found that the Uncertainty of Outcome of a game is a significant factor in explaining

23 Humphreys, Alternative Measures a/Competitive Balance in Sports Leagues, 133.

24 Ibid, 135.

25 Ibid, 133.

26 Ibid,134.

27 Ibid,137. 57 a club's revenue, which again is essential to the possibility ofleague failure due to lack of competitive balance. 28 He incorporates Team Standard Deviation of win percentage and Within-Season Standard Deviation of win percentages into his

Competitive Balance Ratio. The Team Standard Deviation of win percentages measures team variation of success across seasons and is given by:

N I (WPC~.t - WPCT i)2 i=l (3.5) T where WPCTirepresents each team's average won-loss percentage during the T seasons, the first term in the numerator WPC~.t is the win percentage of year t, and as previously stated T represents the total number of seasons in the time period?9 The smaller the value of (JT.i' the less the variation in team i' s winning percentage during the seasons being analyzed. One problem with Team Standard Deviation of win percentages is that it does not account directly for the relative strength of teams.

Humphreys (2002) attempts to develop a measure that captures the benefits of

Team Standard Deviation of win percentages and Within Season Standard Deviation of win percentages. He argues that the relative quality of all games in a league, and the relative shifts in team success, are both essential to comprehensively measure competitive balance as it relates to fan interest.3o Humphreys (2002) uses two measures of average variation to calculate his Competitive Balance Ratio. First a

28 R. G. Noll, "Attendance, Prices, and Profits in Professional Sports," in Government and the Sports Business (Washington: Brookings Institution, 1974),

29 Humphreys, Alternative Measures a/Competitive Balance in Sports Leagues, 133. 30 Ibid. 58 measure of the average variation in a teams' win percentages can be found by averaging the a T,i across teams in the league given by:

LaT,; aT =-"-;-- (3.6) N

where N is the number of teams, and T is a period of a given number of seasons. 31

Similarly a measure of average variation of win percentages in each season is given by

N a N.t I (WPCTi,t - .500)2 I ;=1 aN = .....;1.....;·__ Where aT ,1. = (3.7) T T where T is the number of seasons, t is a given season, and N the summation of all teams.32 Humphreys (2002) uses these two averages to calculate his Competitive

Balance Ratio (CBR) given by:33

CBR = aT (3.8) aN

Humphreys contends that the CBR may be the best measure of competitive balance as it relates to fan interest, especially compared to HHI, and within-season 34 standard deviation. The ratio is bounded by 0 and 1 allowing ease of comparison.

Unlike standard deviation of win percentage the ratio is easier to compare during different time period because it does not have to be compared to an idealized value

31 Ibid.

32 Ibid.

33 Ibid. 34 Ibid. 59 that depends on the number of games played in a season. 35 This makes it useful for long term comparisons in sports leagues. Finally through regression analysis

Humphreys shows that the CBR does a better job explaining observed variation in attendance in MLB than HHI or standard deviation of win percentages.36 This is a critical conclusion as competitive balance is only important as it relates to fan interest.

The only downfall of the CBR is that it requires a large amount of observations for empirical testing that are not available in the NHL. This is especially true when looking at the relatively new implementation of a salary cap. It may however be a useful tool to use in an investigation of the salary cap after several years in existence.

For the purpose of this study the performance mobility of teams is even more important than the relative quality of teams in a season. There is little significant change in standard deviation of winning percentages across NHL seasons in the time period which can be seen in Table 3.1 :37

35 Ibid, 13 7.

36 Ibid.

37 "NHL Stats Machine Control Panel." in National Hockey League Enterprises [database online]. [cited 2007]. Available from www.nhl.com. 60

TABLE 3.1

Standard Deviation of Winning Percentages in the NHL

Year (Yr,i

2006* 0.112275

2005* 0.114327

2003 0.098367

2002 0.097146

2001 0.093253

2000 0.108812

* Salary Cap was in effect for these seasons.

The changes in (Y r,i that can be seen (namely a marginal rise in (Yr,i post lockout), do not represent the changes in ranks that occurred as a result of the implementation of a salary cap. Since Humphreys (2002) contends that increases in competitive balance are associated with increases in attendance it is interesting to consider NHL attendance totals as in Table 3.2. 38

38 "NHL Statistics," in USA TODAY [database online]. [cited 2008]. Available from http://www.usatoday.comlsportslhockey/stats/index.htm. 61

TABLE 3.2

NHL Attendance Totals

Year Attendance

2006 20,857,288

2005 20,854,299

2003 20,366,162

2002 20,406,333

2001 20,613,351

2000 20,344,965

Many NHL analysts predicted a significant drop in attendance after the 2004-

2005 season strike. However the actual attendance totals were above 2003 totals by almost 500,000 or 2.4%.39 This could be suggestive of changes in competitive balance or changes in team ranking increasing the Uncertainty of Outcome and thus fan interest. This increase is especially important since at the beginning of the 2005-

06 season many articles preached that "critics are already pointing to sagging attendance in several markets".4o This indicates that at the beginning of the season attendance may have been lower than the previous season, but increased as the season progressed. These changes in attendance coincide with an increased season standard

39 Milner, NHL Attendance Problems: It's Too Early to Stop Worrying

40 Ibid. 62 deviation (which should indicate a decrease in competitive balance). The changes in season standard deviation obviously do not capture the changes in attendance and ranks in the NHL after the 2004-05 lockout.

The measure used to study changes in competitive balance before and after the salary cap, in this investigation follows the intuition behind Humphreys (2002) use of team standard deviation. Competitive Balance is important to prevent league failure, and furthermore changes in competitive balance are only important as they relate to fan interest, and the Uncertainty of Outcome Hypothesis.41 The goal ofthis investigation is to capture not only the changes in competitive balance before and after the implementation of the salary cap but to investigate the changes that occurred as a direct result of the implementation of the salary cap. In other words it must capture the marked change in rankings seen for several teams as a result of the implementation of salary cap restrictions.

These changes include the Colorado Avalanche missing the playoffs for the first time in franchise history, Edmonton (being eighth seeded team in the playoffs) making it to the Stanley Cup Finals, Buffalo making the playoffs for the first time in three years as a horne seed, Carolina winning the Stanley cup after finishing 11 th, as well as a significant rank shake-up for almost every team.42 While using some form of team standard deviation is important because it is how teams' relative positions in the league have changed, using team standard deviation as in Humphreys (2002) or later Fenn et Al (2005) will not suffice. In using Humphreys (2002) definition of team

41 Humphreys, Alternative Measures o/Competitive Balance in Sports Leagues, 133.

42 "NHL Stats Machine Control Panel." in National Hockey League Enterprises [database online]. [cited 2007]. Available from www.nhl.com/superstats. 63 standard deviation, it would appear from simple observation that team standard deviation actually decreased in the two seasons after the implementation of the salary cap. This would indicate a decrease in competitive balance, which is counterintuitive to the change in ranks, and increase in attendance that has been seen in the NHL. The changes in Team Standard Deviation as measured by Humphreys can be seen in

Table 3.3: 43

TABLE 3.3

Team Standard Deviation of Win Percentage Comparison

WESTERN CONFERENCE TEAMSDBEFORE TEAMSDAFfER ANAHEIM 0.0687 0.0365 COLUMBUS 0.0340 0.003 CALGARY 0.0502 0.0215 CHICAGO 0.0818 0.0185 COLORADO 0.0462 0 DALLAS 0.0493 0.0155 DETROIT 0.0162 0.0335 EDMONTON 0.009 0.073 LOS ANGELES 0.0434 0.064 MINNESOTA 0.0628 0.061 NASHVILLE 0.05 0.0125 PHOENIX 0.0639 0.0425 SAN JOSE 0.0722 0.024 ST. LOUIS 0.0263 0.073 VANCOUVER 0.0337 0.0395

43 Ibid. 64

TABLE 3.3 - Continued

EASTERN CONFERENCE TEAMSDBEFORE TEAMSDAFfER ATLANTA 0.085 0.021 BOSTON 0.0654 0.006 BUFFALO 0.0803 0.009 CAROLINA 0.102 0.073 FLORIDA 0.0472 0.003 MONTREAL 0.0762 0.009 NEW JERSEY 0.055 0.018 NY ISLANDERS 0.1474 0.0425 NY RANGERS 0.0383 0.0185 OTIAWA 0.0625 0.0245 PHILADELPHIA 0.0312 0.1375 PITISBURGH 0.1239 0.143 TAMPA BAY 0.1607 0.003 TORONTO 0.0415 0.003 WASHINGTON 0.1239 0

From Table 3.3, no significant relationship can be observed in a trend between team standard deviation of win percentage before and after the implementation of the salary cap. The hypothesis that a salary cap will increase competitive balance would expect that team standard deviation would actually increase after the salary cap.

However since it has only been two years since the salary cap has been implemented it may be too early to observe this trend. Thus a variation ofteam standard deviation will be used to capture the change in win percentage that occurred as a result of the salary cap.

To use a standard deviation measure to capture this change in ranks, the measure must incorporate a reference to the entire period, not just the period after or 65 just the period before the implementation of the salary cap. There are two measures that could be used as the dependent variable, to this end. The first measure looks almost identical to Humphreys' (2002) team standard deviation, except for the second term in the numerator:

N I (WPCT;,I - WPCT i,E)2 i=1 (Yr,i = (3.9) T where WPCT i.E is the average win percentage of a team in the period before the implementation of the salary cap, used as an approximation of a team's trend in win percentage which likely would have continued but for the implementation of the salary cap.

This measure would capture the changes in team standard deviation before and as a result of the implementation of the salary cap, by comparing team success in both periods to an approximation of the team's trend in win percentage. This change will be important in determining if competitive balance has changed in the NHL, and what specifically has caused the change. 66

Determinants of Competitive Balance

There are many factors in sports and in particular in the NHL that can affect competitive balance, and change team success over time. In attempting to explain changes in team success and competitive balance, the model must take these factors into account. Creating a model that emulates the varied success of teams is not a simple task as variables of human behavior and talent are involved. Three major factors affecting competitive balance and variations in team success are top player talent, coaching ability, and team chemistry/composition, as well as structural variables of sports leagues. The structural variables of sports leagues will be detailed further in chapter IV. Figure 3.10 is a visual representation of all the factors influencing competitive balance. 67

Figure 3.10

Detenninants of Competitive Balance

Top Defensive Top Goalie Top Offensive Talent Talent Talent

TOP PLAYER TALENT

STRUCTURAL CHANGES

COMPETITIVE BALANCE

COACHING TEAM ) CHEMISTRY ABILITY /COMPOSITION

The Presence of Top Talent 68

The object ofthe game of hockey is to score more goals than the other team.

Winning thus requires a certain combination of offensive and defensive talent.

Several studies have discussed the importance of the talent pool and talent distribution to competitive balance in the NHL and other leagues. Depken (1999) found that greater concentrations of offensive talent correlated to greater deviations away from perfect parity and thus less competitive balance.44 In terms of defensive talent, Depken (1999) found that the higher the concentration of poor defensive play, the greater competitive balance in the league.45 Fenn et Al (2005) found that changes in the pool of imported (or international) players in the NHL increases competitive balance.46 They reasoned that increases in imported talent made top talent available for smaller market teams with lower budgets. 47

Kesenne (2000) outlines the theory behind the salary cap, using a two club model. He also distinguishes between two types of player talent: regular talent or top talent. Though simplistic, Kesenne's (2000) model shows that the major source of competitive balance is the difference between large market clubs and small market club's ability to spend money on acquiring talent. 48 Large market teams, with larger budgets, have a greater demand for 'top talent', and thus acquire greater

44 Depken, Free-Agency and the Competitiveness ofMajor League Baseball, 205.

45 Ibid, 213.

46 Aju J. Fenn et ai., "The Influence of Structural Changes and International Players on Competitive Balance in the NHL," Atlantic Economic Journal 33, no. 2 (06 2005): 215-224.

47 Ibid.

48 Stefan Kesenne, "The Impact of Salary Caps in Professional Team Sports," Scottish Journal of Political Economy 47, no. 4 (09 2000): 422-430. 69 concentrations of 'top talent' .49 Small market teams on the other hand acquire mostly average talent, being unable to afford top talent. 50 In theory the salary cap will reduce large market teams' demand for top talent and make top talent more available to small market teams by indirectly limiting salaries of top players. 51 Simply put, the point of a salary cap is to change the distribution of top talent. The distribution of top talent before the salary cap may have been a cause for concern as in 2003-04: of the top 25 goal scorers, only 3 were from non-playoff contending teams, of the top 25 defensemen by plus-minus, only 3 were from non-playoff contending teams (and all

th three were from teams missing the playoffs by one rank - 9 place). 52 In the case of distribution of goalies, the statistics are similar. In the top 15 goalies by save percentage - only 1 was on a non-playoff contending team. 53 These simple statistics show that non- playoff contending teams have one thing in common: a lack of top talent. The presence of top players on a team appears to be a detenninant to success, as is assumed in salary cap theory. As the presence of top talent is essential to team success, it would follow that the distribution of top talent is a detenninant of competitive balance.

The measurement of the presence of top talent is relatively new to the field of competitive balance. Previous studies have used indexes of goals for and goals against, as a way to measure the concentration of offensive and defensive talent.

49 Ibid.

50 Ibid.

51 Ibid.

52 ''NHL Stats Machine Control PaneL" in National Hockey League Enterprises [database online). [cited 2007). Available from www.nhl.comlsuperstats.

53 Goalies who are considered starting goalies, having played more than 45 games. 70

However these indexes do not focus solely on the presence of top talent that the salary cap attempts to alter. The salary cap assumes that top talent is directly related to high salaries. Richardson (2000) acknowledges that in the NHL salaries are strongly related to productivity (or skill), as the surplus between marginal revenue productivity and marginal salary was only 1.6%.54 Due to the lack of studies pertaining directly to team success and measures of top talent in the NHL, studies on determinants of salary may be considered, because of this strong correlation between productivity and salary. In the article "Salary Determination in the NHL: The Effects of Skills, Franchise Characteristics and Discrimination", Jones and Walsh (1988) determine the primary measures of offensive, defensive and goalie skill to the determination of a player's salary. 55 Jones and Walsh (1988) find that points scored are the primary determinant of salaries for forwards, while for defensemen points scored and plus-minus ratings are strongly related to salaries, and finally a player's goals against average is the primary determinant of their salary as a goalie. 56 Idson and Kahane (2000) also find that for individual determinants of salaries, points scored are the primary determinant of salaries for forwards and plus-minus statistics are a significant determinant of defensive salaries. 57

54 David H. Richardson, "Pay, Perfonnance and Competitive Balance in the National Hockey League," Eastern Economic lournal26, no. 4 (Fall 2000): 393.

55 Jones, 1. C. H. and William D. Walsh, "Salary Determination in the National Hockey League: The Effects of Skills, Franchise Characteristics, and Discrimination," Industrial and Labor Relations Review 41, no. 4 (July 1988): 592.

56 Ibid.

57 Todd L. Idson and Leo H. Kahane, "Team Effects on Compensation: An Application to Salary Determination in the National Hockey League," Economic Inquiry 38, no. 2 (April 2000): 345. 71

In summary, Salary Cap Theory hypothesizes that the greater ability oflarge market teams to acquire top talent compared to small market teams is the primary source of imbalance in the NHL. Changes in the presence and distribution of Top

Talent, are expected to change competitive balance in the league. Top Talent will be measured by three major positions: offense, defense and goalie. These positions have very different roles in the game and it will be essential to consider them separately.

Top Talent will be essential to determine the possible ramifications of the implementation of the Salary Cap.

Coaching Ability

Teams dedicate a significant amount of time to finding a suitable and experienced coach. In the event that a team does consistently poorly, teams often make drastic coaching changes, firing and hiring coaches mid-season. The theory behind these moves is that even if a team has significant talent, if a coach cannot motivate and direct that talent, a team will not be successful. In their study on salary determination, Idson and Kahane (2000) hypothesized that coaches with greater experience and coaching talent will be able to enhance the individual player's performance by utilizing players in such a way that maximizes the team's likelihood of winning a game. 58 This is comparable to the primary function of a manager of a firm - to discover complementarities between inputs, to increase output and reduce shirking. 59 Simply put the role of the coach is to find ways to increase the

58 Ibid, 350.

59 Ibid, 346. 72 productivity of the team, by finding complementarities in talent (putting lines together), and adjusting playing time to achieve the best result possible.6o

Complementarities in talent are important in hockey because there are many different roles including: goal scorer, playmaker, enforcer, and safe defensemen, all of which are strategically important. Idson and Kahane (2000) used two variables to account for differences in coaching ability: firstly the number of seasons coached in the NHL, and secondly the career win percentage in the NHL. 61 These variables capture not only experience, but also natural coaching talent independent of experience, in career win percentage. Idson and Kahane (2000) found that there is a correlation between player productivity and coaching quality.62 This conclusion is important to recognize because coaching quality may have a strong effect on team success, meaning differences in coaching talent could result in competitive imbalance even if talent were relatively equal.

Team Chemistry and Composition

In sports it is often the case that a less talented team can beat a more talented team because of an extraordinary ability to consistently work together. However, a measure of good team chemistry may lie in the realm of psychology and not modem economics. There have been a few studies that provide viable measures to be used in this investigation. Idson and Kahane (2000) find that individual productivity is rewarded at a higher rate on teams with better players. In essence, these labor inputs

60 Ibid.

61 Ibid.

62 Ibid, 353. 73 appear to be complementarities.63 This idea is useful to this investigation because it points out that having top players becomes more important as the level of talent around them increases. As the average level of talent increases, the productivity of top players, and average players increase.64 Simply put, playing with better players makes each individual player better, as there are more opportunities to score and to prevent the other team from scoring. This concept also involves the basic teamwork idea of trust, if you trust players around you to accomplish their task, this helps you focus and accomplish your task. In hockey this is important because a goal scorer has to have a play maker to feed him the puck, and defensemen behind him to protect him ifhe loses the puck, in order to be solely focused on scoring a goal. For the purposes of this investigation a measure of the average level of talent around the top players may be an important determinant to a team's success.

In another article on Pay and Performance, Debrock, Hendricks and Koenker

(2004) concluded that baseball teams with greater salary dispersion perform less well on the field even when talent differentials are attempted to be accounted for. 65 Hence, teams with greater salary dispersion are less successful ceteris paribus. A measure of salary dispersion may also be used to describe team chemistry. Debrock, Hendricks and Koenker (2004) reasoned that for most sports involving team work the proper team composition strategy involved in winning games involved an average or equal set of talented players with the possibility of some top talent, and hence a relatively

63 Ibid.

64 Ibid.

65 Lawrence Debrock, Wallace Hendricks, and Roger Koenker, "Pay and Perfonnance: The Impact of Salary Distribution on Finn-Level Outcomes in Baseball," Journal a/Sports Economics 5, no. 3 (August 2004): 243. 74 flat salary profile. 66 They find that teams with some top talent, and the rest of the team comprised of lower than average or very young players, with low salaries will perform less well. Although salary levels have changed as a result of the implementation of the salary cap, measures of salary dispersion may still be used to describe changes in team composition as long as no direct correlation is present between the implementation of salary cap and salary dispersion values. While

Debrock, Hendricks and Kroenker (2004) found this measure useful in studying the composition and success of teams in the MLB, it is unknown whether a measures of salary dispersion will have an effect on team success in the NHL.

Summary of the Theoretical Model

To summarize, in determining a model for competitive balance using a form of team standard deviation as the dependent variable, the variations in factors affecting team success will translate into variations in competitive balance. Three main factors affecting team success were discussed: the presence of top talent, coaching ability and team chemistry. It has been established by Kesenne's (2000)

Salary Cap theory and several sports economists writing on the General Theory of

Sports Leagues, that concentrations oftop talent are a main source of competitive imbalance. 67 Coaching experience and ability can affect the success of the team by finding complementarities in talent inputs, regardless of the level of talent. Finally, the team chemistry variables discussed affect the production of top talent, and describe the average talent level on the team. The Salary Cap is expected to produce a

66 Ibid.

67 Kesenne, The Impact ofSalary Caps in Professional Team Sports, 422-430. 75

change in the distribution of Top Talent and even out disparate salary levels discussed

in team chemistry variables. These variable groups are essential to explaining the

effect of the salary cap, and its possibilities in the future.

Conclusion

This chapter has discussed the most widely used ways to measure competitive

balance. It has also provided a theoretical background for the use of team standard

deviation of win percentages, as it relates to fan interest. Finally, the chapter has laid out the general concepts behind three major factors affecting competitive balance: top player talent, coaching and team chemistry. It has discussed the importance of top player talent as it relates to Salary Cap Theory, the influence of coaches on team success, and possibilities for economic measures ofteam chemistry. Theoretically, variations in these three factors will produce the variations in team success that are associated with competitive balance. The next chapter will go into more detail about the use of these variables in the model, and their implications to competitive balance.

It will also discuss the model specifications, and data used for the investigation. CHAPTER IV

METHODOLOGY

The purpose of this chapter is to present the methodology of the empirical model as presented in the previous chapter. In light of shortcomings in the empirical model using team standard deviation, a second model will be estimated using Conference Rank as the dependent variable. This dependent variable follows Humphreys' (2002) reasoning that measures of competitive balance are only important as they relate to fan interest. 1

Conference rank is important to fan interest as it determines a team's entrance to and seeding for the post-season, the ultimate goal of both teams and their fans. Both regressions will be used to explain the effect of the implementation of the salary cap on a team's success, and ultimately competitive balance. This chapter will also discuss the specific variables and their expected effect on competitive balance. For each variable, the source and nature of the data set will be presented, as well as the assumptions made in a variables use. The results of the empirical test will be presented in the following chapter.

1 Brad R. Humphreys, "Alternative Measures of Competitive Balance in Sports Leagues," Journal of Sports Economics 3, no. 2 (2002): 133.

76 77

Data Set

The empirical model will be tested using a pooled data set collected from a

variation of National Hockey League sources including NHL.com, and the Official Guide

and Record Book of the NHL. The data set has been collected for every one of the teams

participating in the 2000-01 to 2006-07 seasons, not including the 2004-05 season when

the lockout occurred, the season was cancelled and the salary cap was implemented.

There are a total of 30 teams that participated in 82 games regular season games each, for

every season in the time period. (The regular season does not include pre-season or playoff games). The regular season was used in an effort to gain the most uniform source

of data across all 30 teams.

The NHL is made up of two conferences with 15 teams each, the Western

Conference, and the Eastern Conference. The top 8 teams from each conference participate in the playoffs. Each Conference (East and West), has 3 divisions where teams play each other more often than any other team. The league has built in safeguards to ensure that the relative strength in divisions does not exclude teams from top rankings in the conference. For example if one division had 5 really strong teams, and had to play each other the most, their win-loss records would be artificially low and not reflect their talent, when compared to an equally strong team in a weak division. The League accounts for this difference by requiring that the top three ranks in the conference are awarded to each of the divisional winners by highest number of points, and the following ranks are simply awarded to the highest points. Points are awarded as follows: 2 points for a win, 1 for a tie, overtime or shoot-out loss, and 0 points for a loss. 78

During the time period, there were no new expansion teams, or team relocations.

This eliminates the need for dummy variables to account for structural changes, used in most other empirical studies of the NHL. However it will be necessary to include a

dummy variable to account for teams playing in different divisions. There was a league wide lockout during the 2004-05 season, in which no games were played. Instead a new

Collective Bargaining Agreement was signed between the NHLP A (players association),

and the team owners, that implemented several rule changes including the advent of the salary cap. One other important rule change that must be mentioned as a result of the

New Collective Bargaining Agreement (CBA) was the shoot-out. Prior to 2004-05 teams could tie in a 5 minute sudden death overtime each receiving 1 point, or one team could win by scoring once, and the winning team would receive 2 points and the losing team in overtime would receive 1 point. 2 As a result of the 2004-05 CBA, if a tie results after overtime, teams will playa sudden death shoot-out where the winning team will receive 2 points and the losing team 1 point. Win percents in the data set are thus calculated by the percent of points earned by a team out of a possible 164 points. This investigation assumes that this rule change affected all teams equally and thus did not significantly alter competitive balance.

There are 6 seasons included in the data set, and 30 teams for each season, yielding 180 observations. The dependent variable - a team standard deviation of success

- is calculated only for the period before and after the implementation ofthe salary cap.

The annual data was collected post-season with respect to each team for the following variables: the presence of top offensive talent, the presence of top defensive talent, the

2 Paul D. Staudohar, "The Hockey Lockout 0[2004 -- 05," Monthly Labor Review 128, no. 12 (12 2005): 23-29. 79 presence oftop goalie talent, coaching talent/experience, salary dispersion, and average team talent. The model will be estimated using an instrumental variables approach due to the potential endogeneity between the implementation of the salary cap, the salary dispersion, and average team talent variables.

Dependent Variables

To estimate a model that uses the cross-section variation over time of a team's success, a standard deviation of team success will be used. As outlined in the previous chapter the measure (TEAMSD) will be slightly different than Humphreys (2002) team standard deviation using a difference average win percentage, to capture changes that occurred as a result of the salary cap. 3 This measure will also account for the limited time frame of data used after the implementation of the salary cap that could make team specific variation artificially small.4 This change in definition will be explored and justified after the presentation of the equation for TEAMSD.

The first empirical model failed to meet the required assumptions for errors due to insufficient data. This limitation necessitated the use of another dependent variable to support the validity of the conclusions found in the TEAMSD model. This additional model will utilize conference rank as a dependent variable, to investigate the relationship of the independent variable groups (presented in the previous chapter) and their effect on team success. The end of this section will discuss the justification and limitations of this new dependent variable, conference rank.

3 Humphreys, Alternative Measures a/Competitive Balance in Sports Leagues, 133.

4 By changing the original definition of WPCTi,B . 80

Team Standard Deviation

Model 1 will be estimated using an adjusted measures team standard deviation

(TEAMSD) as the dependent variable, as discussed in the previous chapter. Team

standard deviation of win percentage is presented in equation 4.1 :

N "2) WPCT; ,( - WPCTi,B)2 ;=1 (JT,i = (4.1) T

where

win percentage of the ith team for the eh season, and WPCTj,B is the average win

percentage ofthe ith team for the period before the salary cap. The period before the

salary cap is used as an approximation of a trend in the team's average win percentage, that it is assumed would have continued if the salary cap had never been implemented.

Since the salary cap produced a dramatic change in ranks (and thus a change in competitive balance as it relates to the fans) that would not have occurred otherwise, it is not useful to compare team standard deviations using the original average win percentage of the period T (WPCTj,T)' This would only capture the change in deviation in win percentage over the given period (before or after the salary cap), and there is not enough data (in seasons) after the salary cap to make this a useful comparison. For example: even if as a result ofthe salary cap a team saw a huge change in ranks (between 2003-04 season and 2005-06 season), unless the ranks changed dramatically between the 2005-06 and 2006-07 seasons, this change in ranks would not be reflected in TEAMSD using

WPCTj,T. It is important to consider this change in ranks, because according to 81

Humphreys' (2002) ideas on competitive balance - competitive balance is only important

as it relates to fan interest. 5

The assumption made in using win percentage of the period before the salary cap

instead of win percentage over each period (as in Humphreys (2002), is that it is a good

representation of what the teams trend in win percentage is. And furthermore, without the

implementation ofthe salary cap, a team's trend in win percentage (and thus competitive

balance) would not have changed significantly within a few years. This assumption is

supported by an article written by Staudohar (2005) who states that it generally takes a

few years for a team to acquire sufficient talent to build itself into a playoff contender or

fall from grace, due to the multi-year duration of player contracts, especially those of top

players.6

Team standard deviation is a difficult dependent variable to use because of the

difficulty determining relationships between it and the independent variables. For example if a team's talent increases, the increase in win percentage produced will increase team standard deviation, however it is also true that if a team's talent decreases, the decrease in win percentage will produce an increase in team standard deviation.

Simply, changes in independent variables relative to previous values will increase

TEAMSD. The relationship between independent variables and TEAMSD is very difficult to determine. Using an Ordinary Least Squares (OLS) estimation the model failed to meet the necessary assumption of normality of errors, likely due to the lack of

5 Ibid.

The attendance figures in Table 3.2 show that fan attendance increased after the strike, despite predictions that a strike would hurt demand, indicating some change in competitive balance that peaked interest.

6 Staudohar, The Hockey Lockout of2004 -- 05,23-29. 82

available data. Several fixes were tried including estimating the model with different

functional forms, and combining independent variables. However, none of these fixes,

which will be discussed in detail in the Results and Conclusions Chapter, significantly

allowed the model to meet the necessary assumption of normality of error.

Conference Rank

Model 2 will be estimated using Conference Rank as a measure of team success to

test the influence of Top Talent, Coaching Experience and Team Chemistry (as described

in the previous chapter). The previous model consistently found the Salary Cap to be a

significant determinant of various forms ofTEAMSD, however given that the model

failed to meet the normality of error assumption necessary for OLS it's t-stats may be

unreliable. 7 This model is used to determine if Model 1 's findings with respect to top

talent and the salary cap are valid.

The use of conference rank as a dependent variable follows the intuition of

Humphreys' Competitive Balance Ratio. Conference rank is ultimately important to the

fan interest in the regular season and penultimate post season. Changes or consistency in

conference ranks over a number of years determines the level of Uncertainty of Outcome in the post season that drives fan interest. 8 Conference rank is not a measure of competitive balance, but the influence of this model on CFRANK has strong implications for the possible effect of the Salary Cap and competitive balance as a whole. If determinants of Conference Rank can be found, only those policies that affect the

7 Leo H. Kahane, Regression Basics (Thousand Oaks: Sage Publications Inc., 2001)

8 Humphreys, Alternative Measures o/Competitive Balance in Sports Leagues, 133. 83

detenninants will affect conference rank over time and thus competitive balance. For

example, Kesenne's (2000) Salary Cap theory assumes that Top Talent is a significant

detenninant of a team's success, and since the salary cap changes the distribution of Top

Talent, it will also improve competitive balance.9 This assumption will be tested in order

to draw conclusions about the possible success or failure of the Salary Cap. If Top Talent

variables are not significant in explaining a team's success then the Salary Cap will

ultimately fail in the NHL.

As mentioned previously the NHL is divided into two Conferences: Eastern and

Western, each with 15 teams. Conference rank refers to a team's rank in these

Conferences at the end of regular season play. The teams in each conference are

presented in Table 4.1.

9 Stefan Kesenne, "The Impact of Salary Caps in Professional Team Sports," Scottish Journal ofPolitical Economy 47, no. 4 (09 2000): 422-430. 84

TABLE 4.1 10

Eastern and Western Conference Teams in the NHL

Eastern Conference Western Conference Atlanta Thrashers Anaheim Mighty Ducks Boston Bruins Calgary Flames Buffalo Sabres Chicago Blackhawks Carolina Hurricanes Colorado Avalanche Florida Panthers Columbus Blue Jackets Montreal Canadiens Dallas Stars New Jersey Devils Detroit Redwings New York Islanders Edmonton Oilers New York Rangers L.A. Kings Ottawa Minnesota Wild Philadelphia Flyers Nashville Predators Pittsburgh Penguins Phoenix Coyotes Tampa Bay Lightning San Jose Sharks Toronto Maple Leafs St. Louis Blues Washington Capitals Vancouver Canucks

Conference Rank will always fall between 1 and 15. Conference Rank is determined by points, where 2 points are assigned for a win, 1 point for a tie, overtime or shootout loss, and 0 points are assigned for a loss in regulation time for each of the 82 regular season

games. I I The only exception to ranking by point totals is the NHL requirement that the top 3 ranks in the conference are assigned to the winner of each division to account for differences in strength among the divisions. The top 8 ranked teams advance to the playoffs where 1 plays 8, 2 plays 7, 3 plays 6 and 4 plays 5 in a 7 game series. The winner of each conference advances to the Stanley Cup Finals. By nature as teams accrue

10 "NHL Statistics," in USA TODAY [database online]. [cited 2008]. Available from http://www.usatoday.com!sports/hockey/stats/index.htm.

11 For the period 2000-2004 it was possible to tie after the overtime period, during the regular season. As a result of the CBA a shoot-out procedure was implemented, so that in the event of a tie, the game would be decided by a shoot-out. Thus, as of the 2005-06 ties where no longer possible, but 1 point is still awarded for an overtime or shoot-out loss. 85

more points relative to other teams, their conference rank will decrease, with the top

ranking being 1. As such, many of the variable relationships will be expected to be

negative. These negative relationships indicate that a variable has increased the success

ofa team.

In summary Conference Rank will be used to test the validity of the independent

variables as determinants of team success. The conclusions drawn from the model will be

used in conjunction with the results from Model 1 to establish the effect of the Salary Cap

on the competitive balance in NHL upon implementation, and in the future.

Empirical Models

Both Empirical Models will use consists of three major groups of explanatory variables (Presence of Top Talent, Coaching, and Team Chemistry/Composition) and then a number of dummy variables to account for changes in the league (implementation of the Salary Cap and the division a team plays in) as specified in the previous chapter.

This section will proceed as follows: first, the two general models will be presented,

Model 1 uses TEAMSD as the dependent variable, and Model 2 uses CFRANK. Next the variable specifications will be explained for Top Talent, Coaching, Team Chemistry and

Dummy variables. For each individual variable the expected relationship to TEAMSD and CFRANK will be considered.

Model 1 is specified in equations 4.2 and 4.3:

TEAMSD = f (presence oftop offensive talent, presence of top defensive talent,

presence of top goalie talent, coaching experience and ability, salary range,

median salary, salary cap, division) (4.2) 86

Or more specifically in equation 4.4:

TEAMSD = f (TOPOFFSD, TOPDEFSD, TOPGOALIESD, COACHEXP,

COACHAB, SALRANGE, MEDSAL, SALCAP, DIVISON) (4.3)

TEAMSD will also be estimated using the standard deviation of a comprehensive Top

Talent Score (TOPTALENT). This is demonstrated in equation 4.4:

TEAMSD = f(TOPTALENTSD, COACHEXP, COACHAB, SALRANGE,

MEDSAL, SALCAP, DIVISION) (4.4)

Model 2 will use CFRANK as a dependent variable, and the function is presented in equation 4.5:

CFRANK = f(presence of top offensive talent, presence of top defensive talent,

presence of top goalie talent, coaching experience and ability, salary range,

median salary, salary cap, division) (4.5)

Or more specifically in equation 4.6:

CFRANK = f(TOPOFF, TOPDEF, TOPGOALIE, COACHEXP, COACHAB,

SALRANGE, MEDSAL, SAL CAP, DIVISION) (4.6)

The independent variables used in each model are outlined in Table 4.2; they will be described in detail throughout the chapter. 87

TABLE 4.2

Independent Variable Definitions

Variable Definition Expected Expected Sign Sign TEAMSD RANK TOPOFF Presence of top offensive talent - TOPOFFSD* teams receive a score to represent the number and quality of players ranked + - in top 30 point scorers in a specific year TOPDEF Presence of top defensive talent- TOPDEFSD* teams receive a score to represent the number and quality of players ranked + - in top 30 defensemen by Plus-Minus rating in a specific year TOP GOALIE Presence of top Goalie - teams receive TOPGOALIESD* a score to represent goalie talent by receiving points for a goalie ranked in + - top 15 goalies by Goals against average (GAA) in a specific year TOPTALENTSD* Variation in top talent of all positions, calculated by the standard deviation of NA the sum of TOP OFF, TOPDEF, + TOP GOALIE COACHEXP Measure of coaching experience, + number of games coached in the NHL. - COACHAB Measure of natural NHL coaching + ability, in career win percentage - SALRANGE Team Chemistry as determined by a measure of salary range - - MEDSAL Measure ofteam composition, and level of talent, through the median - salary. + SALCAP Dummy Variable to account for salary + cap implemented in the 2005-06 - season DIVISION Dummy Variable to account for 6 different NHL divisions: Central Northwest, Pacific, Atlantic, + - Northeast, Southeast * Used for Model 1 88

Independent Variables

Presence of Top Talent Variables

The use of these variables (TOPOFF, TOPDEF, TOP GOALIE and their standard

deviations) stems from Salary Cap Theory which says that the larger demand for Top

Players by large market teams is the primary source of competitive imbalance. 12 It may

be useful to divide these variables according to position as talent is measured differently

for each position. 13 Depken (2001) and later Fenn et al (2005) found that concentrations

of both offensive and defensive talent affected competitive balance separately. 14 Idson

and Kahane (2000), and Jones and Walsh (1988) showed that measures of talent are

valued differently for each position with the primary measures for each position being

presented in Table 4.3 :

12 Kesenne, The Impact ofSalary Caps in Professional Team Sports, 422-430.

13 Todd L. Idson and Leo H. Kahane, "Team Effects on Compensation: An Application to Salary Determination in the National Hockey League," Economic Inquiry 38, no. 2 (April 2000): 345.

14 Aju 1. Fenn et aI., "The Influence of Structural Changes and International Players on Competitive Balance in the NHL," Atlantic Economic Journal 33, no. 2 (06 2005): 215-224. Craig Depken, "Free-Agency and the Competitiveness of Major League Baseball," Review ofIndustrial Organization 14, no. 3 (1999): 205. 89

TABLE 4.3

Measures of Talent by Position

Position Best Measure of Talent

Forwards Points Scored (ptS)D

Defense Plus-Minus Rating (+I_)ltJ

1 Goalie Goals Against Average (GAA) f

The study assumes that these are the best estimate of talent for each position. It must be acknowledged that the study also assumes that these measures of top talent are reflective of superior individual talent and not player complementarities of team production. IS It also is consistent with Salary Cap Theory, insofar as the distribution of

Top Talent is a primary source of competitive imbalance. 19

To measure the presence of top talent for each position, a score will be assigned to each team representing the number of players ranking in the top 30 in their positional measure of talent, and where those players are ranked in the top 30. There will be a separate score for offensive talent, defensive talent and goalie talent to determine if

15 Jones, J. C. H. and William D. Walsh, "Salary Determination in the National Hockey League: The Effects of Skills, Franchise Characteristics, and Discrimination," Industrial and Labor Relations Review 41, no. 4 (July 1988): 592.

16 Idson and Kahane, Team Effects on Compensation: An Application to Salary Determination in the National Hockey League, 345.

17 Jones, J. C. H. and Walsh, Salary Determination in the National Hockey League: The Effects o/Skills, Franchise Characteristics, and Discrimination, 592.

18 This is a key assumption since Idson and Kahane showed that different players' talent are complementary. It is possible that the plus-minus rating is a product of line-mates defensive and offensive production, however it is assumed that this effect is universal and the Top Plus-Minus ratings are reflective of superior individual defensive talent.

19 Salary Cap Theory as presented by Stefan Kesenne (2000) in his article the Impact of Salary Caps 90

competitive balance is affected differently by the specific distribution of talent for each

position.

In assigning point to teams, it also follows that the top ranked and the bottom

ranked in the top 30 should not receive equal points, for there are significant differences

in their point totals that make a difference to the team success in each game. Top Talent

Points for Forwards and Defense will be assigned as presented in the Table 4.4:

TABLE 4.4

Points Assigned for Top Forwards and Defense

Rank in Talent MeasureLU Points Assigned to Team21

1-10 3pts

11-20 2pts

21-30 Ipts

For example if a team had players ranked at 2, 16 and 25 for forwards in a given year their TOPOFF variable would be:

TOPOFF = 3 + 2 + 1 = 6 pts (4.7)

Different points are assigned for different ranks, as the point variation between the first ranked player and 30th ranked player is assumed to be a significant indicator of difference

20 The measure of talent used for forwards or offense is points scored, and the measure used for defense is the Plus-Minus statistic.

21 Points assigned are estimated to decline significantly in accordance with decreases in points scored. 91

in talent, or talent production that will influence a team's level of success. Another

assumption taken in assigning these specific values is that the value to the team in terms

of production declines in a similar proportion to the points assigned. It must be

acknowledged however that the majority of forwards on teams receive no points at all,

and thus any points received are an indicator of talent.

Goalie talent points will be assigned differently as each team has only 1 or 2

goalies that play consistently, so the number of top goalies to be considered must be

reduced. The Goalie position is perhaps the most vital to the game, as a Goalie directly

influences the outcome of the game, by having significant control over the goals scored

against the team. Hence the number of ranks considered does not decrease in proportion

to the number of goalies on a team. It is possible for a team to have more than one goalie

in the top goalie ranks if each goalie played about half the games.22 The points assigned

for Top Goalie Talent are presented in TABLE 4.5:

TABLE 4.523

Points Assigned for Top Goalies

Rank in GAAL'I Points Assigned

1-10 2

11-15 1

22 Goalies included in ranks must have played at least 45 games. Each NHL team has at least two goalies that record games played. The talent of both these goalies is expected to influence the result of games, although not evenly. 23 Top Goalie points are assigned according to a visual estimation of a division in GAA.

24 Goals Against Average 92

Although Jones and Walsh (1988) state that the Goals Against Average (GAA) is the best

measure of goalie talent, using it as a measure of Top Talent assumes that the Goals

Against Average is not reflective ofteam defensive attributes, and instead is a sole

measure of individual talent.25

According to Depken (1999) and Fenn et al (2005) concentrations of Top Talent

will decrease competitive balance, as Salary Cap Theory assumes that these

concentrations coincide with large market teams and large budgets.26 With the

implementation of a salary cap significant changes in the presence of talent are expected

to occur on small budget teams. Increases in the presence of talent are theoretically

expected to increase a team's win percentage in relation to its average win percentage

before the implementation of the salary cap, and thus increase TEAMSD. However it

must be acknowledged that significant decreases in the presence of Top Talent may also

significantly decrease winning percentage in relation to the average win percentage

before the implementation of the salary cap, which will also increase TEAMSD.27 It is

possible that this could have occurred for teams who had to significantly decrease their

payroll as a result of the Salary Cap. It is for this reason that the standard deviation of

Top Talent scores was used to estimate Modell - TOPOFFSD, TOPDEFSD,

TOPGOALIESD.

These variables will be defined as follows in Figure 4.8, 4.9, and 4.10.

25 Ibid.

26 Kesenne, The Impact ofSalary Caps in Professional Team Sports, 422-430. 27 TEAMSD is a measure of dispersion, and thus any dramatic changes in win percentage in relation to its mean (increases or decreases), will increase TEAMSD. 93

N L (TOPOFF;" - TOPOFFi,B)2 TOPOFFSD= i=l (4,8) T

N L (TOPDEF;" - TOPDEFi,B)2 TOPDEFSD= i=l (4.9) T

N L (TOPGOALIEi" - TOPGOALIE i,B) 2 TOPGOALIE= i=l (4.1 0) T

where similar to the definition ofTEAMSD TOPOFFSD, TOPDEFSD, and

TOPGOALIESD are the standard deviation of respective talent scores for team i, over

period of T seasons. As an example TOPOFFj,t is the score of top offensive talent of the

ith team for the tth season, and TOPOFFj,B is the average top offensive talent score of the

ith team for the period before the salary cap. The period before the salary cap is used as

an approximation of a trend in the team's average top talent score, that it is assumed

would have continued if the salary cap had never been implemented. Since the salary cap

produced a dramatic change in ranks and the hypothesis of this investigation supposes

that it did this by producing a change in the distribution of top talent, it is useful to use

the average top talent score of the period before the salary cap and not the specific period,

that many standard deviation calculations would use.

The relationship between the standard deviation of top talent variables and

TEAMSD is expected to be positive as the presence of talent is expected to correlate 94

directly to a team's success. A greater variation in the presence oftop talent will be

expected to correlate to a greater variation in team success.

The relationship of the standard deviation of Top Talent variables was found to be

significant at the 95% confidence level in Modell. The standard deviation of the sum of

Top Talent scores (TOPTALENTSD) will be used in an alternate estimation of Model 1,

in place ofTOPOFFSD, TOPDEFSD, and TOPGOALIESD, to explain changes in

TEAMSD. The specifications for the calculation of TOPTALENTSD can be seen in

equations 4.11 and 4.12:

N L(TOPTALEN~,t -TOPTALENTi,B)2 TOPTALENTSD i = i=1 (4.11) T T

where TOPTALEN~,t = TOPOFF;.t + TOPDEFi,1 + TOPGOALIEi,t (4.12)

and where TOPTALENTi,B is the average of the TOPTALENTi,t, in the period before the implementation of the salary cap (2000-2004).

Similar to the individual variation in top talent variables (TOPOFFSD,

TOPDEFSD, and TOP GOALIES D) the relationship ofthe standard deviation of Top

Talent (TOPTALENTSD) is expected to be positive. This is because according to Salary

Cap Theory, the presence of Top Talent is a significant determinant of team success.

Thus, as the standard deviation of the presence of Top Talent increases, TEAMSD is also expected to increase. A greater dispersion of talent will produce more variation in win percentage and translate into a greater TEAMSD over time. A greater significance in 95

TOPTALENTSD could indicate complementarities between positional talent, which is

expected in a team sport.

In Model 2, TOPOFF, TOPDEF, and TOP GOALIE variables are used to

investigate the relationship of the presence of top talent to a team's Conference Rank

(CFRANK). An increase in Top Talent is expected to improve a team's win percentage

or number of points, and decrease their rank. 28 Thus, TOPOFF, TOPDEF, TOP GOALIE

are all expected to have a negative relationship with CFRANK, indicating an

improvement in rank. A negative relationship may be counterintuitive to an improvement

in rank, but it is important to remember here that the best team gets a rank of 1.

Coaching Variables

Idson and Kahane (2000) hypothesize that coaches with greater experience and coaching talent will be able to enhance individual and team production in such a way that maximizes the team's likelihood of winning a game.29 The Coaching Variables represent these two aspects: experience and natural talent. The variable for coaching experience

(COACHEXP) will be measured by the total number of games coached in the NHL. The variable for coaching ability (COACHAB) will be measured by career NHL win percentage. 30 The use of both of these variables is important as they capture two

28 An increase in points or win percentage relative to other teams, will decrease a team's rank relative to other teams.

29 Idson and Kahane, Team Effects on Compensation: An Application to Salary Determination in the National Hockey League, 350.

30 Win percentage is calculated by dividing the points accrued in a season or career (where 2 points are assigned for wins, 1 for ties, overtime losses and shoot-out losses, and 0 points are assigned for lossed) by the total number of possible points (164 per season - or 2 for every game coached). Uniformity in Career win percentage is achieved by counting shoot-out losses occurring in the 2005-06 and 2006-07 seasons as ties. 96

different aspects of coaching talent. COACHEXP captures the total experience of a coach

in the NHL, where it is assumed that coaches with greater experience will be better at

handling a variety of different situations and teams. Even if coaches have a poor overall

winning percentage this statistic will represent their capability in number of seasons

coached, under the assumption that clubs will not hire 'bad' coaches for long periods of

time but struggling clubs may hire a good coach to no avail. The number of games

coached in the NHL is thus a reflection of a profit-maximizing manager or owner's belief

in the ability of a coach, which is assumed not to be arbitrary.

COACHAB captures natural coaching talent, under the assumption that some

coaching styles are more effective in the NHL than others. It is also assumed the

coaching at the NHL level is markedly different than coaching at other levels. It is for

this reason that Coaching records in minor or semi-professional leagues are not included

as it is assumed they do not correlate to talent needed to coach at the highest level in the

NHL. Acknowledging that the shoot-out rule change may alter coaching win percentage,

shoot-out losses are included in percentages as ties, to provide uniformity to the periods

before and after the new CBA. This variable assumes that career win percentage is not

reflective of talent on a teams coached but as a reflection of coaching talent. Essentially it

assumes that coaching directly and significantly effects the production of a team. In

situations where teams had more than one head coach in a given year, a weighted average

of time coached in the season will be used for both variables.

In Model 1, for both COACHEXP, and COACHAB, a significant increase in the value of coaching variables will theoretically increase win percentage significantly, and produce an increase in TEAMSD. The assumption here is that a decrease in coaching 97 variables will not occur as teams with highly paid coaches are likely large revenue teams, and are not likely to reduce their investment in coaching talent. Teams with smaller revenues have a threshold coaching spending limit, a certain amount they must spend to obtain a coach, and thus can only increase spending on coaches as they generate more revenue. Thus the assumption that decreases in coaching talent that would produce a decrease in win percentage would not occur is valid. Since coaching salaries are not included in the salary cap, it seems illogical that good teams producing high revenues would settle for inexperienced coaching. If they did, it may be because the talent and leadership experience on the team can compensate for lack of coaching experience. The standard deviation of these variables will not be taken as they are not central to the hypothesis. As well, taking the standard deviation would worsen the abnormality of error by reducing the number of data observations.

For Model 2, the relationship between both COACHEXP and COACHAB, and

CFRANK is more straightforward. An increase in coaching talent as measured by either variable is expected to increase the production of players, and the win percentage of the team. Thus, an increase in either COACHEXP or COACHAB, will produce a decrease in ranks. The relationship between both coaching variables and CONFRANK is expected to be negative. 98

Team Chemistry and Composition Variables

The final group of variables encompasses team chemistry and composition. This

is potentially the most difficult idea to measure empirically because it involves human

interactions, and the effect of leadership on team production. In a study on pay and

performance, Debrock, Hendricks and Koenker (2004) found that teams with greater

salary dispersion perform less well even when talent differentials are accounted for. 31

They theorize that high-wage strategies are associated with better win percentages, as

players are happy and motivated to perform. Salary dispersion may create animosity

among players who feel that they are in competition with one another for salary. In this

investigation, the range of salary of regular roster players will be used as a measure of

salary dispersion.32 Range will be calculated by taking the highest salary and subtracting

the lowest salary of regular roster players as is demonstrated in equation 4.13:

SAL RANGE = HIGHEST SAL - LOWEST SAL 33 (4.13)

It is assumed that as salary dispersion on small market teams decreases as a result

of the salary cap, team chemistry will improve, and the production of the team in win

percentage will improve. It is also assumed that many large market teams would have a

31 Lawrence Debrock, Wallace Hendricks, and Roger Koenker, "Pay and Perfonnance: The Impact of Salary Distribution on Finn-Level Outcomes in Baseball," Journal o/Sports Economics 5, no. 3 (August 2004): 243.

32 Regular roster players are those players listed on the pennanent roster or having played at least 45 games - according to: "NHL Statistics," in USA TODAY [database online]. [cited 2008]. Available from http://www.usatoday.com/sports/hockey/stats/index.htm.

33 Of regular roster players. 99 relatively low salary dispersion before the salary cap, and thus not see a huge change in salary dispersion as a result of the cap.

The second variable to represent team composition will measure the average talent on the team. Idson and Kahane (2000) concluded that higher average talent produces higher outcomes in win percentage especially in the presence of top talent, because player talent inputs are complementary and not separate. 34 Essentially the higher the level of average talent on a team, the higher each individual player production, as high level players induce others around them to play better. Richardson (2000) and Idson and Kahane (2000) established that salaries are primarily determined by skill. Richardson found that there is only an average 1.6% surplus between a player's salary and their marginal revenue production in the NHL. 35 This investigation will use the median salary

(MEDSAL) of regular roster players as a measure of average talent. 36 Median salary was chosen because it would not be directly influenced by the salaries of Top Talent present on a team, or the low extreme of a teams payroll as a measure of average salary would.

The expectations for this variable follow Idson and Kahane's (2000) reasoning about player complementarities, the higher the skill level of players in the middle of the salary range, the greater team and individual production. 37

34 Idson and Kahane, Team Effects on Compensation: An Application to Salary Determination in the National Hockey League, 345.

35 David H. Richardson, "Pay, Performance and Competitive Balance in the National Hockey League," Eastern Economic ]ournaI26, no. 4 (Fall 2000): 393. Marginal revenue production is measure of the revenue produced by a players skill contribution to wins.

36 Regular roster players are defmed as players who have played more than 60 games. To use players who have no player at least 60 games may skew the results as these players have not had sufficient opportunity to contribute to team output

37 Debrock, Hendricks, and Koenker, Pay and Performance: The Impact ofSalary Distribution on Firm­ Level Outcomes in Baseball, 243. 100

For Modell, again the relationship between median salary and TEAMSD is

difficult to determine, because if the median salary on a team significantly decreases, or

significantly increases, the significant change in win percentage will produce an increase

in team standard deviation. Again standard deviation will not be used here due to the

limited amount of data. In order to use median salary scored it must be assumed that the

median salary on large market teams will decrease, and the median salary on small

market teams will not significantly increase. This is a valid assumption as the payrolls on

some small market teams are still well below the salary cap, for the period after the salary

cap. A decrease in median salary for large market teams will thus produce an increase in

the volatility of their rank and an increase in TEAMSD. The relationship between

MEDSAL and TEAMSD is expected to be negative.

For Model 2, the relationship of MEDSAL and TEAMSD is expected to be

negative. Simply, if a team's median salary increases, their conference ranking is

expected to improve and get smaller. Alternately if a team's median salary decreases,

their conference ranking is expected to fall and get larger.

Salary Cap Variable

There will be a dummy variable to account for all changes made in the league that

occurred with the implementation of the New Collective Bargaining Agreement. It is

assumed that all changes to competitive balance that occurred with the new CBA are a result of the salary cap (and not other rule changes encompassed by the CBA).38 For all 4

seasons before the lockout and implementation of the salary cap (seasons 2000-01 to

38 A summary of rule changes made by the CBA can be found in Appendix 1. 101

2003-04) SALCAP will take on a value of O. For the 2 seasons after the implementation

of the salary cap SALCAP will take on a value of 1.

There are a number of rule changes that were suggested by a newly formed

Competition Committee that were implemented in the 2005-06 season along with the

salary cap. These rule changes were intended to "emphasize entertainment, skill and

competition" on the ice, and reduce "the scope of defensive tools a team may effectively

employ".39 Essentially these rules were geared to emphasize the offensive part ofthe

game of hockey. This acknowledgement further justifies the separation oftop talent

variables, as it may be that offensive talent is significantly more important to a team's

success especially after the Salary Cap. It is useful to acknowledge these rule changes, as

the SALCAP variable assumes that all changes in competitive balance are a result of the

implementation of the salary cap, and the change in distribution of top talent, and not the

result of these rule changes. The structure of Free-Agency was also slightly liberalized as

a result of the CBA, however as Fenn et al (2005) theorized, because of the gradual

nature of the liberalization, the primary effect captured by the dummy variable will be a

result of the Salary Cap.40 These rule changes that may affect competitive balance can be

seen detailed in Appendix 1.

In Model 1 the implementation of a Salary Cap is expected to increase

competitive balance by improving the win percentage of small market teams, and

decreasing the dominance of large market teams. This effect is expected to take place immediately as teams adjust the composition of their team to comply with the salary cap

39 "Collective Bargaining Agreement F AQs," in National Hockey League Enterprises [database online]. [cited 2008]. Available from http://www.nh1.comlnhlhq/cba/index.html. 40 Aju J. Fenn, Andrew Larsen, and Erin Leanne Spenner, "The Impact of Free Agency and the Salary Cap on Competitive Balance in the National Football League," Journal o/Sports Economics 7, no. 4 (November 2006): 374. 102 restrictions. Essentially the league standard deviation is expected to decrease over time, and the team standard deviation of win percentage is expected to increase immediately with the implementation of the salary cap, and remain at a higher level than before the salary cap. SALCAP is thus expected to have a positive relationship with the dependent variable TEAMSD. The SALCAP variable is perhaps the most crucial variable to this investigation, as the effect of a salary cap tout court has never been investigated empirically, and other salary caps are have rarely been investigated with respect to team standard deviation of success.

In Model 2, the relationship of the SALCAP dummy variable to CFRANK is more difficult to determine. The Salary Cap is expected to reduce the dominance of large markets teams - decreasing their win percentage and increasing (worsening) their conference rank, and is simultaneously expected to increase the win percentage of small market teams - increasing their win percentage, and decreasing their conference rank.

According to Kesenne (2000) a Salary Cap 'tout court' is expected to reduce the amount of top talent demanded by large revenue teams. The theory does not comment on a change in small revenue team's consumption of top talent, other than to say the depression of salaries may change increase the presence of talent on small market teams.

Thus the assumption made by this investigation is that the effect of reducing large market team's win percentage will dominate. That is, the presence of the salary cap indicated by a value of 1, will decrease the success of large market teams and increase their ranks. In

Model 2, SALCAP is expected to have a negative relationship with CFRANK. 103

Division Variable

An exploratory dummy variable is present in the equation to account for the presence of divisions in the NHL. This dummy variable will take on a separate value for each of the divisions as listed in Figure 4.14:

o if division = Central Division 1 if division = Northwest Division 2 if division = Pacific Division DIVISION = 3 if division = Atlantic Division 4 if division = Northeast Division 5 if division = Southeast Division

The team assignments for each division can be seen in Table 4.6:

TABLE 4.641

Division Assignments

DIVISION TEAMS

Central Chicago Blackhawks Columbus Blue Jackets Detroit Red Wings Nashville Predators St. Louis Blues Northwest Calgary Flames Colorado Avalanche Edmonton Oilers Minnesota Wild Vancouver Canucks Pacific Anaheim Ducks Dallas Stars Los Angeles Kings Phoenix Coyotes San Jose Sharks

41 "NHL Stats Machine Control Panel." in National Hockey League Enterprises [database online]. [cited 2007]. Available from www.nhl.comlsuperstats. 104

TABLE 4.6 - Continued

Atlantic New Jersey Devils New York Rangers New York Islanders Philadelphia Flyers Pittsburgh Penguins Northeast Boston Bruins Buffalo Sabres Montreal Canadiens Ottawa Senators Toronto Maple Leafs Southeast Atlanta Thrashers Carolina Hurricanes Florida Panthers Tampa Bay Lightning Washington Capitals

It may be important also to note that values 0-2 represent the Western Conference and values 3-5 represent the Eastern Conference. While DIVISION is an exploratory variable intended to investigate whether division affects TEAMSD or CFRANK, it may be possible to predict a relationship based on simple trends observed by hockey fans in

NHL standings. The predictions made here are admittedly guesswork based on teams with consistent success rates, and divisions known for their competitiveness. In MODEL

1 the relationship between DIVISION and TEAMSD is expected to be positive. The divisions with the appearances of the highest turnover in rankings are the southeast, the pacific and the Northwest (given values of 5, 2 and 1). The divisions with the lowest turnover are the Central and Atlantic divisions, assigned values of 0 and 3 - the lowest in their respective divisions. These divisions have two important teams, Detroit and New 105

Jersey, as these teams are perennial winners of their divisions, and favorites in the playoffs. Thus, the divisions that appear to have the lowest turnover have the lowest possible assignments for dummy variables. The fact that there are two conferences each with ranks 1-15 is important in this relationship because there are ranks 1-15 in divisions

0-2 and in divisions 3-5. This means that a positive relationship will still be created in that there is high turnover in the Eastern Conference. The divisions with the highest appearance oftumover (and thus the greatest TEAMSD), appear to have the highest dummy variable assignments. Thus, the relationship is expected to be positive.

In Model 2 the relationship between division and conference rank is expected to be positive. This follows the logic of the variable specification from Model 1 as the lowest division assignments for each respective conference tend to have the perennial conference and division winners Detroit, New Jersey etc. Despite the high turnover, the

Northwest conference (1), it also tends to be very strong, with 4 out of 5 teams consistently making the playoffs. The highest conference assignments also tend to have teams with consistently low success rates - such as Florida, Washington, Atlanta, L.A. and Phoenix, from the Southeast and Pacific divisions (2 and 5). These teams consistently rank low in their division (between 10-15) and have continually missed the playoffs in their conferences. Given that teams with consistently high ranking (toward 1) also have low division assignments, and teams with consistently low ranking (toward 15) have high division assignments the relationship is expected to be positive. 106

Conclusion

This chapter has detailed the empirical specifications of the theoretical model laid out in chapter III. Due to problems with the normality of error assumption required for

OLS in Model 1, a second Model was added to this investigation. The chapter started by reviewing the data set, and then proceeded to describe the dependent variables in Model 1 and 2 - Team Standard Deviation (TEAMSD) ofWPCT and RANK, and Conference

Rank (CFRANK). The independent variables that explain changes in TEAMSD and

CFRANK were laid out according to their theoretical groups: Presence of Top Talent,

Coaching, and Team Chemistry. To study the effect of the Salary Cap, a dummy variable will be included that will take on a value of 1, for the period when the salary cap was present and a value of 0, when it was not. Finally another dummy variable was added to account for difference in divisions, as the division may affect TEAMSD and CFRANK.

The next chapter will look at the results of the regression equations detailed in Chapters

III and IV. Chapter V will also make final conclusions, and summarize the implications of the results. CHAPTER V

RESULTS AND CONCLUSIONS

This chapter explains the results based on the estimation of the data described in the previous chapter. The data has been analyzed using two separate dependent variables.

Modell uses the team standard deviation of win percentages (TEAMSD) as a measure of competitive balance. Model 2 uses the conference rank (CFRANK) to investigate determinants of success for teams in the NHL.

The chapter begins by discussing the results of the regression estimation using

TEAMSD and its limitations. The next section discusses the regression results pertaining to CFRANK, or the model two equations. The third section will explore the conclusions that can be drawn from both models, especially in relation to the salary cap and the implications for the NHL. Finally there will be comments on the limitations of this research and ideas for future research pertaining to competitive balance in the National

Hockey League.

107 108

Model One Results

Table 5.1 summarizes the results of the regression analyses when Modell was estimated using the Ordinary Least Squares method. The t-statistics are in parentheses below the reported coefficients. The model was estimated using difference forms of the talent variables as discussed in the previous chapter. Regression 1 uses the standard deviation of each of the presence of top talent scores, (TOPOFFSD, TOPDEFSD,

TOPGOALIESD) as a measure of the changing presence oftop talent. Regression 2 uses

TOPTALENTSD as a measure of the changing presence of top talent. TOPTALENTSD is the standard deviation of the sum of presence of talent scores for all positions

(TOPOFF, TOPDEF, TOPGOALIE). The results of the regressions are presented in

TABLE 5.1. 109

TABLE 5.1

Model One Regression Results

DEFINITION REGRESSION REGRESSION VARIABLE 1 2 Standard deviation of top 0.00760 TOPOFFSD offensive talent (1.692)*** Standard deviation of top 0.00710 TOPDEFSD defensive talent (1.719)*** Standard deviation of top 0.0146 TOPGOALIESD goalie talent (1.671)*** Standard deviation of sum 7.78E-03 TOPTALENTSD of top talent scores (2.986)* Coaching Experience by # -2.45E-07 -2.01E-06 COACHEXP of games (-0.037785) (-0.299681 ) Coaching ability by Career -0.000128 -1.10E-04 COACHAB win % ( -1.368) (-1.312) Median Salary of all roster -3.25E-11 -2.95E-09 MEDSAL players (-0.006225) (-0.538721) Range of Salary for roster -2.12E-I0 -5.46E-I0 SALRANGE players (-0.277) (-0.669) 0.0465 0.0429 SALCAP Presence of Salary Cap (6.512)* (6.368)* 0.005505 0.00385 DIVISION Division played in (2.627)* (1.939)*** Autoregressor 0.915 0.900 AR(1) (18.510)* (19.114)* Autoregressor -0.385 -0.390 AR(2) (-4.726)* (-5.341)* Autoregressor 0.220 0.225 AR(3) (3.063)* (3.429)*

* Significance at the 99% confidence level (t-stat > 2.576)

** Significance at the 95% confidence level (t-stat > 1.960)

*** Significance at the 90% confidence level (t-stat > 1.653) 110

TABLE 5.2

Model One Regression Statistics

Statistics REGl REG2

R-Squared 0.666 0.672

Adjusted 0.643 0.652 R- Squared F-Statistic 34.00 34.00

The results of the two regressions in Model 1 are consistent in tenus of the relationships of the independent variables to TEAMSD, indicated by the significance and signs of the coefficients. Coefficients and the conclusions drawn from the Model 1 will be briefly discussed in this section and then further implications of the conclusions will be discussed in the next section on Model 2.

As expected, the standard deviation of Top Talent variables were found to be significant at the 90% confidence level when considered separately in Regression 1

(TOPOFFSD, TOPDEFSD, TOPGOALIESD) and were found to be significant at the

95% confidence level when considered together (TOPT ALENTSD) in Regression 2. This indicates that the presence of all three types of talent detenuine the level of success of the team. As expected the coefficients of the standard deviation of Top Talent variables were also all positive, indicating that the greater the change in the presence of top talent, the greater the dispersion in team win percentage over time. An increase variation of top talent in tenus of offense, defense and goalie talent will produce an increase in team 111 standard deviation of win percentage, ceteris paribus. This conclusion is important to the prospect of success for the salary cap, as salary cap theory assumes that the presence of top talent is a significant determinant of success of a team. 1 The significance of these variables also supports previous research by Idson and Kahane (2000) and Jones and

Walsh (1988) about the best measures of top talent by position.

Some comments must be also made about the relative importance of the presence of top positional talent. The analysis shows that the variation in talent in all three positions is significant in explaining variation in team success. Intuitively this means that a successful team must be comprised of top levels of defensive, offensive and goalie talent, not just one or two of the three. In Regression 2 the variation of all three top talent scores were combined in the variable TOPTALENTSD, and found to be significant at the

95% confidence level. This may lead to the conclusion that while it is important to have talent in all three areas, a higher level of talent in one position could compensate for lack of top talent in another position. For example, it may not be as necessary to have an extremely high level of top talent in defense if you have a top goalie, since the positions are interactive. This supports research proposed by Idson and Kahane (2000) about the complementary nature of talent combinations. 2

The difference in the relative importance of top positional talent to changes in team success over time can also be commented on here. The relative importance can be examined by taking the elasticity coefficients of the top talent variables as is seen in

Table 5.1. The elasticity coefficients are displayed in Table 5.3.

I Stefan Kesenne, "The Impact of Salary Caps in Professional Team Sports," Scottish Journal ofPolitical Economy 47, no. 4 (09 2000): 422-430.

2 Todd L. Idson and Leo H. Kahane, "Team Effects on Compensation: An Application to Salary Detennination in the National Hockey League," Economic Inquiry 38, no. 2 (April 2000): 345. 112

TABLE 5.3

Elasticity of Top Talent Coefficients for Model One

Variable Coefficient Elasticity

TOPOFFSD 0.007589 0.178662

TOPDEFSD 0.007088 0.190581

TOPGOALIESD 0.01458 0.130107 where elasticity is the percent change in TEAMSD for a 1% change in the standard deviation of top talent by position variable. TOPDEFSD has the highest elasticity coefficient, which means that it's effect on TEAMSD will be the greatest. For every 1 % change in the standard deviation of top defensive talent, there will be a 0.179% change in

TEAMSD. This may seem small but consider the magnitude of changes in the standard deviation of top talent scores assigned, the amount that a one player investment could change a team's win percentage over time is quite significant. This means that an investment in or decrease in defensive talent may produce the greatest increase or decrease in a team's success over time, given the positive relationship of top talent variables to TEAMSD. These findings should be considered in conjunction with the fact that TOPTALENTSD as a whole was found to be significant in Regression 2 suggesting that certain combinations of all top talent are still necessary for success. However once a team reaches a certain minimum level of talent, investments in top defensive talent will produce the greatest increase in a team's success and vice versa. TOPOFFSD and

TOPDEFSD are perhaps the most direct comparison as they used the same denominations of top talent score. It is interesting that goalie talent is shown to have the 113 least effect on TEAMSD, this could be because ofthe ultra-sensitivity of the Goal's

Against Average to the performance of one game. Goalie talent is relatively consistent on teams over time as goalie trades are the least common of all trades, even though goalies may not always rank in the top 15 of Goals Against Average year after year. 3 Thus while the goalie talent score may be changing this may not correlate to the change in goalie performance in terms of wins. While Model 1 simply shows the effect that changes in goalie talent have on the variation in win percentage, Model 2 shows that Goalie talent is particularly important to a team's yearly rank.

Coaching experience and coaching ability were found to be insignificant in both regressions of Model 1. This could be because the statistics were taken for each year and not as the standard deviation of coaching experience and coaching ability, or because coaching experience and coaching ability do not significantly influence the success of the team. It was expected that larger market teams would have a consistent level of coaching ability and experience and this would contribute to the lack of win percent fluctuation associated with competitive imbalance. However it appears that high levels of experience do not significantly influence a team's variation in win percentage. The implications of the lack of significance in coaching variables will be discussed further in Model 2.

The coefficients representing the team chemistry in terms of salary and average talent in terms of salary (MEDSAL and SALRANGE), were both found to be insignificant. This is likely because the standard deviation of these variables was not taken, however in taking the standard deviation of these variables the data pool would be significantly smaller since there are only two observations for each team over the entire

3 "NHL Stats Machine Control Pane!"in National Hockey League Enterprises [database online]. [cited 2007]. Available from www.nhl.com 114

period. Since these are exploratory variables and not critical to the hypothesis as the

salary cap and top talent variables are, extensive measures to pursue their significance

were not taken. The implications of these variables will be further discussed in Model 2,

where their standard deviation is not necessary to relate them to the dependent variable.

In perhaps the most critical finding of Modell, the variable for the presence of

the salary cap (SALCAP) was found to be positive, as expected, and significant at the

99% confidence level. This means that the presence ofthe salary cap significantly

changed the fortunes of many teams, as salary cap theorists predicted it would. This

finding, supported by the importance of top talent in determining the variation in a team's

success, suggests that the salary cap might be able to significantly change the level of

competitive balance in the NHL in the long run. The NHL has enjoyed significant

increases in attendance, which many competitive balance authors would attribute to an

increase in the Uncertainty of Outcome of each game and the season itself.4 This finding

is critical not only to the NHL - as it provides a preliminary answer to the debate over

whether the salary cap was implemented in the interests of competitive balance or simply

as a cost cutting measure, but also to other professional sports leagues who have

imple~ented several different measures in an effort to improve competitive balance and

the longevity of their leagues. Fort and Quirk (1995) and Kesenne (2000) theorized that a

salary cap 'tout court' would be the only effective policy in improving competitive

balance between large market and small market teams because it leaves profit incentives

4 Brad R. Humphreys, "Alternative Measures of Competitive Balance in Sports Leagues," Journal o/Sports Economics 3, no. 2 (2002): 133. Rodney Fort and James Quirk, "Cross-Subsidization, Incentives, and Outcomes in Professional Team Sports Leagues," Journal o/Economic Literature 33, no. 3 (09 1995); 1265-1299. 115

in tact. 5,6 This preliminary study on the effect of the salary cap provides support for their

theoretical predictions.

As expected the dummy variable for division was positive and significant. The

NHL uses divisions for cost convenience and to develop stronger rivalries to drive ticket

sales. The dummy variables for division were assigned as displayed in Figure 5.1 :

o if division = Central Division 1 if division = Northwest Division 2 if division = Pacific Division DIVISION = (5.1) 3 if division = Atlantic Division 4 if division = Northeast Division 5 if division = Southeast Division

where values 0-2 represent the Western Conference, and variables 3-5 represent the

Eastern Conference. The positive value ofthe coefficient for DIVISION suggests that the

Central division generally had the least variation in win percentage, and the Southeast

division had the most variation in win percentage. This variation suggests the highest

turnover in win percentage in the divisions which contributes to their competitiveness,

and the uncertainty of the outcome ofteam's in that division. It could be hypothesized

that the Pacific division was the most competitive in the Western Conference and the

Atlantic division was the least competitive in the Eastern Conference. The significance

of division indicates a differing level of team quality across divisions and lends support to

5 Kesenne, The Impact ofSalary Caps in Professional Team Sports, 422-430. Fort and Quirk, Cross­ Subsidization, Incentives, and Outcomes in Professional Team Sports Leagues, 1265-1299.

6 Tout Court - a salary cap implemented without any revenue sharing agreement between League Members in place. 116 the NHL policy of awarding the top three seeds of each conference to the three division winners. The significance suggests there is a significant difference between the relative quality of divisions and their competitiveness. This finding indicates that the NHL should continue to pay attention to division assignments and how they affect competitive balance.

Ordinary Least Squares regression estimations depend on 4 major assumptions about the variables and the standard error in collected data. These assumptions are that error terms are independent, the error variance is constant, the errors are normally distributed, and that there is no correlation between independent variables. Several tests were performed to assure the validity ofthe OLS estimation. Heteroskedasticity, a problem that arises when the error variance is not constant was detected using the White test.? Heteroskedasticity is an econometric problem that arises when the assumption of constant error variance, or homoskedasticity is violated. 8 When the assumption is determined to be unreasonable, the least-squares parameter estimators are not minimum variance in the class of unbiased estimators. This problem was corrected using White's

(1980) correction for standard errors. 9

7 Obs*R-squared = 27.10302 which was above the critical value of26.30.

8 Robert Pindyck and Daniel Rubinfeld, Econometric Models and Economic Forecasts (Boston: Irwin McGraw-Hill, 19998), 146.

9 Halbert White, "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica 48, no. 4 (1980): 817. 117

First Order Autocorrelation was detected in preliminary regressions using the

Durbin Watson test. 10 Serial correlation occurs when assumption of no correlation between the error terms corresponding to different observations across time is violated. I I

This is a common problem in time series data that causes the OLS estimation of the equation to be inaccurate. Including an index variable for year is one measure that can be taken to correct for serial correlation. However in preliminary regressions even with the inclusion of a variable for year, the Durbin Watson test still indicated the presence of serial correlation. Also the variable of year showed signification correlation to the

SALCAP variable, so a year variable was not included in the final regressions. Therefore a correction first theorized by Fair (1970) is employed. 12 The correction uses autoregressive models that take into account how much persistence is present in terms of

7 the dependent variable. Once the necessary autoregressive terms are incorporated, one

8 can fail to reject the null hypothesis of no serial correlation. In both Regression 1 and 2 in Model 1 the autoregressive terms were found to be significant at the 99% confidence level.

For Modell, the inclusion of autoregressive terms captures how much one period's TEAMSD is determined by last period's TEAMSD. The inclusion of these variables therefore makes intuitive sense because both calculations ofTEAMSD use the average win percentage from the four year period before the implementation of the salary

10 The Durbin Watson statistic was 0.679414 for Regression 1, with upper and lower level values being dL =1.675 and dU=1.836. For Regression 2, the Durbin Watson statistic was also below the lower limit, with upper and lower critical values being dL= 1.697 and dU= 1.841.

11 Leo H. Kahane, Regression Basics (Thousand Oaks: Sage Publications Inc., 2001)

12 R. C. Fair, "The Estimation of Simultaneous Equation Models with Lagged Endogenous Variables and First Order Serially Correlated Errors," Econometrica 38 (1970): 507. 118 cap. As well, in a sports league with competitive imbalance, it makes sense that a team's success rate and variation in success rate is related over time.

The assumption of normality necessary in an Ordinary Least Squares regression was also rejected as the Jarque Bera statistic was above its critical chi-squared value of

5.99 in both regressions. \3 This means that the null hypothesis of Normality of Errors is rejected. When a normal distribution of errors cannot be assured, the t-tests and f-tests may not be reliable. One possible correction for non-normality of errors is to employ different function forms ofthe independent variables. However after a plethora of different regression estimations using different functional forms, the Jarque Bera statistic remained above the critical chi - squared value.

In order to assure the accuracy of these statistics one could use a regression estimator that does not rely on the normality of standard errors, such as the Generalized

Method of Moments, but that is beyond the scope of this thesis as viable results are also generated in Model 2. In addition, the collection of data that would be necessary to perform a Generalized Method of Moments or Instrumental Variables regression was not possible given the constraint of data availability in the NHL. In fact the non-normality of errors leads us to an interesting conclusion about distribution of the variables and the team standard deviation of win percentages, since they are team specific. In a league with competitive imbalance it makes sense that the standard deviation of team win percentage

(and other variables) would not be normally distributed around a mean. The non- normality of error could be interpreted as representative of the grave difference between large market and small market teams, and their ability to afford talent, which many

13 In Regression 1 the Jarque Bera Statistic was 67.358 and in Regression 2 the Jarque Bera Statistic was 82.2487. 119 authors such as Fort and Quirk (1995), Vrooman (1995), and Salary Cap Theorist

Kesenne (2000) cite as the primary source of competitive imbalance. The difference between small market and large market teams would produce errors clustered around two points and not a normal bell-curve distribution.

Several other variables were tried as control variables including a dummy variable for team and a dummy variable for conference, however none of these variables were found to be significant. No control variables were necessary for other structural changes in the NHL. A variable for constant was also not included because in all regression trials it was consistently insignificant. This makes sense because it is possible in a league with competitive imbalance for a team standard deviation of win percentage to be very close to zero.

Model One Conclusions

Model 1 found that salary cap and the variation in top talent were significant in determining changes in team standard deviation of win percentage. Division was also found to be a significant determinant of team success. The salary cap increased team standard deviation of win percentage indicating that the implementation of the salary cap significantly changed the fortunes of all teams relative to the trend of their win percentages in the seasons before the salary cap. This means that the alterations, in terms of talent, that were made to comply with the salary cap, significantly changed the fortunes of all teams in the NHL. It is now important to determine whether it is possible that the uncertainty of outcome in the league in the first years after the implementation of the salary cap will persist. According to Salary Cap theory, if competitive balance and the 120 uncertainty of outcome is to continue and persist, after the initial compliance with salary cap restrictions, top talent must be a significant determinant of success and players must be able to move freely about the league. Since this model struggled with the normality of error assumption essential to OLS, another model was implemented using a simpler measure oftearn success to determine the validity of the importance oftop talent assumption used in Salary Cap Theory.

Model Two Results

In order to be sure of the validity of the explanatory variables, given the non- normality of error seen in model 1, a second model was estimated. Model 2 tests the assumption of salary cap theory that the distribution of top talent is a primary determinant of success. The results of the regression estimation can be seen in Table 5.4 and Table

5.5. 121

TABLE 5.4

Model 2 Regression Results

Variable Defmition Coefficient 13.3132 C Constant (15.31562)** Presence of top offensive players -0.391224 TOPOFF score (-3.964436)* Presence of top defensive players -0.555128 TOPDEF score (-6.43513)* -1.48541 TOP GOALIE Presence of top goalie score (-6.78826)* COACHEXP Coaching Experience in games -0.001645 *COACHAB multiplied by Career Win percentage (-1.47241) -1.53E-06 MEDSAL Median Salary of all roster players ( -2.493638)** 2.90E-08 SALRANGE Range of Salary for roster players (0.274949) Dummy variable for presence of -8.47E-02 SALCAP Salary Cap (-0.190452) Dummy variable for different -1.30E-01 DIVISION divisions ( -0.899686) 0.194145 AR(1) Autoregressor (2.513559) **

* Significance at the 99% confidence level (t-stat > 2.576)

** Significance at the 95% confidence level (t-stat > 1.960) 122

TABLE 5.5

Model 2 Regression Statistics

R-squared 0.632

Adjusted R-squared 0.612

F statistic 32.255

Durbin-Watson statistic 1.59888

Chi-squared statistic 19.705

Jarque Bera statistic 4.293

First Order Autocorrelation was detected in preliminary regressions using the

Durbin Watson test. Including an index variable for year is one measure that can be taken to correct for serial correlation. However in preliminary regressions even with the inclusion of a variable for year, the Durbin Watson test still indicated the presence of serial correlation, with a Durbin Watson Statistic of 1.59888 which falls below the lower limit statistic of 1.675. 14 Also the variable of year showed signification correlation to the

SALCAP variable, so a year variable was not included in final regressions. As in Model

1 autoregressive terms were used to correct for serial correlation as proposed by Fair

(1970), and upon inclusion of these terms one can fail to reject the hypothesis of serial correlation. ls The model met the rest of the requirements for OLS - error terms were independent, the error variance was constant and the errors were normally distributed.

14 For the Durbin Watson test the lower limit was dL = 1.675 and the upper limit was dU = 1.863.

15 Ibid. 123

All three top talent variables are negative and significant. This indicates that the

presence oftop talent in all three positions is a significant detenninant of a team's

success. This shows that it is necessary for a team to acquire top talent to be successful. A

team cannot expect to win the conference with only the presence of top forwards or only

top defensemen or only a top goalie. This makes intuitive sense, however there are still

several NHL organizations who focus solely on offensive talent or solely on defensive

and goalie talent.

From this model the relative importance of positional talent to conference rank

can be established. The elasticity coefficients used to establish the relative importance of top positional talent can be seen in Table 5.6:

TABLE 5.6

Elasticity of Top Talent Variable Coefficients For Model 2

Variable Coefficient Elasticity

TOPOFF -0.39315 -0.09883

TOPDEF -0.56538 -0.14095

TOP GOALIE -1.4873 -0.15286

where elasticity is the percent change in TEAMSD for a 1% change in the standard deviation of top talent by position variable. The elasticity coefficient is highest with

TOP GOALIE meaning that the presence of top goalie talent is the most important to conference rank. Similar to Modell, TOPDEF was found to be more important than

TOPOFF, indicating that after a certain threshold level of talent is acquired, defense is more important to winning games. 124

It may seem inconsistent that the relative importance of positional talent changes

between Model 1 and Model 2. However Model 1 measures the effect of a change in

positional talent on a team's success relative to its own previous success in terms of win

percentage and Model 2 measures the effect of positional talent on a team's success

relative to other teams. Intuitively it makes sense that goalie talent would be more

important in a conference rank as it makes small difference in win percentage and is

perhaps the most influential position on other team's success. In both cases defensive top

talent is more important than offensive top talent, and provides a greater return on

investment. This is interesting given Idson and Kahane's (2000) conclusion that

defensive talent is not rewarded at the same rate as offensive talent, in terms of salary. It

may be concluded then that above a given level of talent, the most efficient way to

improve a team's standing is to add defensive talent. 16 This conclusion has implications

for how General Manager's (GM's) build their teams, in a profit maximizing way.

The necessity of a basic level of talent for success can be seen in the significance of the variable representing median salary on a team (MEDSAL). As expected MEDSAL was negative indicating that the higher the median level of salary on a team, the lower their ranking in the conference, thus teams with higher median salaries are more

successful. This variable provides a positive outlook for the prospects of the salary cap in the future, as theoretically the salary cap will serve to limit the total salary and median salary disparity among NHL teams. It is also a useful finding for general managers (GMs) as it provides some recipe for a successful salary distribution. From this conclusion one could extrapolate that team's with a few top talent players with high salaries, and the rest

16 Idson and Kahane, Team Effects on Compensation: An Application to Salary Determination in the National Hockey League, 345. 125

of the player earning very low salaries will not be as successful as a team with several

relatively high paid players, and only and few players earning relatively low salaries (that

had a higher median salary). Median Salary provides a valuable avenue for GMs to

critique the make-up of their team and shows that the salary cap could indeed be effective

by reducing the median salary disparity between large market and small market teams.

As in Model 1, Coach experience and coaching ability were not significant in any

form in Model 2. This means that coaching experience and ability does not consistently

influence a team's conference rank. This could be because most coaches provide a similar

effect on a team's performance, or that coaches at the NHL have similar levels of talent.

It is interesting that measures of coaching talent are not significant because many NHL

teams believe that the firing and hiring of coaches will change a team's fortune, as there

are many coaches let go in the middle of the year. It still may be the case that coaches do not fit within a team's organization and with certain players but based on these results in general coaches do not consistently affect a team's fortunes. This conclusion is also interesting considering the findings of Idson and Kahane (2000), who found coaching to significantly influence the production and salaries of players, insofar as they could affect complementarities to player production. However the lack of significance of these variables suggest that coaching talent does not significantly influence the overall outcome of the team, which is ultimately important to sport's franchise.

The fact that coaching variables remain insignificant indicates that the prospects for competitive balance are even greater with the salary cap, as the amount paid for a quality coach will not be able to significantly differentiate large market and small market team's success. 126

As in Model 1 the variable for salary range was found to be insignificant. This is

likely because the disparity between the highest and lowest salary is not representative of

the actual talent on the team in the NHL. This means that the conclusion of Debrock,

Hendricks and Kroenker (2004), that teams with greater salary dispersion perform less

well even when talent differentials are accounted for, does not apply for hockey as

players do not feel the same competition for salary. 17 This supports Idson and Kahane's

(2000) finding regarding salaries that in hockey, player talent complementarities act to

increase individual player salaries. 18 In effect, by playing with better players, an

individual's production and thus their salary will increase.

In Model 2 the effect of the Salary Cap variable was found to be insignificant on

conference rank, even when considered with a dummy variable for team. This is likely

because the salary cap would have the effect of decreasing large market team's ranks,

while increasing small market team's ranks, which are counteracting effects. This makes

intuitive sense when considered in conjunction with Model 1, since salary cap was

significant in increasing teams' standard deviation of win percentages, meaning that top teams win percentages must have decreased, and bottom teams win percentages must

have increased.

In Model 2, similar to the salary cap variable, division was found to be insignificant. This is because it may be very difficult to associate one division with the highest ranks or the lowest ranks, especially since each division winner is awarded one of

17 Lawrence Debrock, Wallace Hendricks, and Roger Koenker, "Pay and Performance: The Impact of Salary Distribution on Firm-Level Outcomes in Baseball," Journal ojSports Economics 5, no. 3 (August 2004): 243.

18 Idson and Kahane, Team Effects on Compensation: An Application to Salary Determination in the National Hockey League, 345. 127

the top three seeds in the conference. However the conclusion in Model 1 still applies that

certain divisions provide greater variation in team win percentage and likely team rank

than others.

Model Two Conclusions

In finding that the presence of top talent and the median salary on a team are

significant determinants of team success, Model 2 provides further support for the

prospect of an effective salary cap. Model 1 showed that the salary cap was able to

significantly alter team fortunes as a result of initial compliance with its regulations, and

it showed that the redistribution of top talent was a key factor in this change. Model 2

supports that finding by showing changes in top talent and payrolls will be key factors to

changes in competitive balance, since they are the primary determinants of team success.

The findings of Model 2 also suggest that the most cost effective way to improve a

team's success (after a given level oftalent), would be to add defensive talent. The

insignificance coaching variables further supports the prospect of a successful salary cap,

as high revenue teams will not be able to skirt the salary cap and spend money to improve their team through coaching. This study suggests that the salary cap will be effective if the NHL can continue to promote the movement of top talent, and decrease the difference in spending on talent that still exists under the salary cap. 128

Limits and Further Research

This study provides valuable preliminary findings on the effectiveness of the

salary cap. However, the salary cap was implemented for the 2005-06 season and this

study only examines 2 years worth of data after the implementation of salary cap. While this study shows strong promise for the future of the salary cap and its effect on

competitive balance through its support of the assumption that top talent is a key ingredient to a team's success and the preliminary significance of the salary cap variable

in Modell. However more extensive research is needed as time progresses. Model 1

consistently failed to reject the null hypothesis of normality of errors in the Jarque-Bera test, likely due to the limited amount of data. 19 Further studies should be undertaken in the future to test the progression of the influence of the salary cap on competitive balance.

With more data, future studies would likely see higher r-squared values and the assumptions on the error term required in Ordinary Least Squares regression are more likely to hold true without correction. The models used in this study will be a valuable starting point for future research, however there are some considerations that should be taken.

Both Modell and 2 used Debrock, Hendricks and Koenker's (2004) notion of salary dispersion as a measure of team chemistry (SALRANGE), but this measure appears not to be applicable to the NHL. It will be necessary to find adequate variables to measure the important aspect of human behavior in professional sports such as team chemistry and leadership. Given the insignificance of the coaching variables in this study, future studies should consider the differences in success produced by the General

19 Kahane, Regression Basics 129

Manager since it is the OM that decides player personnel, salaries and composition of

talent within a team.

In future studies on the salary cap, Humphreys (2002) Competitive Balance Ratio,

or simply the tradition fonn ofteam standard deviation could also be used as a measure

of competitive balance. This study showed that the salary cap significantly changed

team's win percentages relative to their win percentage trend before the salary cap.

However in the future it will be necessary to detennine if the salary cap simply changed

team's fortunes in initial compliance with its restrictions, or whether it actually increased

the standard deviation of win percentage over time. With the limited amount of data after

the salary cap this study only established the change in fortunes associated with the initial

implementation of the salary cap.

Conclusion

This study should be considered a preliminary study on the effect of a salary cap

'tout court' on competitive balance. As discussed in the previous chapters, competitive

balance is an important and sought after concept in all sports leagues. Uncertainty of

Outcome and team success are both significant detenninants of ticket sales and revenues

in all sports leagues. However, according to Kesenne (2000), teams within a league do

not take into account the important of the uncertainty of outcome to league health and

often pursue unhealthy levels ofteam success. This means that large market teams have more incentive than small market teams in tenns of revenue to pursue team success, as the additional revenue they derive from each additional win is greater than small market teams. Fort and Quirk (1995) established that this is a problem for all professional sports 130

leagues. Many leagues have reached a point where talent disparity and lack of

competitive balance has forced them to take measures to improve competitive balance

within the league. Without some level of competitive balance (and Uncertainty of

Outcome), leagues will ultimately fail.

There have been a variety of studies done on measures that leagues have taken to

improve competitive balance, including elimination of the reserve clause, free agency,

revenue sharing and salary cap agreements. The NHL implemented the first hard salary

cap, in the absence of a revenue sharing agreement, that many sports economists

predicted would be the only measure that would improve competitive balance. This study

examined the effect of the salary cap and the central assumption in salary cap theory that

changing the distribution of top talent would significantly improve competitive balance between large and small market teams.

This study showed that the salary cap significantly altered team's fortunes with its initial implementation. The salary cap changed teams win percentages significantly from the previous years, indicating an increase in the uncertainty of outcome that was not previously present with NHL standings. The presence of top talent was also found to be associated with team success in both Models. This is important because the salary cap is meant to restrict the amount of top talent that large market teams can employ, and in doing so change the distribution of top talent in the league. It is the distribution of top talent that will ultimately alter competitive balance in the league. This idea is further supported by the importance of median salary to team success displayed in Model 2. The salary cap will serve to reduce the disparity between median salaries by essentially creating a budget constraint for all teams. 131

The control variables in this study also provided valuable infonnation about

detenninants of team success in the NHL that are worthy of comment. First of all the

presence of division was found to influence the team standard deviation of win

percentages, indicating that the relative quality of teams in all divisions is not the same,

and the uncertainty of outcome for all divisions is not the same. The unimportance of

coaching skill and experience has important implications for competitive balance in the

league as well. This means that the ability of large market teams to hire more experienced

and 'better' coaches does not significantly affect their success. There is no need for small

market teams to invest in top quality coaching (above a certain level) in order to improve.

The ability of teams to pay high coaching salaries is not a source of competitive

imbalance. However it may be that another position is vital to a team's success: the

General Manager. Further studies on the NHL whether on the salary cap or not should

consider these findings.

This study provides preliminary support for the theoretical conclusion of Fort and

Quirk (1995) that "the problem of maintaining financial viability for teams located in weak drawing markets is a major one for sports leagues .... an enforceable salary cap is the only of the cross-subsidization schemes currently in use that can be expected to accomplish this while improving the competitive balance in the league".2o Kesenne (2000) contributed that "the equilibrium on a perfectly competitive labor market of top players, given a constant supply oftop players, is found where the marginal revenues of both clubs are equal to the salary difference.,,21 This conclusion indicates that over time the

20 Fort and Quirk, Cross-Subsidization, Incentives, and Outcomes in Professional Team Sports Leagues, 1265-1299.

21 Kesenne, The Impact ofSalary Caps in ProfeSSional Team Sports, 422-430. 132

salary cap may not completely eradicate competitive imbalance as large market teams

will still employ as many top players as they can under the salary cap and it may not be

profitable for small market teams to employ top talent past the point where marginal

revenue = marginal cost of talent, salaries. This has been the case recently in the NHL as

some teams are spending as low as $27 million, with the cap set at $46 million. 22

However the salary cap is supposed to theoretically decrease the amount of top talent that

large market teams may employ, and decrease the price of top talent. This study showed

that top talent is a significant determinant of success, one of the key assumptions in

Kesenne (2000) advocating for the effectiveness ofthe salary cap.

In order for the salary cap to be effective in maintaining uncertainty of outcome

past the first years of adjustment to salary cap restrictions, the NHL will need to maintain

and improve player mobility, so that top talent distribution is continually effected by the

salary cap budget constraints. If a top talented player is in a long term low salary contract,

then the distribution of top talent will be skewed. Competitive balance could also be

further improved by working to decrease the difference in market size, focusing on

increasing attendance for small market teams. The uncertainty of outcome associated

with initial effect of the implementation of the salary cap increased attendance for many

small market teams, however this needs to continue as many of these teams are still

spending well below the salary cap.23 This study assumed that players of top talent

caliber were being paid salaries reflective of their talent, which was supported by

22 "Understanding the NHL Salary Cap," in The New York Times Company [database online]. [cited 2008]. Available from http://proicehockey.about.comlodithenewnhValsalarycapexpl.htm. Anonymous, Collective Bargaining Agreement FAQs

23 Paul D. Staudohar, "The Hockey Lockout 0[2004 -- 05," Monthly Labor Review 128, no. 12 (12 2005): 23-29. 133

Richardson (2000). In order for the salary cap to continue to be effective salaries must

reflect player production in terms of contribution to team success, as Richardson (2000)

demonstrated before the salary cap.

In conclusion, this preliminary study showed that the implementation of the salary

cap altered the competitive balance of the NHL, by changing the distribution of top talent,

as Kesenne (2000) predicted. In light of an adversarial history between players and

owners, the salary cap may have seemed like a simple cost cutting strategy; another tactic

in the "frequent wrangling over money and power" in the league. 24 However the salary

cap has the potential to alter the competitive balance in the league by continually

changing team success, if the NHL can encourage the movement of players, and possibly

decrease contract lengths, so salaries consistently reflect skill levels. The salary cap

shows promise for the financial viability of the NHL, and emphasizes the necessity of the

balance of competition on the ice and some cooperation off the ice to the success of

sports leagues.

24 Paul D. Staudohar, "The Hockey Lockout 0[2004 -- 05," Monthly Labor Review 128, no. 12 (12 2005): 23-29. APPENDIX

RULE CHANGES ASSOCIATED WITH THE

NEW COLLECTIVE BARGAINING AGREEMENT

Rule changes that may affect competitive balance include: 1

• Rink Dimension Changes - the neutral zone is reduced in size from 54 ft to

50 ft, and the goal line is moved 2 ft closer to the end boards, resulting in

the offensive zones for each team increasing in size by 4ft; the two-line

pass rule was eliminated;

• To Quicken the pace of play: a tag-up rule is implemented for off-sides;

for icing - the team that ices the puck may not change lines before the

ensuing face-o ff;

• Goalie Pads - the dimensions of goalie pads were reduced in size by 11 %;

• Shoot-out - there will be no ties. If after the 5 minute overtime period the

score is still tied, a 3 player shoot-out will ensure after a 5 minute

overtime period, and if the score is still tied after 3 shots for each team, a

sudden death shoot-out will occur to determine a winner.

I "Collective Bargaining Agreement F AQs," in National Hockey League Enterprises [database online]. [cited 2008]. Available from http://www.nh1.comlnhlhq/cbalindex.html.

134 135

• Officiating changes - there will be zero tolerance for Holding and

Hooking/Obstruction penalties, any player who shoots the puck above the

glass in his defensive zone will be penalized for delay of game, and the

Goalie may only handle the puck in a trapezoid marked directly behind the

goal. 2

2 Prior to 2005-06 season only the Goalie could receive a delay of game penalty for lifting the puck above the glass. SOURCES CONSULTED

Gould, S. J. Full House: The Spread ofExcellence from Plato to Darwin New York: Three Rivers, 1996.

Kahane, Leo H. Regression Basics. Thousand Oaks: Sage Publications Inc., 2001.

Leeds, Michael A., and Peter Von Allmen. The Economics ofSports. Boston: Pearson Addison Wesley, 2005.

Noll, R. G. "Attendance, Prices, and Profits in Professional Sports." In Government and the Sports Business. Washington: Brookings Institution, 1974.

Quirk, James P., and Rodney D. Fort. Pay Dirt: The Business ofProfessional Team Sports. Princeton, N.J.: Princeton University Press, 1992.

Scully, Gerald. The Business ofMajor League Baseball. Chicago: University of Chicago Press, 1989.

Journal Articles

Burger, J. D., and S. J. K. Walters. "Market Size, Pay, and Performance. A General Model and Application to Major League Baseball." Journal ofSports Economics 4, no. 2 (05 2003): 108-125.

Depken, Craig. "Free-Agency and the Competitiveness of Major League Baseball." Review ofIndus tria I Organization 14, no. 3 (1999): 205.

Eckard, E. Woodrow. "Free Agency, Competitive Balance and Diminishing Returns to Pennant Contention." Journal ofEconomic Inquiry 39, no. 3 (2001): 430.

El-Hodiri, Mohamed, and James Quirk. "An Economic Model of a Professional Sports League." The Journal ofPolitical Economy 79, no. 6 (November 1974): 1302.

Fair, R. C. "The Estimation of Simultaneous Equation Models with Lagged Endogenous Variables and First Order Serially Correlated Errors." Econometrica 38 (1970): 507.

136 137

Fenn, Aju J., Andrew Larsen, and Erin Leanne Spenner. "The Impact of Free Agency and the Salary Cap on Competitive Balance in the National Football League." Journal of Sports Economics 7, no. 4 (November 2006): 374.

Fenn, Aju J., Peter Von Allmen, Stacey Brook, and Thomas J. Preissing. "The Influence of Structural Changes and International Players on Competitive Balance in the NHL." Atlantic Economic Journal 33, no. 2 (06 2005): 215-224.

Fishman, Peter. "Competitive Balance and Free Agency in Major League Baseball." American Economist 47, no. 2 (Fall 2003): 86-91.

Forrest, David, James Beaumont, John Goddard, and Robert Simmons. "Home Advantage and the Debate about Competitive Balance in Professional Sports Leagues." Journal ofSports Sciences 23, no. 4 (04 2005): 439-445.

Fort, Rodney, and Maxcy Joel. "Competitive Balance in Sports Leagues: An Introduction." Journal of Sports Economics 4, no. 2 (2003): 154.

Fort, Rodney, and James Quirk. "Cross-Subsidization, Incentives, and Outcomes in Professional Team Sports Leagues." Journal ofEconomic Literature 33, no. 3 (09 1995): 1265-1299.

Humphreys, Brad R. "Alternative Measures of Competitive Balance in Sports Leagues." Journal ofSports Economics 3, no. 2 (2002): 133.

Idson, Todd L., and Leo H. Kahane. "Team Effects on Compensation: An Application to Salary Determination in the National Hockey League." Economic Inquiry 38, no. 2 (April 2000): 345.

Jones, J. C. H., and William D. Walsh. "Salary Determination in the National Hockey League: The Effects of Skills, Franchise Characteristics, and Discrimination." Industrial and Labor Relations Review 41, no. 4 (July 1988): 592.

Kahane, Leo H. "Team and Player Effects on NHL Player Salaries: A Hierarchical Linear Model Approach." Applied Economics Letter 8, no. 9 (September 2001): 629.

Kesenne, Stefan. "Revenue Sharing and Competitive Balance: Does the Invariance Proposition Hold?" Journal of Sports Economics 6, no. 1 (02 2005a): 98-106.

"Revenue Sharing and Competitive Balance: Does the Invariance Proposition Hold?" Journal ofSports Economics 6, no. 1 (02 2005b): 98-106.

"Improving the Competitive Balance and the Salary Distribution in Professional Team Sports." In Transatlantic Sport: The Comparative Economics ofNorth American and European Sports. Cheltenham, u.K. and Northampton, Mass.:; Elgar; distributed by American International Distribution Corporation, Williston, Vt, 2002, 95-108. 138

"The Impact of Salary Caps in Professional Team Sports." Scottish Journal ofPolitical Economy 47, no. 4 (09 2000): 422-430.

M El-Hodiri, and J Quirk. "The Economic Theory of a Professional Sports League." Journal ofPolitical Economy 79, no. 6 ((November-December) 1971): 1302-1319.

Pindyck, Robert, and Daniel Rubinfeld. Econometric Models and Economic Forecasts. Boston: Irwin McGraw-Hill, 19998.

Richardson, David H. "Pay, Performance and Competitive Balance in the National Hockey League." Eastern Economic Journal 26, no. 4 (Fall 2000): 393.

Rottenberg, Simon. "The Baseball Players' Labor Market." The Journal ofPolitical Economy 64, no. 3 (June 1956): 242.

Schmidt, Martin B., and David J. Bern. "Concentration of Playing Talent: Evolution in Major League Baseball." Journal ofSports Economics 6, no. 4 (11 2005): 412-419.

Staudohar, Paul D. "The Hockey Lockout of2004 -- 05." Monthly Labor Review 128, no. 12 (12 2005): 23-29.

Surdham, David G. "The Coase Theorem and Player Movement in Major League Baseball." Journal ofSports Economics 7, no. 2 (May 2006): 201.

Szymanski, Stefan, and Stefan Kesenne. "Competitive Balance and Gate Revenue Sharing in Team Sports." Journal ofIndustrial Economics 52, no. 1 (03 2004): 165- 177.

Vrooman, John. "A General Theory of Professional Sports Leagues." Southern Economic Journal 61, no. 4 (04 1995): 971-990.

White, Halbert. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity." Econometrica 48, no. 4 (1980): 817.

Miscellaneous

"Collective Bargaining Agreement F AQs." in National Hockey League Enterprises [database online]. [cited 2008]. Available from http://www.nhl.com!nhlhq/ cba/index.html.

Milner, Brian, "NHL Attendance Problems: It's Too Early to Stop Worrying," The Globe and Mail, October 24 2006, .

"NHL Statistics." in USA TODAY [database online]. [cited 2008]. Available from http://www.usatoday.com!sports/hockey/statslindex.htm. 139

''NHL Stats Machine Control Panel." in National Hockey League Enterprises [database online]. [cited 2007]. Available from www.nhl.comlsuperstats.

"Understanding the NHL Salary Cap." in The New York Times Company [database online]. [cited 2008] . Available from http://proicehockey.about.comlodlthenewnhllalsalary_cap_expl.htm.