sign in sign up
<<
Home , Alexander Ovechkin, Jay Bouwmeester, Mike Komisarek, Mikko Koivu, Point (ice hockey), Ryan Whitney

PEAK PERFORMANCE AND CONTRACT INEFFICIENCY IN THE

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

Scott Winkler

April 2013

PEAK PERFORMANCE AND CONTRACT INEFFICIENCY IN THE NATIONAL HOCKEY LEAGUE

Scott Winkler

April 2013

Economics

Abstract

The purpose of this study is to determine what age National Hockey League (NHL) players have their best seasons and how this relates to their contract earnings. The hypothesis is that NHL players have their peak performance at age 27, which indicates that long-term contracts that exceed this age create inefficiency. The study will examine player productivity by taking 30 NHL players and evaluating their performance in the years leading up to age 27 as well as years that follow. Performance measures include average production and the highest average ice-time per game. The study will also use two OLS (Ordinary Least Squares) regressions where the dependent variables are average time on ice per game and capital hit, both good indicators of how valuable a player is to his team, as well as several statistical independent variables such as points, games played and most importantly age. By gaining knowledge of peak performance, NHL organizations could better manage their teams by limiting long-term contracts to players and as a result lessen the inefficiency that exists in the market.

KEYWORDS: (National Hockey League, Performance, Age)

TABLE OF CONTENTS

ABSTRACT

1 Introduction………………………………………...…………………………… 1

2 Literature Review……………………..………………………………………… 5

3 Theory……………..……………………..……………………...……………… 11

4 Data and Methodology…………………………..……………………………… 17

4.1 Data Collection……………………………………………………………… 18

4.2 Dependent and Independent Variables……………………………………… 18

5 Results and Analysis……………………………………………….…………… 25

6 Conclusion……………………………………………………………………… 44

6.1 Limitations………………………………………………………..………… 46

6.2 Future Study…………………………..…………………….………………. 46

7 References…………………………………………...…………………………. 48

LIST OF TABLES

1 Salaries in the National Hockey League…………………….……..…… 2

2 Independent Variables and Expected Signs …………………...…….…. 19

3 Data for ..…………………………………...…….. 23

4 Contract Amounts for Alexander Ovechkin...………………………….. 23

5 Ovechkin - Summary Statistics.………………………………………... 24

6 Summary Statistics……………………………………………………... 26

7 Regression Model Results: Average Ice-Time Per Game...…………..... 29

8 Correlation Matrix..…………………………………………………….. 32

9 Regression Model Results Without Omitted and Insignificant Variables: Average Ice-Time Per Game……………………………….. 34

10 Regression Model results: Capital Hit………..………………………… 36

11 Players Examined With Mean Age of Highest Average Ice-time Per Game and Highest Point Production……………………………….. 38

12 Age of Lowest Cap Hit Per Point and Lowest Cap Hit Per Minute of Ice-Time…………………………………………………….. 40

13 Case of Highest Cap Hit Per Point – Summary Statistics……..………………………………………..……... 42

14 Case of Highest Cap Hit Per Average Minute of Ice-Time Tomas Vanek – Summary Statistics….………………………..……….. 43

Introduction

The National Hockey League (NHL) is considered one of the major sports in

North America, and its popularity is continuously growing. Since its start the NHL has grown from a game played by moonlighting workers to a vast money-making industry including 30 teams, multi-million dollar players, national media coverage, arenas, and immeasurable fan support. In the 21st century, especially, these aspects of the NHL have escalated to new heights as superstars, bigger paychecks, and larger arenas are becoming the norm of the league. To manage a league of this magnitude, regulations were established to assure competitive balance within the different teams. Through the

Collective Bargaining Agreement (CBA), an agreement settled between the NHL and the players union, rules regarding the salary cap were put in place. The salary cap is a set amount of money a team is allowed to use on its players each year, and under no circumstances can a team exceed this limit. Even though there is a salary cap, certain players in the league do still make substantial salary earnings. As the financial upside to the game increases, players are realizing the opportunity that lies ahead and have made hockey a full-time job with no off-, all in hopes of obtaining a financial boost through the next big contract.

In the NHL, if a player has performed well over several years and is viewed as a superstar in the league, he is likely to sign a lucrative deal once his contract expires. To get a better understanding of how the value of these contracts has increased over the last couple of decades, one need only look at the top 10 NHL salaries from 1991 and compare them to the top 10 salaries of 2013, as depicted on the following page.

1

Table 1

Salaries in the National

Hockey League

Top 10 NHL Salaries of 1991 Top 10 NHL Salaries of 2013

No. Player Salary No. Player Salary

Amount Amount

1 Ron Hextall $3,500,000 1 $14,000,000

2 $3,000,000 2 $12,000,000

3 $2,200,000 3 $12,000,000

4 $1,600,000 4 $12,000,000

5 Patrick Roy $1,400,000 5 Tyler Myers $12,000,000

6 $1,300,000 6 Ilya Kovalchuck $11,000,000

7 $1,250,000 7 $10,000,000

8 Denis Savard $1,250,000 8 $9,000,000

9 Chris Chelios $1,200,000 9 Alex Ovechkin $9,000,000

10 Paul Coffey $1,100,000 10 $8,500,000

SOURCE: NHL numbers.com NHL Numbers explained, http:/www.nhl numbers.com /March 5, 2013

2

These numbers make it easy to see that the NHL has evolved from a simple sport played for pleasure to a complicated business played for money. In addition, the impact of contracts is no longer limited to the value of the deal but the length as well. Long-term contracts are becoming more common in the NHL; however the negative side to this is that they are slowly creating an inefficient market. The problem with these long-term contracts is that they are guaranteed, meaning a player will receive every penny of his contract for as many years as the contract has been agreed upon regardless of how many games he plays or how he performs in these games. Deals extending ten years in length, some even up to fifteen years, are appearing all over the league for different teams, and the question now becomes, if these players are under long-term contracts, will they perform to the value of the deal ten years in the future or even five?

During the 2011-2012 NHL season the age of players in the league ranged from

18 years to 41 years. Although the game of hockey seems to be played by players of different ages, and it is clear that older players have value to their respective teams, there must be a certain age in the careers of NHL superstars where they are likely to peak and deliver their best performance. If this age point can be identified, this would make it easier to diminish the inefficiency that exists in the league reducing these lengthy deals.

This study can be important to General Managers in the league due to the salary cap. The salary cap gives GMs limited money to work with when acquiring and signing individual players to best fit the mold of a championship team. If a superstar on the team underperforms, he is essentially taking money away from the team and therefore limits the team’s ability to put forth the best product possible. Brent Cullen Estes exemplifies

3 the importance of using the team’s money efficiently as it relates to Major League

Baseball:

Given the current landscape of baseball’s labor market, it is especially important for team owners and executives to be able to determine, with some degree of certainty, a player’s performance value. With skyrocketing player salaries and the ever-diminishing realization of competitive balance, the success of an organization hinges on its ability to make correct personnel decisions in terms of signing and resigning players. (Estes, 2006)

If GMs had knowledge of the age at which players are likely to have their best season, they would run less of a risk of over committing and overpaying for a specific player.

Secondly, this study becomes important to the owners of the various NHL teams.

Despite the salary cap, most teams operate on costs under the cap limit. This is especially true during the rough economic times such as the one we are currently experiencing.

Owners are losing large amounts of money in the business world, limiting their opportunity to invest more capital in their respective teams. Even though owners might be limiting their assets to teams, they still wish to put the best possible roster on the ice in hope of winning games. The NHL is a business, and more wins generally means more interest from the fans and more earnings for the owners as well.

Thirdly, this study is important for the fans. The fans are responsible for the development of the NHL through ticket sales, buying merchandise, and watching games on TV. Although the fans do not possess the power to sign players or determine contract values they deserve to be a part of the best hockey possible. Again, this will only be possible if the market becomes less inefficient through long-term contracts.

To diminish the inefficiency that is taking place in the NHL through long-term contracts the study proposes that NHL players have their best season at the age of 27. If this proves true, it suggests that NHL teams do not benefit, and end up losing money, as

4 well as an opportunity to sign other players, by signing elite players to extensive deals.

Although these elite players might put forth a good performance prior to the age of 27, they might not be worth the contract amount late in their careers.

This introductory section has provided the importance and motivation for this study, as well as a brief look into the contract system in the NHL, especially the workings of long-term contracts and their ineffectiveness. The second section, the literature review, looks at past studies done not only in the NHL but in other sports as well, allowing us to analyze and critique the economic topics at hand and get a better understanding of the questions examined in the current thesis. The third section, theory, looks at relevant economic theory and explains how the questions asked in the study have economic importance. The fourth section, data and methodology, addresses how the study will perform the different specific tasks, including regression and analytical work.

This section also gives the sources of data collected, a description of the variables, and the expected outcomes. The fifth section, results and analysis, shows the results of the regressions and tests performed. It will also discuss the implications of the findings, the limitations of the study, and recommendations for further study.

Literature Review

The purpose of this section is to look at previous research relevant to age, player productivity, and earnings in professional sports to answer the question of whether there is a specific period during which NHL players perform their best and if this aligns with the economic value of their contracts. Although several studies focus on player productivity, age of athletes, and earnings of professional sports players, not much has been done specifically on the NHL, certainly nothing involving all three elements.

5

In human development a major focus involves the relationship between physical ability and age. In the early stages of life people do not possess the same physical attributes they do later in life. However, this development is reversed at a certain point as people get older, and the abilities from this point on decline rather than increase. Sports are connected to one’s physical attributes, and because of this a window of opportunity is created where sports performance should be at its optimal. The time at which people hit this certain point depends on the sport. For example baseball and golf are two very different sports, and require different physical abilities to play. As a result one must expect that the point of peak performance differs with the sport.

To investigate at what age a player is considered to have his best season, one must define “peak performance,” often established through previous research. The first to do a full study of a player’s skill in the NHL were Jones and Walsh (1988). Their study investigates to what extent skill differences, player by position, and the condition of the franchise would affect player salaries. With the help of an OLS regression model, a player’s salary, from the 1978-79 season provides the dependent variable and career points per game as well as games played constitute the independent variables; the authors determine when a player has his optimal productivity and that player skill is significant in determining player salary.

In a similar study looking at performance in the NHL, Mclean and Veall (1987), take player performance and set it up against yearly salary. This study varies from the previous one by also looking at the plus/minus statistic as an independent variable.

Plus/minus is a statistic that rewards a player who is on the ice while a is scored, by giving him a plus, and penalizes him if he is on the ice for a goal against, by giving him a

6 minus. This statistic indicates whether a player is helping or hurting the team over a longer period of time. This new variable gives the Mclean and Veall study a more in depth result on player productivity. In a more contemporary work, Lavoie, Grenier, and

Coulombe (1987) again look at the issue of how to determine player performance. This study offers more recent data than the previous two. The study also focuses on

Europeans and Americans, in addition to , and therefore gives a more broad view of the league as a whole. It also investigates the differences in performance between offense men and defensemen.

To build on this approach, Idson and Kahane (1995) look at individual productivity and how individual players are valued within the team, as well as how their contracts have a direct effect on team productivity. Idson and Kahane find that higher player salaries are associated with higher team revenues. This could be viewed as a counter to the current study, which is suggesting that teams should not issue long-term contracts worth large amounts of money because it creates inefficiency; however it is important to remember that, although the team’s superstars might take a salary decrease, the team will use this capital to acquire other players. The total salaries expense of the team as a whole does not change and should retain a positive relationship with team revenues.

Berri (1999) looks at how valuable individuals are to their teams in the National

Basketball League. Berri links player statistics to team wins to determine the worth of a player. To make sure there are no biases of player statistics and their position, Berri includes not only offensive variables such as points, assists, field goals, etc, but also defensive ones such as rebounding ability and the ability to shut down the opposition.

7

For the current study, to eliminate biases for the different positions in hockey (for example a forward is more likely to score higher than defensemen in most categories), the dependent variable of time-on-ice is used. This creates an equal playing field, as it simply measures how much time a player is on the ice, and therefore takes in account the defensive aspect of the game as well as the offensive.

One study by Kahane (2005) looks at production efficiency and discrimination in the NHL by using a stochastic frontier approach, a form of economic modeling. The study reveals that production inefficiency does exist in the NHL and can be connected to different inputs surrounding the team. Inputs include coaching, whether the team has relocated recently, and the age of the organization, basically recognizing that a player’s production could be a result of outside factors. Another study, performed by Idson and

Kahane (2004), examines how a player’s contract is affected by his own teammates. The phrase “good chemistry” is often used to describe players who seem to have a supreme ability to play together on the ice, which in turn creates more productivity not only for the individual players but the team as well. Using data from the National Hockey League and the National Basketball Association, the study finds that a teammate’s pay is directly influenced by other teammates. It is even shown that players get excited about the opportunity to play with elite players and therefore increase the level of their own play, which in turn increases their pay.

Now that research devoted to how player production is measured has been covered, the next step is to review the link between productivity and age. A study by

Schultz and Curnow (1988) shows this very aspect. Looking at track and field, it shows that in shorter distance races, such as the 100-meter dash, the Olympic Gold Medal

8 winners from 1896 to 1980 have a mean age of 22.85, while in a longer race, such as the

5000-meter dash, the mean age is 27.20. Here we see two events that have the same element involved, running, and simply due to change in distance, the mean age of the winners rises tremendously. Now one might think that, because this study was done over such a lengthy period, age numbers today might be different than those nearly a century ago, but actually the absolute level of performance has increased dramatically, while the age at which athletes perform best has been consistent through the time period.

The whole process of recognizing the age of peak performance takes a whole new turn when it comes to more complex sports that involve multiple abilities to play. Sports such as , golf, and baseball represent very different sports that all involve different abilities. One can look at the age of the different tennis players as they held the number one ranking in the world; from the years 1914 to 1980, the mean suggests that the peak performance age for this sport is 25.43. In the world of golf the mean for the top-ranked golfers was 34.56 between the years of 1948 and 1966. These two individual sports require multiple abilities, and the mean age for peak performance varies a lot between the two. For different statistical categories in baseball, such as runs and hits, Schultz and

Curnow (1988) also find that the peak performance of a baseball player is about 27 or 28.

A more recent study by Turner and Hakes (2007) explores how productivity differs between baseball players with similar ability. Turner and Hakes measure skill at two different rates, first the deterioration of skill over time and second the skill variation within MLB. Findings suggest that baseball hitting ability peaks close to age 27 and from there slowly starts to decrease. These studies involve a team sport, which is

9 interesting, because the different players have different abilities for their respective positions on the team, a characteristic that holds true in hockey, as well.

Studies focusing on the different aspects of long-term contracts also become relevant for the current study. As an effect of long-term contracts, the question of age and team sports has both positive and negative implications on the team. Teams might be willing to sign an older player because of his league experience, something that could be valuable not only for the individual player but also for the younger players on the roster who could pick up a thing or two from the veteran. On the other hand an older player’s productivity could be lower than what his deal suggests, as skill and efficiency tend to decrease with age. One study by MacDonald and Reynolds (1994) looks at whether or not MLB players are paid to their marginal product. Using experienced players (players who have played at least seven years in the league), they look at productivity to see if the players are paid in accordance with their performance. Their data, taken from the 1986 to

1987 seasons, show that these experienced players are in fact paid appropriately.

To counter MacDonald and Reynolds’ study, Turner and Hakes (2007) examine trends in salary as players get older. The interesting finding in this study is that a player contract seems to peak roughly 1.8 years after the level of productivity peaks. Here inefficient long-term contracts are in effect, since the pinnacle performance and pinnacle pay do not align. Another study done by Maxcy (2004) examines long-term contracts and the risk that follows within MLB. Findings show that older players are less likely to sign long-term deals because there is more uncertainty that goes along with their age.

Although this is true, it does not affect the current study, because the players examined here are much younger when signing a lengthy deal. While it makes sense that older

10 players would not sign long-term deals with questions surrounding the ability to continue at a high performance level, however, elite NHL players are signing long-term deals before the age of 25, some much younger, and as the expected peak performance of a player is age 27, the long-term deals are not efficient.

One of the major arguments against long-term deals is the idea of shirking.

Shirking says that a player will not put forth his best effort under a lengthy contract because there is less incentive to perform well. Berri and Krautmann (2006) look at shirking in the National Basketball Association. Their findings are somewhat inconclusive, as they do not find direct evidence of shirking through their study. The investigation of player productivity reveals evidence of players not performing to their optimal level; however, in the end the authors agree that no valid evidence of shirking exists in the NBA, primarily because it is too hard to prove that a player is not giving his full effort.

As mentioned several questions arise regarding sports stars, their age, their contracts and their productivity, and this is especially true in the National Hockey

League. To get a better understanding of how this is all connected, it is important to look at the theoretical issues, the subject of the following section.

Theory

This section introduces the different economic theories that related to the current study. To better understand how player productivity, age, and contracts are connected to the economic world, we describe features such as production, competitive balance, industrial organization, as well as the role of labor unions and labor relations.

11

A look inside the production of winning highlights a clear correlation between the number of star players a team has and its winning percentage. Stars are hired to increase the winning percent in the long run, and the marginal revenue product (the change in win percentage and revenue generated by adding one more star player) for sports talent is defined as follows:

MRP (W) = MP (W) * MR (W)

Here, W is the team winning percent, MP (W) is the marginal product or simply the player’s contribution to the winning percentage, and MR (W) is the marginal revenue generated by the player’s contribution to winning (Fort, 2011). In this case it becomes clear that teams benefit by having as many stars as possible; this is not as easy as it seems, however, because sports organizations are under economic budgets and must follow strict rules set forth by their respective leagues.

Salaries constitute one of the major costs of professional sports. Salaries, which include deferred payments, bonuses, worker’s compensation expense, and pension contributions, make up over 50 % of a team’s cost in every major sport. However, the costs of player development are specific to the NHL and the MLB. Extensive minor league systems impose a large cost on the affiliated NHL teams, because in exchange for developing talent, the major league team subsidizes a large part of the minor league team’s labor and operating expenses. Baseball players’ development costs in excess of

$5 million per club per year. These costs are even larger when considering that minor leagues only produce a few players per year, making the cost for developing a major league player well over $1 million (Leeds & von Allmen, (2005). Although the dollar amounts probably are not as significant as in baseball, this general relationship holds true

12 for hockey, as teams also run similar development systems. Due to the high costs that exist in the NHL, contract management becomes an important part of creating a winning organization. Teams must make the most of the resources they have to find the best players for the best price. In this category several teams make a major mistake by signing players to long-term contracts, which have become more common in recent times.

Currently, several NHL All-Stars play under long-term contracts that exceed five years in length, creating an inefficient market for several reasons. One reason for this inefficiency relates to the age of the players. After players have gone through the first three years (maximum length of a rookie deal) of their original contract, they are signed to multiyear contracts, in some cases over a ten-year period, that could be worth up to

$10 million per year. Therefore, typically, elite NHL players are 21 to 23 years old when they are done with their initial contract, and by the time they complete their second contract, they could be 30 years of age or even older. The long-term contracts become inefficient, because this study predicts that elite NHL players will have their peak performance at the age of 27. The assumption is that players will have a slight increase in their productivity in the years leading up to age 26 but will decline at a more rapid rate as the years go on past that peak. Therefore if the contract exceeds five years, teams are paying players more than their production is worth in the years following age 26.

Shirking represents another reason why it is inefficient to have long-term contracts. The idea of shirking states that, since the player under a long-term contract has his future income set for several years to come, this creates a disincentive to work. Contracts in the

13

NHL are guaranteed, so the player receives his money regardless of his performance on the ice.

Other threats to market efficiency caused by long-term contracts involve competitive balance, as well as theoretical elements associated with industrial organization. Competitive balance becomes an important aspect in sports through something economists call the uncertainty of outcome hypothesis. The uncertainty of outcome hypothesis states that, although fans will always cheer for their respective teams, they want to see a contest with an uncertain outcome. Research shows that the most interesting games are the ones where the home team has a 60 to 79 percent chance of winning. If NHL teams make choices on long-term contracts that turn out to hurt the organization, they will not be able to compete at the same level as other teams, thereby creating a competitive imbalance.

To help uphold the balance in the NHL, the league has implemented certain rules that should help stabilize the order. One aspect includes the reverse-order entry draft.

The reverse-order entry draft allows teams that finished in the bottom of the league the previous year to have a greater chance of picking first in next year’s draft. This helps competitive balance in the league by spreading the wealth of superstars across different teams. Introducing the future superstars of the NHL to different teams lessens the chance for one team to sign several players to long-term deals, as it is most often only the elite players in the league that are considered for the lengthy deals.

Other aspects of industrial organization that help make the NHL a more competitive league include a salary cap system and free agency, both of which are centered around the collective bargaining agreement (CBA). The NHL’s Collective

14

Bargaining Agreement (CBA) is the agreement set forth between the NHL and the

National Hockey League’s Players union (NHLPA) and addresses all financial rules that involve the players in the league. One major aspect of the CBA is to set a salary cap each year, meaning teams have a maximum amount of capital to use on their players. Since the lockout in 2004-2005, when the entire season was cancelled due to CBA negotiations, the salary cap has increased from $39 million in 2005, to $56.8 million in 2009, to an estimated $70.2 million during the 2012-2013 season. The rules involved with the salary cap plays a large role in organizing an NHL team. With a salary cap it becomes important for teams to manage their salaries to players in the best way possible as there is a limited amount of money they are allowed to spend. For example, if a team signs a player to a long-term contract, it is required to pay the player regardless of his performance. If the player underperforms over several years, his salary could have been put to better use on perhaps acquiring a different player who could better the team’s chances of winning games and championships. Another major aspect of the CBA is the issue of free agency. If a player is considered an unrestricted free agent (UFA), he can negotiate and receive bids from any team in the league and choose the offer he views as most acceptable. A player receives the status of UFA when he is 27 years of age or has played seven years in the league, whichever comes first. If a player is considered a restricted free agent (RFA), he can negotiate and receive bids from any team in the league; however, if his existing team chooses to match the offer of another team, the player must accept the offer from the existing team. The problem that exists with free agency, especially unrestricted free agency, is that it creates long-term contracts. Players tend to get caught in bidding wars between different teams, and in these bidding wars the

15 contract amount is not the only aspect that increases; the term of the contract does as well. Players who should in theory sign a five-year deal could now be looking at up to ten-year deals, depending on how bad a team wants him.

Finally, labor unions and labor relations play a large part in the NHL. Over the last two decades the NHL has endured three lockouts, a lockout being the flip side of a strike, meaning that instead of the players refusing to play, in this case the management refuses to let its workers play. In 1994 this took place as the NHL and the NHLPA had not signed a new collective bargaining agreement before entering the season. Then in

2004 a lockout took place due to CBA negotiations, and again in 2012 a lockout occurred as a result of CBA disputes. This becomes important to the current study as the negotiations between the NHL and NHLPA heavily involve rule changes regarding the salary cap, free agency, and long-term contracts. Three lockouts in the span of less than

20 years shows that the disputes involving these matters are ongoing, and therefore finding out when NHL players perform their best could help create an efficient market that pleases both sides.

The profession should consider designing the rules and regulations involved in contract negotiations in such a way that long-term contracts, especially those exceeding ten years in length, should not be permitted by the league. This would secure teams from the risk they undergo when committing to one All-Star for such a long period. As mentioned the salary has increased greatly over the past years, and this is one of the leading causes of long-term contracts, because teams assume they will have more money to spend on players in the future, since the salary cap is expected to increase. Perhaps if the increase in the cap from season to season were minimized, this would lead teams to

16 pay more attention to their needs down the road and limit lengthy deals. There is also room for change in player status as it pertains to free agency. If the age of becoming a

UFA were increased, it would prevent the long-term contracts that are being issued simply through the act of creating a bidding war between the teams in the league. This scenario takes place when teams, in a bidding war for a player, have reached their yearly spending amount and the only thing left to do is increase the length of the contract offer.

The economic theories reveal the relevance of the current study. Although this theory has great importance, without statistical data providing concrete evidence of what is going on with player productivity, age, and contracts, the theory becomes irrelevant and offers no opportunity for recommended improvements. The next section provides the data and model for generating this statistical evidence.

Data and Methodology

This study first looks at the player productivity of 30 different elite NHL players who were born between 1981 and 1983. These years are used because valid contract information on each player only dates back to the 2007-2008 season. Therefore these years will cover the players in the years leading up to age 27, as well as some of the seasons thereafter. The study is designed to determine if the years these players are most productive coincide with higher contract earnings. “Time-on-ice” is used to value a player’s productivity, a measure that typically increases with effectiveness in making points, goals, assists, etc. “Time-on-ice” provides a good indicator of how valuable the player is to the team, as time the specific player is on the ice is time-on-ice he is taking away from another teammate. To understand contract earnings the study will investigate how much capital hit each player is giving their respective teams. Capital hit, or cap hit, differs from salary, since the actual salary of a player changes each year depending on his 17 contract, while the total amount of the contract is spread over its length to create the capital hit. Capital hit is a better indicator of a player’s value to the team because it shows how much capital space, which is a set amount of money that the team cannot exceed, is devoted to a specific player.

The current study uses a quantitative approach to see what factors are involved in evaluating a player’s best season. It employs Ordinary Least Square (OLS) regression and looks at players that have played in the National Hockey League (NHL) at least ten years to get a better understanding of when a player would be most likely to perform his best.

Data collection

NHL.com and NHLnumbers.com provide the data for the current study. NHL.com records detailed statistics for every player who played a game in the league dating back to the 1997-1998 season. NHLnumbers.com provides information about the contracts signed by every player in the league. Thus we obtain games played, goals, assists, points, plus/minus, minutes, shots, , and age from the first source and contract value and length from the second.

Dependent and independent variables

To determine the average ice time per game of a player (dependent variable) the independent variables are only on-ice related. The chosen independent variables are: games played, goals, assists, points, plus/minus, penalty minutes, shots, captain, and age.

The table on the following page summarizes and offers expected signs for these variables.

18

Table 2

Independent Variables and Expected Signs

Variable Abbreviation Predicted Sign

Points P +

Goals G +

Assists A +

Games Played GP +

Plus/Minus +/- +

Shots SHOTS +

Penalty Minutes PIMS -

Captain CAP +

Age AGE Uncertain

19

The equations used are OLS regression equations, as follows:

1) Average ice time per game = β0 + β4P + β2G + β3A + β1GP + β5+/- +

β6SHOTS + β7PIMS + β9CAP + β10AGE

2) Contract Amount = βaverage ice time per game + βpoints + βcaptain + βage

In the first equation the dependent variable average ice time per game, measures the average amount of minutes a player spends on the ice per game and is calculated by dividing the total minutes a player has been on the ice by the number of games he has played. Average ice time per game measures how valuable a player is to his team, because the time one player spends on the ice is time that could be devoted to another player. Therefore, greater amounts of limited ice time devoted to a player reflect his importance to the team, resulting in a positive relationship between effectiveness and ice time. The following independent variables help judge that effectiveness.

The first independent variable, points, represents the combined sum of how many goals and assists a player has accumulated. The points measure is important because it shows how much of the team’s production is due to a specific player.

The second and third independent variables are goals and assists. A goal is registered when a player is the last to touch the puck before it ends up in the opposing team’s net. An assist is given to the last two players on the scoring team who had possession of the puck before it was given to the actual goal scorer; however assists are not required on every goal. Again, these statistics are important because they show how a player is contributing to the scoring production.

The fourth independent variable is games played. This is simply a count of every game a player is put on the active roster sheet. This becomes a valuable statistic, as a

20 player who misses games through injuries or suspensions becomes less valuable for his team.

The fifth variable, plus/minus, shows if a player is on the ice while his team scores a goal; if this happens he is rewarded with a plus, while if the player is on the ice when a goal is scored by the opposing team, the player is credited with a minus. The plus/minus provides one measure to judge if a player is helping or hurting his team by the time he spends on the ice.

The sixth independent variable, shots, is defined as any attempt to get the puck into the net. For the player to register a , the puck must actually have a legitimate chance of going in. A shot that would miss the net if untouched does not count. The number of shots a player takes helps contribute to a goal or a rebound situation where a goal can be made by another player. Essentially, the more a player shoots the puck, the more he helps increase the team’s scoring production.

The seventh variable, penalty minutes, occurs when a player performs an illegal act on the ice. Penalty minutes vary with the act. The most common penalty minutes assessed are 2, 5, and 10 minutes. When a player receives a penalty, his team is forced to play the length of the penalty a man short. Because of this, penalties are viewed to have a negative effect on the team, reducing the measure of productivity for the individual player.

The eighth variable, captain, is given when the players on a team exhibit leadership qualities. On each team the norm is to have one captain and two or three assistant captains. A captain has more responsibility on the ice in guiding his team in the

21 right direction as well as having the responsibility to communicate with the referees.

Thus captains are in most cases viewed as the most valuable players on the team.

The ninth and final independent variable is age. Age tells us at what point a player is in his career. A young player has a lengthy career ahead of him and therefore could be given opportunities over other players to develop, while older players can be counted on in certain situations due to their experience with the game. To help picture the analytical approach, we offer the following example.

In the second equation the dependent variable used is contracts amount. This variable shows how much a team believes a player is worth through a numerical value.

The contract amount given to a player signifies his importance to the team and should therefore have a positive relationship with the independent variable in the second equation, the independent variables being average ice time, points, captain, and age.

To better understand what numbers are involved with the different variables descriptive statistics such as means, standard deviations, minimums, and maximums will be provided. The mean will offer information on what the average of each variable is, and the standard deviation shows how much variation exists from the mean. The minimum and the maximum are simply the lowest and highest observation recorded from each variable within the study.

In Table 3, we offer an example of the information gathered for one player:

Alexander Ovechkin, and Table 4 lists his contracts for 2008 to 2012. Finally, Table 5 provides information to judge the relationship between contract amount and performance.

Note the bolded columns: Salary per minute of ice time and Salary per point. In the case of Alexander Ovechkin, considered one of the premier players in the current NHL, the

22

numbers suggest a decline in productivity even before the age of 26. In year 2012 his

points production is only half of what it was in 2008. This also corresponds with his

average ice time per game, as it went down to 19:48 in 2012 from 23:06 in 2008.

Table 3

Data for Alexander Ovechkin

Year Games Goals Assists Points Plus/ Penalty Shots Captain Age Average played minus minutes ice time per game (in min) 2008 82 65 47 112 28 40 446 1 22 23.10

2009 79 56 54 110 8 72 528 1 23 23.00

2010 72 50 59 109 45 89 368 1 24 21.78

2011 79 32 53 85 24 41 367 1 25 21.35

2012 78 38 27 65 -8 26 303 1 26 19.80

Table 4

Contract Amounts for Alexander Ovechkin

Year cap salary hit (in millions) Years remaining in contract 2008 3.834 1 2009 9.538 13 2010 9.094 12 2011 9.538 11 2012 9.023 10

23

Table 5

Ovechkin – Summary Statistics

Year Salary in Average Salary Points Salary Age millions ice time per per point minute of ice time

2008 $3.834 23.10 $165,974 112 $34,232 22 2009 $9.538 23.00 $414,696 110 $86,709 23 2010 $9.094 21.78 $417,539 109 $83,431 24

2011 $9.538 21.35 $446,745 85 $112,212 25 2012 $9.023 19.80 $455,707 65 $138,815 26

24

Ovechkin’s value to the team is declining at a time when he still has nine years remaining on his contract and will be making roughly $9 million per year for the remainder of his deal. Table 4.4 shows that Ovechkin’s productivity, from a monetary standpoint, is starting to cost his team more money. In 2008 they were paying him

$165,974 per minute of ice time per game, while in 2012 this number had jumped to

$455,707, close to triple of what it was in 2008. Also in 2008 his team was paying

$34,232 per point, while in 2012 the number increased to $138,815. By comparing the year that players such as Ovechkin have their best season with the amount of money made during that specific year, as well as the money made in the years when production is not at its peak, we provide evidence suggesting that contracts exceeding five years are not in the best interest of the organization. The following section offers the full results of the study.

Results and Analysis

The following section displays, analyzes, and discusses the results of the regressions explained in the previous section. Summary statistics are shown on the following page in Table 6. The variables used in the regression model are listed with their corresponding statistics for the mean, standard deviation, minimum, and maximum.

The results were collected from the data for the 30 players examined over a period of five-years, giving a total of 150 observations. Noteworthy items include average ice-time per game, age, capital hit, and contract length. With a mean of 19.6, a minimum of 12.7, and a maximum of 27.4, the average ice-time per game shows that the players in the current study are different players consuming different amounts of minutes. This is good for the study as it shows that the players selected were not similar in style of play.

25

Table 6

Summary Statistics

Variable Mean Standard Min Max Deviation Average Ice-Time Per 19.6 3.1 12.7 27.5 Game (TOI) Games Played (GP) 70.1 15.6 15 82

Goals (G) 19.4 12.0 0 52

Assists (A) 29.8 11.9 3 58

Points (P) 49.2 21.3 4 92

Plus/Minus (+/-) 3.6 12.3 -26 36

Penalty Minutes 49.0 28.9 2 159 (PIMS) Shots (SHOTS) 173.3 69.9 21 329

Captain (CAP) .407 .49 0 1

Age (AGE) 26.8 1.6 24 30

Capital Hit $4.2 $1.8 $0.5 $7.8 (in millions) Contract Length 4.7 2.4 1 15 (in years)

26

With a minimum of 24, and a maximum of 30, it is important to notice that the range in the study is not large and therefore creates a limitation. Although unlikely from looking at previous studies, there is a possibility that the peak age for NHL players exists outside the current range. Capital hit is interesting as it shows that the minimum, $0.5 million per year, and the maximum, $7.8 million per year, indicate a variety of players examined in the study. Lastly it is important to see that the maximum contract length is 15 years.

Even with the limited sample size that the study observed, long-term contracts exceeding

10 years in length appear.

To attain the results needed for the first equation, the dependent variable of average ice time per game is put up against on-ice variables that influence the aforementioned statistic. Once more, the independent variables are: Points (P), goals (G), assists (A), games played (GP), plus/minus (+/-), shots (SHOTS), penalty minutes

(PIMS), captain (CAP), and age (AGE). For the second equation, the dependent variable of contract amount is measured against independent variables believed to affect the value of the contract. Again, the independent variables in the second equation are average ice time per game (TOI/G) and age (AGE).

The first equation involved offers a good understanding of what affects a player’s average ice-time per game. An important variable involved in the regression is age, as knowledge of how age affects ice-time will assist us in determining at what age

NHL players perform their best. The independent variables will not explain all of the variance in the dependent variable; however each still has great importance in determining a player’s value as the independent variables used are the basic statistics used to evaluate players in the NHL on a yearly basis.

27

The second equation gives insight into what helps or hurts the contract amount of a player. Again, age is a vital variable as it will show if a player’s age is connected to the cap hit he has. For the current study, age should be significant, as the argument at hand is that NHL players are inefficient by taking up cap space through long-term contracts. To simplify the results, the regression results will be displayed in tables, including their coefficients and T-statistics. If a variable has a T-statistic between -1.96 and 1.95, at a

5% significance level it will be considered insignificant in the regression equation.

Five variables in the regression model for average ice time are considered significant (see Table 7). The first significant variable, games played, has a positive impact average ice time per game. The positive impact makes sense, as players who play more games are considered more reliable and trustworthy on the ice, which should result in more playing time. The second significant variable, assists, also has a positive impact on average ice time per game. The positive impact signifies that a player with added assists tends to be on the ice more than players with fewer assists. The third significant variable, points, has a negative impact on average ice time per game. In theory we would think that a player who produces more points will have an increased ice time per game.

One interpretation of this surprising result could be that a player’s average ice time per game has a slight carry-over effect from the previous season, meaning if a player performed well the previous season, a coach will give him more ice time based on what a player is capable of doing, rather than his point production during the current season.

This finding could even be argued to solidify the study at hand, as the player’s point production should decrease after the age of 27.

28

Table 7

Regression Model Results: Average Ice-Time Per Game (Coefficients listed first) (T-Statistics are in parentheses)

Independent Variable Dependent Variable: Average Ice-Time Per Game GP 0.0565907 (2.67) **

G (omitted)

A 0.185685 (3.76) ***

P - 0.1353211 (-3.41) ***

+/- 0.0470349 (2.45) *

PIMS -0.0159602 (-1.86)

SHOTS 0.0057888 (0.70)

CAP 1.700858 (3.67) ***

AGE -0.1784866 (-1.33)

R-Squared 0.3350

Adjusted R-Squared 0.2973

*** Significant at 0.001 ** Significant at 0.01 * Significant at 0.05

29

The fourth significant variable, plus/minus, has a positive effect on average ice time per game. This positive impact tells us that a player who is on the ice for more goals scored than let in is awarded with more playing time. This makes sense; coaches would likely want to have a player with this attribute in key situations of a game, perhaps at the end during a tight game where a goal either way could be the difference between a win and a loss. The fifth variable, captain, has a positive effect on average ice time. Again this is logical because a captain, a player who displays leadership qualities, will be used in difficult situations of the game, and as a result his ice-time will increase.

Three variables in the regression model show up as insignificant, but we will discuss the signs of those relationships. The first insignificant variable, penalty minutes, has a negative relationship to average ice time per game. The negative impact suggests that a player who spends more time in the penalty box receives less ice time. This is logical for two reasons, first when a player is sitting in the penalty box, or receives a game misconduct and must leave the game entirely, that is time he is unable to be on the ice to increase his average ice time per game. Secondly if a player receives a lot of penalty minutes he is considered a risk because his team must play shorthanded during that time, increasing the chances of the opposing team to score; this would disincentivize the coach from putting the player on the ice. The second insignificant variable, shots, shows a positive relationship to average ice time per game. This makes sense, as a player who is given more time on the ice increases his chances to put the puck on net and get credited with a shot. The third insignificant variable, age, relates negatively to average ice time per game. This helps solidify the theory that a player’s peak performance might be at the age of 27, since as age increases the average ice time per game decreases. Even

30 though age is considered insignificant, which was expected (as the average ice-time will not change to a large extent as a result of age), it tells that the value of the player is diminishing to the team. The last independent variable, goals, is omitted due to high collinearity (shown in Table 8), meaning there is a close relationship between variables.

Goals are related to points, as points are a combination of assists and goals.

The R-squared, with a value of 0.335, and adjusted R-squared, with a value of

0.2973, show us that the independent variables in the model account for approximately

33.5% of the variance in the dependent variable average ice-time per game.

The correlation matrix reveals some statistics worth mentioning. The first column displays the correlation between the dependent variable, average ice time per game, and the rest of the independent variables. Three variables including games played, assists, and captain show up as strongly correlated to the dependent variable. Games played

(r=0.244) is understandably highly correlated with average ice-time per game since, when a player increases his games played during a season, he is viewed as a trustworthy and experienced player, which should help his average ice-time per game. Assists (r=0.250) are also highly correlated with the dependent variable, because a player who produces more assists should have an increase in his ice-time. Lastly, captain (r=0.314) has the highest correlation with the dependent variable. This is expected because a captain in most cases is a player who plays in all kinds of different situations on the ice, including power-play and penalty-killing, and this increases average ice-time per game.

31

Table 8

Correlation Matrix

Ice-Time Games Goals Assists Points Plus/ Penalty Shots Captain Age Played Minus Minutes Ice-Time 1.000

Games 0.244 1.000 Played Goals -0.153 0.436 1.000

Assists 0.250 0.586 0.593 1.000

32 Points 0.053 0.573 0.894 0.891 1.000

Plus/ Minus 0.144 0.072 0.126 0.256 0.214 1.000 Penalty -0.100 0.382 0.144 0.007 0.085 0.100 1.000 Minutes Shots 0.046 0.632 0.874 0.718 0.892 0.113 0.143 1.000

Captain 0.314 0.097 0.014 0.169 0.102 -0.194 -0.133 0.138 1.000

Age -0.011 -0.064 -0.165 -0.129 -0.165 -0.048 -0.136 -0.086 0.150 1.000

The columns after the first display the correlation between the independent variables. High correlation values between the variables suggest muliticollinearity. The table shows clearly that multicollinearity is taking place. Analyzing the third column, goals, we see a high correlation between this variable and points (r=0.894) as well as shots (r=0.874). This collinearity exists, since points are a product of combining goals and assists, making them highly connected. Shots and goals are related simply because, the more shots a player gets on net, the higher his chance of scoring becomes. This all combines to explain why goals were omitted from the regression; the variable had high correlation with other independent variables. It is also noteworthy to look at the high correlation between assists and points (r=0.891), assists and shots (r=0.718), as well as points and shots (r=0.892). The four variables are all connected as explained through the previous discussion.

The following table, Table 9, shows an easier to read display of the same regression model run in Table 7, however this time without the omitted and insignificant variables. Here the coefficients and T-statistics for the independent variables do not change, however the R-squared and adjusted R-squared are slightly different. In Table 7 the R-squared has a value of 0.335, and the adjusted R-squared has a value of 0.2973, while in Table 9 the R-squared has a value 0.3106, and the adjusted R-squared has a value of 0.2866. Table 9 accounts for approximately 31.1% of the variance in the dependent variable average ice-time per game, slightly less than in Table 7. This difference comes from eliminating the other variables; even though they were insignificant they still help explain the dependent variable at a small level.

33

Table 9

Regression Model Results Without Omitted and Insignificant Variables: Average Ice-Time Per Game (Coefficients listed first) (T-Statistics are in parentheses)

Independent Variable Dependent Variable: Average Ice-time Per Game GP .0565907 (2.67)**

A .185685 (3.76)***

P - 0.1353211 (-3.41)***

+/- 0.0470349 (2.45)*

CAP 1.700858 (3.67)***

R-Squared 0.3106

Adjusted R-Squared 0.2866

*** Significant at 0.001 ** Significant at 0.01 * Significant at 0.05

34

Table 10 offers the results of the second equation. The second equation gives insight to what affects a player’s capital hit by using four independent variables that are good indicators of a player’s value to a team on the ice. The independent variables are: average ice-time per game (TOI/G), points (P), captain (CAP), and age (AGE). An important variable involved in the regression is age, since age will show if a player is taking up more cap space as he gets older and in theory is less valuable to a team. Again to simplify the results, the regression results will be displayed in tables, including their coefficients and T-statistics. If a variable has a T-statistic between -1.96 and 1.95, at a

5% significance level it will be considered insignificant in the regression equation.

Two variables in the regression model for capital hit are considered significant.

The first significant variable, points, has a positive effect on capital hit. The positive effect makes sense, since the more points a player produces, the more he is viewed as a superstar in the league, which leads to a higher salary and therefore a larger cap hit for the team. Owners of teams are often willing to spend more on players who are considered superstars in the league, viewing it as an investment that produces revenues from higher ticket sales and sales of merchandise. The second significant variable, age, also has a positive effect on capital hit. The positive effect can be explained by the fact that elite players are signing lucrative long-term deals during the years examined in the regression. With the exception of one player out of thirty in the study, all salaries were level or escalating from the previous year spent in the league.

35

Table 10

Regression Model results: Capital Hit (Coefficients listed first) (T-Statistics are in parentheses)

Independent Variable Dependent Variable: Capital Hit

TOI/G 0.0346405 (0.79)

P 0.031748 (5.02)***

CAP 0.3271237 (1.14)

AGE 0.4136868 (5.01)***

R-Squared 0.2584

Adjusted R-Squared 0.2379

*** Significant at 0.001 ** Significant at 0.01 * Significant at 0.05

36

Two variables in the regression model for capital hit are considered insignificant.

The first insignificant variable, average ice time per game, showed a positive relationship to capital hit, as expected; the more the team values a player’s performance on the ice, the more likely the team is to give this player additional ice-time in games. The second insignificant variable, captain, again showed a positive relationship to cap hit. The positive relationship can be explained through the fact that a player who bestows the honor of captaincy often possesses leadership qualities that not only help the team on the ice but off the ice as well. The R-squared, with a value of 0.2584, and adjusted R- squared, with a value of 0.2379, show us that the independent variables in the model account for around 25.8% of the variance in the dependent variable capital hit.

The second part of the results section steers away from the regression approach towards more simple statistical work. The first upcoming table (Table 11) shows all the players examined in the research as well as the age at which each player has the highest average ice-time per game and the age of their highest point production. It also gives information on the mean age of both categories of the group as a whole. This knowledge gives good insight into when the elite NHL players are likely to perform their best

(remembering that the age range in the study is 24-30 years). The second table tells us when the teams received the most average ice time per game and the utmost point production for the smallest cap hit possible, basically when NHL teams got the most

“bang for their buck.”

37

Table 11

Players Examined With Mean Age of Highest Average Ice-time Per Game and Highest Point Production

player age: age: mean mean highest highest age of age P TOI/G P TOI/G 1 25 25 26.6 26.4 2 28 28 3 Alex Semin 25 26 4 Ilya Kovalchuck 28 25 5 27 24 6 21 24 7 Mikko Koivu 25 26 8 25 25 9 Tomas Plekanec 29 27 10 Patrick Sharp 30 29 11 Ryan Clowe 28 28 12 26 27 13 Marian Gaborik 28 28 14 Scott Hartnell 26 29 15 27 25 16 Ales Hemsky 25 24 17 Scottie Upshall 26 26 18 Justin Williams 26 30 19 Martin Havlat 26 27 20 Mike Cammaleri 27 26 21 27 26 22 Tomas Vanek 27 27 23 24 25 24 28 26 25 27 26 26 28 25 27 Cristian Ehroff 28 28 28 27 27 29 27 27 30 Mike Komisarek 27 26

38

The most important measures in the table are the mean age of highest average ice- time per game and the mean age of highest point production. The respective means of

26.6 and 26.4 tell us that this is the time this group of thirty elite NHL players performed their best and had their biggest value to their teams. Both numbers are lower than the expected outcome of the hypothesis of age 27, which supports the theory that long-term contracts are inefficient. Note when each individual player was at his lowest cost to the team; in terms of point production and ice time, we see several interesting findings.

When it comes to lowest salary per point (how much a team paid for each point they received from the player), Table 12 shows that, out of the thirty players examined, twenty were least inexpensive at the age of 25 and 26.

At the ages of 28, 29, and 30 only one player gave the cheapest production per point for a team. A reason for this could be that a player is still on a lower paid contract during the early years of the observation and then goes on to sign a much larger deal, for example at the age of 27, giving the next three years no chance to be the most beneficial years for the team. Even though the numbers might be somewhat skewed due to this phenomenon, we are still only looking at one single player out of thirty being most beneficial during the later years of the observation. The fact that some of the players observed are signing their new contracts during these years, and would in most cases be signing a larger contract than their previous one, the numbers should not be as lopsided as they are. The numbers from Table 12 tell us that elite NHL players are most economically beneficial to teams at the age range of 25 to 26 when it comes to point production.

39

Table 12

Age of Lowest Cap Hit Per Point and Lowest Cap Hit Per Minute of Ice-Time

Age 24 25 26 27 28 29 30

Lowest Cap 5 9 11 4 0 1 0 Hit Per Point

Lowest Cap 4 10 8 6 1 1 0 Hit Per Minute of Ice- Time Total 9 19 19 10 1 2 0

40

When it comes to lowest cap hit per minute of ice time (how much a team paid for each minute of ice time a player played on average each game), the table shows similar results to lowest salary per point. Here, as with salary per point, the problems of renewed contracts do have an effect on the outcome but not to the extent shown above. Again out of the thirty players observed, with eighteen players falling into the 25 to 26 category; this seems to be when elite NHL players are most economically beneficial to teams.

Note the last row featuring a combination of the two statistics. It becomes even clearer at what age players have the greatest value to their teams. Age 25 and 26 both have 19 observations (combined 38), nearly double that of any other category.

For a better understanding of how much teams are willing to pay for players, two extreme situations are shown below in Table 13 and Table 14. Table 13, displays the player who received the largest cap hit per point, and Table 14 shows the player who received the largest cap hit per average minute of ice-time.

These two extreme observations give a better understanding of how much a player can cost a team in terms of salary per point and salary per minute of ice time. In the case of Mike Komisarek, a new contract signed in 2010 increased the salary from $1.7 to $4.5 million, and his cap hit per point skyrocketed from $154,545 in 2009 to $1,125,000 in

2010. Out of the thirty players in the observation Komisarek was the case where a team paid the most for a player’s point production.

41

Table 13

Case of Highest Cap Hit Per Point Mike Komisarek – Summary Statistics

Year Salary in Years Average Cap Hit Points Cap Hit Age Millions Remaining Ice-Time Per Per Point in Minute Contract of Ice- Time

2008 $1.7 2 21.15 $80,387 17 $100,000 26 2009 $1.7 1 20.62 $82,457 11 $154,545 27 2010 $4.5 5 19.93 $225,752 4 $1,125,000 28

2011 $4.5 4 13.62 $330,477 10 $450,000 29 2012 $4.5 3 16.65 $270,270 5 $900,000 30

42

Table 14

Case of Highest Cap Hit Per Average Minute of Ice-Time Tomas Vanek – Summary Statistics

Year Salary in Years Average Cap Hit Points Cap Hit Age Millions Remaining Ice-Time Per Per Point in Minute Contract of Ice- Time

2008 $7.143 7 16.85 $423,916 64 $111,609 24 2009 $7.143 6 17.18 $415,693 64 $111,609 25 2010 $7.143 5 16.75 $426,447 53 $134,773 26

2011 $7.143 4 17.35 $411,700 73 $97,849 27 2012 $7.143 3 16.93 $421,830 61 $117,098 28

43

In the case of Tomas Vanek, we see a player in the observation who has a large salary per minute of ice time. With a constant cap hit of $7.143 million for seven years and an average ice time per game ranging from 16.75 minutes in 2010 to 17.35 in 2011

(considered extremely low for a player taking up that much cap space), Vanek was the player who received the most money for the average ice time per game in the sample.

By examining the tables above it becomes clear how much capital teams are willing to invest in different players. Even though these two observations are extreme in the current study, it is important to realize that the sample size only exists of 30 players born from 1981 to 1983; therefore chances that similar scenarios exist in the league are quite high. Mike Komisarek and Tomas Vanek are likely to be players a team should not sign to long-term contracts, as they take up too much cap space in comparison to what the team receives in return.

Conclusion

The emphasis of this study was to find the peak age at which elite NHL players are likely to perform their best. This thesis suggested that since NHL players are expected to peak around age 27, long-term contracts create inefficiency in the league.

Sports economists have tested the relationship between player productivity and peak performance in several different ways involving several different sports, however not in the NHL specifically, nor has the research been connected to the idea of unproductive contracts.

Several studies concerning peak performance and age helped to formulate a model that would fit the topic at hand. Since most of the studies regarding the NHL used goals, points, or assists as the dependent variable to measure player productivity,

44 changing the regression equation to average ice time per game as the dependent variable furthers the literature.

In an effort to answer the questions at hand, thirty elite NHL players were examined through statistical data over the period of five seasons. The quantitative analysis using 150 observations in total suggests that the peak age when NHL players put forth their best performance falls somewhere between the ages of 26 and 27. For the current study, the players examined had a mean age of 26.6 when they logged their highest average ice-time per game and a mean age of 26.4 when they had their highest point production, both good indicators of a player’s peak performance. To better understand what affects average ice-time per game, nine independent variables were calculated, and four proved positively significant for the model: Games played (GP), assists (A), plus/minus (+/-), and captain (CAP). To find a relation between this and inefficient contracts, capital hit was also examined against four independent variables, and two were positively significant: average ice-time per game (TOI/G) and age (AGE).

These results warranted further discussion of the issue of long-term deals in the NHL.

The average contract length, 4.7 years, suggests that the NHL is doing what the study recommends, keeping player contracts under the duration of five years. However this number is skewed due to short-term contracts that exist, as the players examined were relatively young in the first years of the observation. The maximum contract of 15 years tells a different story and suggests that several contracts exceed the proposed limit and are inefficient in the long-run, since NHL players are most valuable to their respective teams between the ages of 26 and 27.

45

Depending on the quality of the player, it may be beneficial for General Managers to follow the suggestions of the study at hand. By limiting long-term deals, a team diminishes the possibility of giving cap space to a player whose productivity does not warrant it, particularly over an extended period of time. This could open up opportunities to sign other players who can benefit the team by helping win games and championships in the future.

Limitations

Several limitations for the study must be discussed. The main limitation is the lack of data, especially data involving contracts. Only recently have contract terms and amounts become public knowledge, one reason why this thesis only observed the players over a five year span; there simply is not enough information on the contracts of several different players dating past the 2007-2008 season. Examining players over a longer period of time, including more seasons before and after age 27, would provide a better understanding about the peak performance of an NHL player.

Another limitation includes lack of variables; other recorded statistics could have a positive relationship to average ice time per game, with hits or blocked shots being two good examples. Again, these statistics have only recently started to be recorded, meaning access to the public does not exceed the last four seasons.

Future Study

Since the study of NHL peak performance and contract earnings is unique, several features of this thesis can be used for future research. Simply by looking at the limitations, we find that the study can be improved with more statistical data over a longer period of time; however this would have to done a few years in the future due to

46 the current lack of information. It would also be interesting to have a larger sample size.

The players observed in the present study were the best players in the desired age group and did not include a large number of defensemen. Perhaps looking at forwards and defensemen in two different groups would give different results. The position of goalie could also be an interesting aspect to look at; these players were omitted from this study since they have completely different statistical information to measure their performance.

Lastly, the study could be applied to some of the other major sports in .

While the NFL would not work with the current study because NFL contracts are not guaranteed (a team can cut and choose not to pay a player based on his performance), the

NBA and MLB do not have this problem and could definitely be fields where peak age and the connection with contracts could indicate inefficiency. The current study offers a good first step in this direction.

47

References

Berri, David J. “Who is Most Valuable? Measuring the Player’s Production of Wins in

the National Basketball Association.” Managerial and Decisional Economics,

Vol.20 No.8 (Dec. 1999): 411-427.

Berri, David J. and Anthony C. Krautmann. “Shirking on the Court: Testing for the

Incentive Effects of Guaranteed Pay.” Economic Inquiry 44 (2006): 536-546.

Estes, Brent C. “Slugger or Slacker: A Sabermetric Assessment of Free Agency on Major

League Baseball Player Performance,” Ph.D. diss., The Florida State University,

2006 p.5.

Fort, Rodney D. (2011). Sports Economics. Upper Saddle River, New Jersey: Pearson

Education.

Idson, Todd and Leo H. Kahane, “Team Effects on Compensation: An Application to

Salary Determination in the National Hockey League,” Discussion Paper Series

No. 743 (Sep. 1995): 1-23.

Idson, Todd and Leo H. Kahane, “Teammate Effects on Pay.” Applied Economics Letter

11, no. 12 (2004): 731-733.

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, Vol. 41, no.4 (Jul. 1988): 592-604.

Kahane, Leo H. “Production Efficiency and Discriminatory Hiring Practices in the

National Hockey League; A Stochastic Frontier Approach.” Review of Industrial

Organization, Springer 27, no 1 (2005): 47-71.

48

Lavoie, Marc, Gilles Grenier, and Serge Coulombe, “Discrimination and Performance

Differences in the National Hockey League,” Canadian Public Policy vol.13 no. 4

(Dec. 1987): 407-422.

Leeds, Michael A. and Peter von Allmen (2005). The Economics of Sports. Upper Saddle

River, New Jersey: Pearson Education.

MacDonald, Don N. and Morgan O. Reynolds. “Are Baseball Players Paid their

Marginal Products?” Managerial and Decision Economics 15 (1994): 443-57.

Maxcy, Joel. “Motivating Long-Term Employment Contracts: Risk Management in

Major League Baseball.” Managerial and Decision Economics 25 (2004):

109-120.

Mclean, Robert C. and Michael R. Veall, “Performance and Salary Differentials in

the National Hockey League,” Canadian Public Policy- McMaster

University (1987): 470-475.

Schultz, Richard and Christene Curnow. “Peak Performance and Age Among

Superathletes: Track and Field, Swimming, Baseball, Tennis, and Golf,”

Journal of Gerontology Vol.43. No. 5 (1988): 113-120.

Turner, Chad and John Hakes, “Pay, Productivity, and Aging in Major League

Baseball,” MPRA No.4326 (Nov 7. 2007): 1-35.

49