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GETTING DRAFTED INTO THE NFL: MEASURING THE EFFIECIENY OF NCAA FOOTBALL COACHES

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

Eamonn McDermott

May 2014

GETTING DRAFTED INTO THE NFL: MEASURING THE EFFIECIENY OF NCAA FOOTBALL COACHES

Eamonn McDermott

May 2014

Economics

Abstract

NCAA football currently serves as a minor league feeder system for the NFL. coaches are some of the highest, if not the highest paid officials, at their respective schools. With salaries in the millions, coaches are expected to be able to execute numerous responsibilities for their universities and players. One of the reasons why student-athletes pick certain universities is because of the coaching and development they will receive. But do coaches have any impact on getting their players drafted into the NFL? By looking at data from 2003-2013 almost 30,000 student-athletes were measured by 232 coaches. The results of this study show that coaches do in fact have positive, negative and no effect on getting their student-athletes drafted into the NFL.

KEYWORDS: (College Football, NFL Draft, Coaches)

Dedication

I would like to dedicate this paper to my parents Kevin McDermott and

Marybeth Laskey without their guidance and love throughout out my life I would

not be in the position I am today.

ON MY HONOR, I HAVE NEITHER GIVEN NOR RECEIVED UNAUTHORIZED AID ON THIS THESIS

Signature

TABLE OF CONTENTS

ABSTRACT

DEDICATION/ACKNOWLEDGEMENTS 1 INTRODUCTION 1 1.2 Motivation…………………………………………………… 2

2 LITERATURE REVIEW....……………………………………… 6

3 SUMMARY STATISTICS……………………………………..... 13 4 RESULTS.………………………………………………………… 24

5 CONCLUSIUON………………………………………………… 34

APPENDIX 35 References 37

LIST OF TABLES

1.1 Top 10 School Profits for Football in 2011-2012…………………… 3 1.2 Top 20 Highest Paid NCAA Coaches in 2013……………………... 4

3.1 Breakdown of Rivals 3,4, and 5-Star Recruits by Position ………… 14 3.2 Breakdown of Rival 5-Star Recruits by Conference………………... 15

3.3 Breakdown of Rival 5-Star Recruits by Position and Conference…. 17 3.4 Coaches with more then 10 5-Star Recruits ………………………... 18 3.5 Coaches with more then 100 4-Star Recruits……………………….. 19 3.6 Coaches with more then 20 Players Drafted into the NFL…………. 21-22

3.7 Coaches with 10-20 Players Drafted into the NFL ………………… 23

4.1 Fixed Effects Table of Coaches Significant at 85% Level…………. 26-27 4.2 Fixed Effects Table of Miscellaneous Variables at 85% Level… 30

LIST OF APPENDICIES

1 Names of all 232 Coaches Measured 35-36

Acknowledgment

I would first like to start off by thanking my family for always being there

for me in all circumstances of my life. I would not be the student or person I am

without them. I would next like to thank all of the Professors I have had the

privilege of learning under at Colorado College. I also would like to thank Kevin

Rask for giving me the opportunity to work with and learn from. I would like to

next thank Dean Edmonds for the relationship we have built over the last four

years at Colorado College. I would lastly like to thank all of my teammates,

coaches, friends, and Emma Volk for helping me along the way at Colorado

College.

Chapter 1

Introduction

The National Collegiate Athletic Association (NCAA) was established in

1906, urged by former President of the Theodore Roosevelt. The

purpose of the NCAA was to safeguard young adults from unsafe and oppressive

behavior in that time period. In 1906, there were only 62 members in the

intercollegiate athletic association of the United States (original name of NCAA).

Today, there are over 1,096 schools and universities competing in the NCAA and

over 450,000 athletes (NCAA, 2014). While almost all of these student-athletes

will be going pro in something other than sports, a select few will have the

opportunity to play professional sports for a living. But what gives these select

few the opportunity to play sports for a living? Some people argue that they have

superior ability and are destined from the beginning to play professional sports.

Others argue that it is the result of the environment in which they were raised and

were taught the skills and qualities necessary to become professional athletes.

Finally, some argue that it is the university or school that the athlete attends that

makes him a professional athlete because of the experience and coaching that they

receive. While considering the latter, most universities and schools make their

men’s football coaches their highest paid officials. What is the actual value of

these coaches in developing their players into professional athletes?

1

Most successful businesses have one thing in common: a strong person or

chief executive at the top. According to Maxcy (2013) “the structure of college

football much resembles the hierarchical organizational chart of the midsize

corporation” (p.370). Whether you like it or not, the NCAA is one of the biggest

businesses in the world with TV contracts in the billions. According to Henry

Mintzberg, the world-renowned business and strategy guru, successful chief

executives have three things in common. The first is the different roles that

executives or coaches must take on. In coaching this could be considered

assembling a staff, recruiting, setting lineups and ultimately coaching. The

second role is interpreting and analyzing information. A coach analyzes

information when he looks at video of the opponent and his own team, statistics,

or new potential recruits. The last role or duty that an executive or coach fulfills

is the interpretation of data in order to make the business or team run more

efficiently (Kahn, 2000). A coach undertakes this responsibility by taking in all

the data and making decisions that puts his team in the best opportunity to

succeed. Ultimately, executives and coaches are responsible for molding and

motivating individual people to embrace being part of a team in which they can

accomplish far more then they could have individually.

Motivation

As previously stated, the money that is being poured into division one

athletics is astounding. One of the reasons why these universities and colleges are

investing so much into football is because they are the beneficiaries of these

exuberant revenues. In the 2012-2013 year, the NCAA generated 11 billion

2

dollars worth of revenue across all of the sports (Berkowitz & Peter, 2014). The

top ten football universities in 2011-2012 alone produced almost 500 million

dollars amongst themselves (Dosh, 2012) (See Table 1.1). Most of these

universities and colleges are investing seven figure contracts not only to their

head football coach but also to the whole staff (Berkowtiz, Dougherty, Schnaars

& Upton, 2013) (See Table 1.2). But what is the actual value of these coaches to

their universities? And how is the best way to measure this value?

Table 1.1

Top 10 School Profits for Football in 2011-2012 (Value in Millions)

School Revenue Expenditure Profit

Texas $103.80 $25.90 $77.90 $85.20 $23.60 $61.60 Georgia $75.00 $22.70 $52.30 $74.10 $23.10 $51.10 Alabama $82.00 $36.90 $45.10 LSU $68.80 $24.10 $44.80 Auburn $77.20 $33.30 $43.80 Notre Dame $69.00 $25.80 $43.20 Arkansas $64.20 $24.30 $39.90 Nebraska $55.10 $18.70 $36.40 Source: Dosh, K (2011-2012) ESPN.go.com

3

Table 1.2

Top 20 Highest Paid Coaches in NCAA going into the 2013 Season

RK School Conf Coach School Pay Other Max Staff Pay Pay Bonus Total

1 AL SEC Saban 5,395,852 150,000 700,000 4,462,700 2 TX Big 12 Brown 5,392,500 61,250 850,000 4,111,000 3 AR SEC Bielema 5,158,863 - 700,000 3,233,000 4 TN SEC Jones 4,860,000 0 1,000,000 3,170,000 5 OK Big 12 Stoops 4,741,667 31,500 819,500 3,436,000 6 OH State Big 10 Meyer 4,608,000 0 550,000 3,474,504 7 LSU SEC Miles 4,300,000 159,363 700,000 4,565,803 8 MI Big 10 Hoke 4,154,000 0 550,000 3,072,000 9 IA Big 10 Ferentz 3,985,000 0 1,750,000 2,367,500 10 L’Ville AAC Strong 3,700,000 38,500 808,333 2,703,900 11 OSU Big 12 Gundy 3,450,000 - 550,000 2,884,000 12 SC SEC Spurrier 3,300,000 22,500 1,550,000 2,744,600 13 GA SEC Richt 3,200,000 114,000 1,000,000 3,294,000 14 PA Big 10 O'Brien 3,282,779 - 200,000 - 15 UC AAC Tuberville 3,143,000 0 465,000 1,920,000 16 TCU Big 12 Patterson 3,120,760 - - -

17 TX A&M SEC Sumlin 3,100,000 300 750,000 3,392,250 18 NE Big 10 Pelini 2,975,000 - 1,000,000 2,648,500 19 KS State Big 12 Snyder 2,800,000 3,000 580,000 2,594,750 20 MO SEC Pinkel 2,800,000 200 850,000 2,642,500 Source: Berkowitz, S., Dougherty, S., Schnaars, C., & Upton, J (2013)

USAToday.com

Most would argue that it is all about winning and keeping your program

relevant in the national spotlight. But for most student-athletes, “just winning” is

not enough. These student-athletes also find that the free education they receive

in return is not sufficient either. A good of student-athletes have

aspirations of playing in the (NFL). One of the main

decisions behind choosing a university is determining where the best place to

develop as a player is. Many student-athletes and all NFL teams look at NCAA

4

football as a minor league system for the NFL. During their collegiate career it is

crucial for student-athletes to do whatever they can to best position themselves for

the NFL draft. One of the ways to do this is by picking a coach who will develop

you into the best player possible. The current state of coaching in the Football

Bowl Subdivision (FBS) programs begs the question, which coaches beyond

winning games and championship, are the best at getting their players drafted

higher for the next phase of their football career in the NFL? Through my model

I will try to determine which coaches best answer this question.

5

Chapter 2

Literature Review

Considering all the money that goes into not only NCAA sports but also

professional sports, many people have tried to figure out the efficiency of sports.

Simon Rottenberg (1956) opened the doors for this research when he used a

production function to the look at the labor markets of Major League Baseball.

According to Rottenberg (1956):

A baseball team, like any other firm produces its product by

combining factors of production. Consider the two teams engaged

in a contest to be collapsed into a single firm, producing as output

games, weighted by the revenue derived from admission fees. Let

the players of one team be one factor and all others (management,

transportation, ballparks and the payers of the other team), another.

The quantity of the factor—players—is measured by making the

appropriate adjustment for differential qualities among players.

(p.255)

Rottenberg’s production function resembled

P*Q=f (T, X).

6

(2.1)

Where,

P is the mean ticket price, Q is the size of the audience, T is the total available playing talent on a team X incorporates all other input factors such as management, the stadium, transport, and the quality of players on the other team.

Gerald Scully (1974) was the first to study team performance using a

production function. Scully differed from Rottenberg by designating team

performance as his output and individual player performance as his input. Since

these studies, numerous people have referenced the work of Rottenberg and

Scully to figure out the efficiency of coaches in sports using production functions

(Zak & Huang & Siegfried, 1979; Kahn, 1993; Fizel & D’Itri, 1997; Dawson &

Dobson & Gerrard, 2000; Lee & Berri, 2008; Maxcy, 2013).

Many economists have tried to determine the efficiency of collegiate and

professional coaches by looking at a wide variety of sports. On the professional

side, Kahn (1993) tried to establish the impact that a Major League Baseball

(MLB) manager had not only on his team performance but also on his individual

player performance by using data from 1969-1987. Dawson, Dobson, and

Gerrard (2000) tried to determine the coaching efficiency of English Association

Football by looking at 147 coaches from 1992-1998. In contrast, on the collegiate

side, Fizel and D’Itri (1997) attempted to determine the efficiency of NCAA

division one men’s coaches by looking at 147 universities from 1984-

1991. Furthermore, Maxcy (2013) strived to figure out the efficiency of NCAA

division one men’s football coaches through looking at 120 universities from

7

2002-2011. All of the researchers above defined their outputs as either wins or

winning percentage and all of their inputs dealt with differing measures of player

quality.

In all sports it is difficult to measure a player’s true value. Even though

numbers do not lie, they do not always tell the whole story. Kahn (1993) defined

his input of player quality as a predicted salary determined by his career up to that

point. Fizel and D’Itri (1997) referenced the basketball index Hoop Scoop that

rated every NCAA athlete on a value from 1-10. The sum of the player ratings

for the top 10 players on the team supplied the team with an overall rating.

Dawson, Dobson, and Gerrard (2000) identified playing talent as the predicted

transfer fee of each player, which is defined as the price that a club is prepared to

pay a player’s current club to acquire the player’s registration provided that player

is not a free agent. Differing from the previous studies, Lee and Berri (2008)

developed a way to measure guards, small forwards, and big men. In other words,

each position is not valued the same way. Maxcy (2013) defined talent by using a

weighted ranking of the previous four recruiting classes provided by Rivals

scouting website.

After defining their talent, all of the economists had to determine the best

method to analyze their data. Scully, Kahn (1993), Dawson, Dobson, and Gerrard

(2000) and Lee and Berri (2008) all used the same model but differing approaches

to determine efficiency. Kahn (1993) applied an Ordinary Least Squares (OLS)

model and determined that higher quality managers produce more wins than

lower quality managers. Furthermore, Kahn found that higher quality managers

8

raise individual playing performance. According to Dawson, Dobson, and

Gerrard, the Scully OLS method approach has a couple of limitations when trying

to determine efficiency. The OLS approach has trouble producing any

approximations of the complete range of technical inadequacy of individual teams

(Dawson, Dobson, & Gerrard, 2000). In addition, the Scully approach focuses

primarily on teams and not the coaches. As a result, Dawson, Dobson, and

Gerrard and Lee and Berri applied a different OLS where they accounted for more

dummy variables and time invariant effects. Fizel and D’Itri (1997) and Dawson,

Dobson, and Gerrard found that in order for a coach to retain their job they must

win and that the most efficient coaches do not always win and ultimately get

fired.

In addition, there has been research analyzing professional sports league

drafts. The objective of a draft is to try to evenly disperse playing talent to

professional teams around their respected leagues. The order of the draft is

determined by a reverse-order determined by last year’s standings. The National

Hockey League (NHL) and the National Basketball Association (NBA) differ

from the National Football League (NFL) and Major League Baseball (MLB)

because they employ a lottery system for the bottom teams, which encourages

teams not to lose on purpose to receive the first pick.

Each year thousands of players are drafted into professional sports.

Although being drafted into professional sports does not guarantee that one will

play professionally, it greatly increases their chances. Spurr (2000) looked at the

position of players taken in the MLB draft from 1966-1968 and the year 1983 and

9

which players ultimately played in the MLB. Berri, Brook, and Fenn (2011) tried

to find the determinants of why players were drafted into the NBA by looking at

draft data from 1995-2009. In addition, Groothuis, Hill, and Perri (2007)

attempted to determine what influenced the early entry of the NBA draft.

Spurr (2000) and Groothuis, Hill, and Perri (2007) both used a probit

model to analyze their data. Spurr found that one’s ability to reach the MLB was

increased, holding constant his draft spot and extra variables, if he played in

college and if his college was elite. Spurr defined elite as a college that played or

lost in the college World Series during the time period studied. Groothuis, Hill,

and Perri discovered that the structure of the Collective Bargaining Agreement

(CBA) added incentives for both the players and teams in deciding to enter the

draft. Furthermore, they found that firms and players are trying to lengthen their

careers by entering early. In addition, they found that players who enter early

compared to 4-year college players, play fewer minutes in the first year but

overall they improve more quickly. Berri, Brook, and Fenn (2011) used three

different methods and models to analyze their data. They started with an OLS

model but because of the dependent variable (place taken in the draft), they

experimented with a Poisson model, which was not much of an improvement.

Lastly, they attempted to use a Negative-Binomial model because of the problem

of over dispersion. Ultimately, they found if a player wants to be drafted at a

higher rank, they must be a premier scorer.

One of the main purposes of the NCAA is to be a feeder or minor league

system for the professional teams. In many cases it is cheaper for the professional

10

teams to “employ” universities and schools to develop their labor for them. Each

year less than one percent of NCAA athletes will be drafted into sports and have

the opportunity to play professionally at the top level. Many researches have

attempted to figure out the overall efficiency of not only team sports but also the

CEO’s or coaches of those sports. All of the studies do not unanimously agree on

every aspect. They do find that the coaches who win stay and the coaches who do

not have to look for new work. In addition, research has been conducted to find

out why certain players are drafted and ultimately play professionally. Similar to

the research of the efficiency of sports, there are not any universal answers.

Further research should attempt to answer which coaches are most efficient in

developing their NCAA division one-football student-athletes into NFL draft

picks.

The NCAA coaching business has never been so prosperous with not only

head coaches making millions but also some of the top assistant coaches.

Universities justify these exuberant contacts by saying that the revenue these

programs bring in far exceeds the coach’s salary expenses. The Chancellor at the

University of Alabama, Dr. Robert Witt said that the head football coach, “Nick

Saban’s is the best financial investment the University has ever made” (Litman,

2013). The University’s revenue has increased over 112% in the last four years

(Mihailovich & Keteyian, 2013). Aside from the revenue aspect, are coaches like

Nick Saban really worth the seven figure contracts? Besides raising money for

the universities, are they giving their players the best opportunity to fulfill their

ultimate dream of playing professional sports? Using similar methods by the

11

people previously stated, this paper will attempt to find out which coaches prepare

their recruits best for professional sports. Lastly, this paper will take into account

what the student-athletes are ranked going into college and at the of their

collegiate career which coaches best develop their players for the NFL draft.

12

Chapter 3

Summary Statistics

The data that was evaluated covered 104 schools in the Football Bowl

Subdivision (FBS), looking at 232 coaches, over a 10-year period from 2003-

2013 (See Appendix 1). The total number of student-athletes that were measured

was 29,580.

These student-athletes were all measured on a scale between 1-Star being

the lowest and 5-Star being the highest. There were 6, 1-Star players, 1,384, 2-

Star players, 18,690, 3-Star players, 8,620, 4-Star players, and 880, 5-Star players

in the 10-year period (See Table 3.1). Some notable 5-Star recruits include

Heisman trophy winners such as and . There were also

two number one overall draft picks, Mathew Stafford and who were

5-Star recruits (Huguenin, 2014). Arguably the best in the NFL,

Calvin Johnson, was a 4-Star recruit. In addition, Darrell Revis is considered by

many to be the best defensive back in the NFL, but was only a 3-Star recruit. One

notable 2-Star recruit is the defending champion Russell

Wilson (Weathersby, 2011). Furthermore, for what it is worth, in the past Super

Bowl XLVIII the and Seahawks had more 2-Star recruits

then 4 or 5-Star on their respective rosters (Duffy, 2014).

13

Table 3.1

Breakdown of Rival 3, 4, and 5-Star Recruits by Position during 2003-2013

Position Rival 5-Star Rival 4-Star Rival 3-Star

AT 0 11 0 DB 105 1,306 2867 DE 105 976 2055 DT 87 832 1272 K 0 12 341 LB 125 1,074 2681 OL 133 1,379 3361 QB 82 606 1422 RB 109 1,001 1946 TE 14 405 1108 WR 120 1,029 1625 Total 880 8631 18689 Source: (2003-2013) Rivals Recruiting

Additionally, for the period tested, there were no 5-Star athletes or kickers.

The most 5-Star positions recruited during this time period were offensive

lineman with 133. This makes sense because there are 5 possible positions on the

offensive line. The least amount of 5-Star positions recruited were tight ends with

only 14. The most 4-Star positions recruited during this time period were

defensive backs with 1,306. The least amount of 4-Star positions, besides kickers

and tight ends, were with 606. The largest amount of 3-Star recruits

were offensive lineman with 3,361 and the least amount, beside kickers and

athletes, were tight ends with 1108 (See Table 3.1).

There were 10 major football conferences covered in the data. These

conferences included the American Athletic Conference (AAC), the Atlantic

14

Coastal Conference (ACC), the Big 12, the Big 10, Conference USA, the Mid-

American Conference (MAC), the (MWC), Pacific-

12 (Pac-12), the (SEC), and the .

The three highest conferences for recruits were the SEC with 7,008, the

ACC with 6,046 and the Pac-12 with 4,903. The conference with the most 5-Star

recruits was the SEC with 286. The MAC, MWC and Sun Belt conferences all

had zero 5-Star recruits.

Table 3.2

Breakdown of Rival 5-Star Recruits by Conference during 2003-2013

Conference

American Athletic Conference 4 ACC 182 Big 12 109 Big 10 140 Conference USA 4 MAC 0 Mountain West 0 Pac-12 155 SEC 286 Sun Belt 0 Total 880 Source: (2003-2013) Rivals Recruiting

15

From this Star Ranked scale, the players were also divided into 11

positional categories based on the position they were going to play in college.

These positions were offensive lineman (OL), quarterback (QB),

(RB), wide receiver (WR), (TE), athlete (AT), defensive end (DE),

defensive tackle (DT), (LB), defensive back (DB), and kicker (K).

Furthermore, based on their position, they were then ranked amongst their fellow

positional peers for each recruiting year. While the recruiting has changed each

year, in the 10-year span OL were the highest recruited players on offense and DB

were the highest recruited players on defense.

The most 5-Star QBs, DBs, DEs, DTs, OLs, RBs and WRs recruits

decided to play in the SEC (See Table 3.3). The most 5-Star recruits for a given

year was 2005 with 98. There were only 20 coaches in this study who had more

than 10, 5-Star recruits during 2003-2013 (See Table 3.4). Some of these coaches

included former Florida State coach and

coach . Furthermore, there were only 15 coaches who had between 5

and 10, 5-Star recruits. Some of these coaches include Teach coach

Frank Beamer and Florida coach . There were 24 coaches who

had more then 100, 4-Star recruits (See Table 3.5). These coaches include former

University of Southern coach and State coach and

former Florida coach .

16

Table 3.3

Breakdown of Position and Conference of 5-Star Recruits during 2003-2013

Conferences AAC ACC BIG12 BIG C- MAC MT- PAC- SEC Sun- 10 USA WEST 12 Belt Position AT 0 0 0 0 0 0 0 0 0 0 DB 0 24 7 24 0 0 0 7 43 0 DE 0 20 19 15 0 0 0 19 32 0 DT 4 20 15 7 0 0 0 3 38 0 K 0 0 0 0 0 0 0 0 0 0 LB 0 36 19 26 0 0 0 19 25 0 OL 0 19 15 32 0 0 0 26 41 0 QB 0 15 11 15 0 0 0 15 26 0 RB 0 20 13 14 0 0 0 29 33 0 TE 0 4 4 0 0 0 0 3 3 0 WR 0 24 6 7 4 0 0 34 45 0 Total 4 182 109 140 4 0 0 155 286 0 Source: (2003-2013) Rivals Recruiting

17

Table 3.4

Coaches with more than 10, 5-Star Recruits during 2003-2013

Coaches School 5-Star Recruits

Bob Stoops Oklahoma 44 Bobby Bowden Florida State 44 Clemson 17 Auburn/ State 11 California 10 Ohio State 43 Florida State 17 Penn State 16 Southern California/ Tennessee 19 Miami (FL) 28 Louisiana State/Oklahoma State 39 Michigan 29 65 Georgia 32 Nick Saban Alabama/Louisiana State 42 Pete Carroll Southern California 84 Miami (FL) 12 /Florida 27 South Carolina 11 Urban Meyer Ohio State/Florida 61 Source: (2003-2013) Rivals Recruiting

18

Table 3.5

Coaches with more than 100, 4-Star Recruits during 2003-2013

Coaches School 4-Star Recruits

Al Groh Virginia 102 Oklahoma 379 Bobby Bowden Florida State 271 Dabo Swinney Clemson 117 143 Gene Chizik Auburn 108 Ole Mississippi/Arkansas 101 Jeff Tedford California 195 Jim Tressel Ohio State 296 Joe Paterno Penn State 210 Larry Coker Miami (FL) 162 Les Miles Louisiana State/Oklahoma 311 State Lloyd Carr Michigan 192 Mack Brown Texas 425 Mark Richt Georgia 375 Nick Saban Alabama/Louisiana State 321 Pete Carroll Southern California 277 Randy Shannon Miami (FL) 121 Arizona/Michigan 117 UCLA/Washington 126 Ron Zook Illinois/Florida 144 Steve Spurrier University of South Carolina 135 Texas Tech/Auburn 206 Urban Meyer Ohio State/Florida 267 Source: (2003-2013) Rivals Recruiting

19

There were 48 coaches who had more then 20 players drafted into the

NFL (See Table 3.6). Some of these coaches included former coach

Barry Alvarez and current Oklahoma University coach Bob Stoops. In addition,

there were 18 coaches who had 10-20 players drafted into the NFL in this period

(See Table 3.7). A few of these coaches include coach Dabo

Swinney, Duke coach , and Texas Christian University coach

Gary Patterson. Some notable coaches who have less then 10 players drafted into

the NFL are State University coach and former Louisville

coach and current Texas .

20

Table 3.6

Coaches with more than 20 Players Drafted during 2003-2013

Coaches School Players Drafted

Al Groh Virginia 39 Wisconsin 22 Bill Callahan Nebraska 31 Bob Stoops Oklahoma 117 Bobby Bowden Florida State 67 Arkansas/Louisville 32 Wisconsin 28 University of North Carolina 25 25 North Carolina State 21 24 Dennis Texas A&M 28 Franchione Arizona State 23 Ole Mississippi 20 Frank Beamer Virginia Tech 47 Missouri 25 Rutgers 27 Houston Nutt Ole Arkansas/Arkansas 32 Jeff Tedford California 61 Wake Forest 23 Stanford 20 Jim Tressel Ohio State 125 Joe Paterno Penn State 78 Purdue 28 John Bunting North Carolina 27 UCLA 28 Iowa 47 Larry Coker Miami (FL) 47 Les Miles Louisiana State/Oklahoma State 70 Lloyd Carr Michigan 81 Mack Brown Texas 103 Mark Richt Georgia 128 Oregon 32 Oklahoma State 34 Mike Leach Texas Tech 22 Alabama 25 Arizona 34 Nick Saban Alabama/Louisiana State 74 Pete Carroll Southern California 168

21

Table 3.6 Continued

Coaches School Players Drafted Maryland 41 Randy Shannon Miami (FL) 47 Ron Zook Illinois/Florida 74 Steve Spurrier University South Carolina 42 Tom O'Brien 22 Clemson 46 Tommy Texas Tech/Auburn 47 Tuberville Urban Meyer Ohio State/Florida 63 Stanford 24 Source: (2003-2013) Rivals Recruiting

22

Table 3.7

Coaches with 10-20 players Drafted in NFL during 2003-2013

Coaches School Players Drafted 10-20

Bo Pelini Nebraska 16 Stanford 10 Dabo Swinney Clemson 17 David Cutcliffe Duke 14 Arizona State 14 Colorado 14 Texas Christian 16 Baylor 13 John Smith Michigan State 13 South Carolina 16 Michigan State 19 Kansas 14 Oregon State 16 Fresno State 14 18 Rich Rodriguez Michigan/Arizona 14 Rick Neuheisel UCLA/Washington 13 Mississippi State 18 Washington 18 Source: (2003-2013) Rivals Recruiting

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Chapter 4

Results

There has been an abundance of research that has attempted to figure out

the efficiency of athletics. Ultimately, few have tried to determine the overall

efficiency of head coaches. This section will show the results and best model

chosen to help explain which coaches do the best, average, and worst job of

getting their players drafted into the NFL.

In the regression, the dependent variable was collegiate players drafted

into the NFL. This was measured by multiple independent variables. These

variables included individual measures such as a players rank going into college

whether it is a Rivals, 5-Star, 4-Star, or 3-Star. The student-athlete’s height and

weight was also taken into consideration. The collegiate expenditure per player

was measured as well. Furthermore, the regression also included team statistics

like RPI and wins. The next set of independent variables, and the most important,

were all 232 coaches and their fixed effect on their players getting drafted into the

NFL. In addition, 102 schools and universities were also included. The last thing

that was measured was the individual player ranks of their respected positions

going into college (See Equation 4.1).

24

DraftPick = Rival5,Rival4,Rival3,Height,Weight,Wins ExpenditurePerPlayer,RPI,HC1− HC232,SID1− SID102,

ATRank,DBRank,DERank,DTRank,KRank,LBRank,OLRank QBRank,RBRank,TERank,WRRank,Cons

(4.1)

€ After looking at all 232 coaches who were measured in this time period,

only 50 coaches with players drafted were significant at the 85 percent level (See

Table 4.1). One reason that a lot of coaches were not significant is because they

simply did not have enough observations. A second reason is that they did not

have any players get drafted into the NFL. The final reason that they were not

significant was because compared to their fellow coaching peers; they did not

help nor hurt their player’s ability to get drafted into the NFL.

25

Table 4.1

Fixed Effect Table of Coaches Significant at 85% Level during 2003-2013

Name School Coef. Std. T P>t Err.

Gary Nord Texas at El Paso -58.04 3.86 -15.05 0.00 Duke -50.90 15.89 -3.20 0.00 Ed Orgeron Ole Miss -50.32 15.26 -3.30 0.00 Duke -48.24 15.51 -3.11 0.00 Pete Carroll Southern California -47.98 8.05 -5.96 0.00 Jim Tressell Ohio State -47.11 6.85 -6.88 0.00 Arkansas -39.06 15.85 -2.46 0.01 Morris Watts Michigan State -37.27 20.07 -1.86 0.06 Bobby Petrino Louisville/Arkansas -36.30 12.78 -2.84 0.01 John Smith Arkansas/Louisville/Michigan -34.08 16.42 -2.08 0.04 State Mark Dantonio Michigan State -33.77 17.23 -1.96 0.05 Jim Harbaugh Stanford -33.15 11.57 -2.87 0.00 Houston Nutt Ole Miss/Arkansas -32.98 13.93 -2.37 0.02 Walt Harris Stanford/Pittsburgh -27.10 12.75 -2.12 0.03 Bob Stoops Oklahoma -26.64 5.24 -5.08 0.00 Mark Richt Georgia -23.47 4.89 -4.80 0.00 Jeff Tedford California -22.71 5.26 -4.31 0.00 Joe Paterno Penn State -21.94 5.23 -4.20 0.00 Mack Brown Texas -21.76 5.22 -4.17 0.00 Kirk Ferentz Iowa -20.94 5.21 -4.02 0.00 Buddy Teevens Stanford -20.90 11.08 -1.89 0.06 Mississippi State -19.53 7.59 -2.57 0.01 Al Groh Virginia -19.06 4.10 -4.65 0.00 Jim Grobe Wake Forest -18.17 5.33 -3.41 0.00 Dirk Koetter Arizona State -18.11 11.58 -1.56 0.12 Ron Zook Florida/Illinois -17.01 5.72 -2.98 0.00 Chan Gailey Georgia Tech -16.69 7.48 -2.23 0.03 Miami (FL)/Temple -16.57 5.27 -3.14 0.00 Mike Leach Texas Tech -15.54 6.51 -2.39 0.02 Frank Beamer Virginia Tech -14.72 4.86 -3.03 0.00 Greg Schiano Rutgers -12.48 5.02 -2.48 0.01 Sylvester Mississippi State -11.96 5.10 -2.35 0.02 Croom Bobby Bowden Florida State -11.49 5.09 -2.26 0.02 Randy Shannon Miami (FL) -10.35 6.20 -1.67 0.10 Gary Patterson Texas Christian -9.21 4.78 -1.93 0.05

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Tommy Clemson -8.97 5.99 -1.50 0.13 Bowden Table 4.1 Continued Name School Coef. Std. T P>t Err. Gary Pinkel Missouri -8.90 4.47 -1.99 0.05 Ralph Friedgen Maryland -8.61 4.77 -1.80 0.07 Colorado 9.62 6.42 1.50 0.13 Rick Neuhisal UCLA/Washington 10.47 4.67 2.24 0.03 Danny Hope Purdue 11.76 6.81 1.73 0.08 Nick Saban Louisiana State/Alabama 12.41 8.22 1.51 0.13 Barry Alvarez Wisconsin 13.82 7.15 1.93 0.05 Rich Rodriguez Arizona/Michigan 14.89 4.95 3.01 0.00 Mike Shula Alabama 19.11 9.95 1.92 0.06 Les Miles Oklahoma State/Louisiana 20.76 3.80 5.46 0.00 State Gerry DiNardo 27.01 18.75 1.44 0.15 Indiana 27.11 18.72 1.45 0.15 Dennis Texas A&M/Texas 37.85 10.17 3.72 0.00 Franchione State/Alabama R.C. Slocum Texas A&M 41.21 14.04 2.93 0.00 Texas A&M 42.18 11.08 3.81 0.00 Source: Author’s Calculation

Of the 50 coaches who were significant at the 85 percent level, 37 of them

had a negative effect on their players getting drafted. A coach would want to

have a negative effect because this means that, holding all other things constant,

he would get his players drafted X spots better than the no effect average coach.

Moreover, of these 37 coaches who had a negative effect, 26 of them had more

than 20 players drafted into the NFL during this time period (2003-2013). Some

of these coaches include former national championship coach at USC and current

Super Bowl champion winning coach at the Pete Carroll.

Carroll, on average, would get his players drafted almost 48 spots higher than the

no effect average coach. In addition, this list of 37 coaches also includes four

other collegiate coaches who have won a national championship. They include

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former Ohio State coach Jim Tressel, current Oklahoma coach Bob Stoops,

former late iconic Penn State coach Joe Paterno, and recently resigned Texas

coach Mack Brown. Another notable coach on this list includes former Stanford

coach and current coach Jim Harbaugh. Ultimately the

former Ole Miss and interim coach at USC Ed Orgeron was one of the best

coaches by having his player’s drafted 50 spots higher than the average no effect

coach.

There were also 11 other coaches who had negative effects but not as

many players drafted as the previous group. Some of the coaches include

Michigan State coach Mark Dantonio and former Mississippi State coach

Sylvester Croom. Simply put, having more than 20 players drafted into the NFL

during this period does not guarantee that you will have a negative effect on your

student-athletes. Of the 47 coaches who had more than 20 players drafted into the

NFL, 21 of these coaches did not have negative effect on their players. Some of

these coaches include four-time NCAA national championship winner and current

Alabama coach Nick Saban who has had more than 74 players drafted into the

NFL during this time period. Another notable coach who has no effect is two-

time NCAA national champion winner at Florida and now current Ohio State

coach Urban Meyer who has 63 players drafted into the NFL. One of the reasons

for Meyer could be that the spread offense that he is notorious for has not always

translated well to the NFL.

A different way to interpret this data is to look at the salaries of these

coaches, who for the most part are the highest paid officials at their respected

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schools (See Table 1.2). According to USA Today, of the 20 highest paid

coaches entering the 2013 season only five coaches are significant at the 85

percent level and have a negative effect on their players getting drafted into the

NFL. This is not to mention that one of these coaches Mack Brown was forced to

resign from Texas after the 2013 season. Of the 37 coaches who are significant

and have a negative effect, 24 are currently either not head coaches in College

Football or make less then the 20 highest paid coaches in the NCAA. Some

coaches that are unemployed were forced to resign because of scandals, like

former Ohio State coach Jim Tressel and the “free tattoo” saga. Other coaches

like Greg Schiano or Chan Gailey try their luck coaching in the NFL. Lastly,

some coaches like Jim Grobe, Ron Zook or Ed Orgeron may develop their players

better than their peers but they ultimately do not have enough Ws in the win

column.

Consequently, there were also 13 coaches of the 50 significant coaches

who had a positive effect. This means that holding all other things constant, these

coaches compared to the average no effect coach will have their players drafted X

amount spots worse. The most notable coach is Alabama coach Nick Saban who

on average gets his players drafted 13 times worse. Furthermore, former West

Virginia, Michigan and current Arizona coach Rich Rodriguez has a positive 15

effect. Also national championship winner and coach of LSU Les Miles gets his

players drafted almost 21 spots worse than the average no effect coach. Former

Green Bay Packers head coach and former Texas A&M coach Mike Sherman was

also found to have a positive effect.

29

A different way to interpret this data is to look at the individual player

measurements that the coaches do not have an effect on. For instance, if you were

a rival 5-Star recruit on average you were chosen almost 62 spots higher than a

rival 2-Star recruit. When it comes to contracts in the NFL that is the difference

between millions of dollars. Furthermore, if you were a 4-Star recruit you were

chosen almost 15 spots higher and a 3-Star was chosen five spots higher than 2-

Star rivals recruit. Moreover, for every pound that the student-athlete weighed

more than his fellow positional peers this led to a half pick better. Contrasting

from this, height had a positive effect of 4 draft slots worse (See Table 4.2).

Table 4.2

Miscellaneous Significant Variables at 85% Level during 2003-2013

Variable Coef. Std. Err. T P>t

Rival5 -61.65 4.50 -13.70 0.00 Rival4 -14.61 1.75 -8.35 0.00 Rival3 -4.80 1.30 -3.68 0.00 QB Rank -0.60 0.07 -8.57 0.00 Weight -0.52 0.02 -28.22 0.00 K Rank -0.38 0.10 -3.88 0.00 WR Rank -0.28 0.03 -9.94 0.00 TE Rank -0.12 0.05 -2.42 0.02 RPI 0.05 0.03 1.71 0.09 OL Rank 0.47 0.03 15.71 0.00 Height 4.14 0.23 18.13 0.00 Source: Author’s Calculation

Taking a step back, it is remarkable to see that only a little more than 20

percent of coaches had any effect on their players getting drafted. Even more

fascinating is that less than 16 percent of coaches had a negative effect on their

players getting drafted. On the other hand, almost five percent of coaches have a

positive effect on their players. But two of these coaches in the ladder include

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Les Miles and Nick Saban who have combined for half the national

championships during this time period measured.

Coaches must walk a fine line when deciding how to manage their time

and efforts into coaching their teams. Does a coach focus on individual growth or

focus more on a team philosophy? As previously stated by Mintzberg, a CEO

much like a coach must be able to balance those three unique roles in order to be

successful. At the end of the day a coach is ultimately responsible for winning

games and keeping his program relevant in the national spectrum. When a

program is relevant in the public eye this leads to exposure for the university on

the national level and hopefully ultimately more sources of revenue for the

university.

On the other hand, one way to gain national media attention for a

university is to have a player in the NFL that people can relate to. This also leads

to coaches being able to recruit better players in the future because they can sell

future recruits on the players they have developed in previous years. When you

give coaches better players this usually leads to not only better teams but also

winning teams. But coaches must be careful not to follow in the steps of Ed

Orgeron because while he was one of the best at getting his players drafted, his

tenure at Ole Miss did not last long.

The first way to evaluate NFL draft picks should be by looking through

the perspective of the individual player. Ultimately, being drafted higher into the

NFL does not guarantee that one will play better or longer then any other players

drafted later. Indeed, too much great surprise, most people do not know that the

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average NFL career is only around 3.2 years (Bennett, 2011). These special

athletes only have a very small window to make as much money as possible, so it

is critical to make the most money while they can. One way to do this is through

their rookie contracts. For many players this will be their only contract they sign.

Due to the new Collective Bargaining Agreement (CBA) in 2011 most rookies

have very little leeway when negotiating their contracts. As a result of the new

CBA, being drafted as high as possible is crucial. The whole draft class is refined

to only make a certain amount of money. In 2011, the rookie draft class was

restricted to a total of 874 million dollars. First round contracts are for four years

with a set cap. Furthermore, as a result of the new CBA, draft picks contracts in

rounds two through seven are slotted depending on how high a player is chosen

(Clayton, 2011). The structure of the new CBA has decreased rookie contracts by

50 percent and has made negotiating virtually a one-way street. The average NFL

career and new CBA has made it essential for NFL prospects to get drafted as

high as possible.

The second way to evaluate NFL draft picks is through the perspective of

the individual NFL team. All NFL teams highly covet their assets and there are

not many better ones than draft picks in the NFL. Furthermore with a hard salary

cap there is only so much money to go around. Following the latest CBA, draft

picks are a better way to invest cheaply in young and athletic prospects. Veteran

NFL players demand higher salaries and have a limited shelf life. When an NFL

team decides to draft a player and not only decide to invest six or seven figures

into the player but also the opportunity to play for them on Sundays. All players,

32

regardless of the sport, will tell you that all they need is an opportunity to succeed

at the next level. Players drafted higher will receive more opportunity then

players drafted lower to play because of the investment made by the NFL team.

33

Chapter 5

Conclusion

Professional sports across the board are billion dollar businesses.

Moreover, almost all people enjoy sports at some level. For these reasons and

more, many people have tried to determine all kinds of different efficiency

measurements for certain sports. While some people have tried to determine the

effect that coaches have on their respected players during the course of the season,

I focused my research on which NCAA football coaches develop their players

best for their future NFL draft. By looking at data from 2003-2013, almost

30,000 players and 232 coaches were evaluated through my model. The statistical

results suggest that certain coaches have a distinct effect on getting their players

drafted higher or lower into the NFL. Some of these notable coaches include

former University of Southern California and defending Super Bowl winning

coach Pete Carroll, who got his players drafted almost 48 spots better then the

average coach. Current Ohio State coach and two time national champion Urban

Meyer does not have any effect in getting his players drafted into the NFL.

Lastly, believe it or not, current Alabama coach and four time national champion

winner Nick Saban gets his players drafted 12 spots worse then the average no

effect coach.

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Appendix

Appendix 1.

Names of all 232 Coaches Measured

Coaches

Al Golden Chuck Amato Al Groh Gary Patterson Andy McCollum Curtis Johnson Gary Pinkel Dabo Swinney Gene Chizik Barry Alvarez George Barlow Bill Blankenship Dan Hawkins Gerry Dinardo Bill Callahan Dan McCarney Greg Robinson Dan Mullen Greg Schiano Guy Morriss Bill Lynch Danny Hope Houston Nutt Bill O'Brien Bill Snyder Jack Bricknell III Bob Davie Dave Wannstedt James Franklin Bob Stoops David Cutcliffe Jeff Bower Bobby Bowden David Shaw Dennis Erickson Jeff Tedford Bobby Petrino Derek Dooley Bobby Wallace Brad Wright Dirk Koetter Jim Grobe Doc Holliday Jim Harbaugh Don Brown Jim McElwain Bret Bielema Jim Mora Brian Kelly Jim Tressel Jimbo Fisher Buddy Teevens Ed Orgeron Joe Glenn Butch Davis Ellis Johnson Eugene Teevens Joe Paterno Carl Franks Joe Tiller Chan Gailey John L. Smith Charlie Strong Frank Beamer John Bunting John Robinson John Thompson Gary Barnett

35

Appendix 1 Continued

Coaches Joe Tenuta Mike MacIntyre Joseph F. Fisher DeBerry Karl Dorrell Mike Riley Keith Burns Mike Sanford Mike Sherman Steve Fairchild Mike Shula Kevin Morris Mike Stoops Steve Logan Morris Watts Neil Callaway Steve Spurrier Kevin Wilson Nick Saban Sylvester Croom Kirk Ferentz Ted Roof Pat Hill Tim Beckham Lane Kiffin Paul Johnson Tim DeRuyter Larry Coker Pete Carroll Les Miles Lloyd Carr Phil Bennett Lou Holtz R.C. Slocum Tom Bradley Ralph Friedgen Mack Brown Randy Edsall Tom O'Brien Randy Shannon Tommy Bowden Mark Dantonio Randy Walker Tommy Tuberville Mark Hudspeth Reggie Herring Rich Brooks Mark Mangino Rich Rodriguez Mark Richt Rick Neuheisel Tyrone Willingham Urban Meyer Ricky Bustle Matt Campbell Walt Harris Mike Belloti Ron English Will Muschamp Mike DeBord Ron McBride Mike Gundy Ron Turner Mike Haywood Ron Zook Mike Leach Ruffin McNeill Source: (2003-2013) Rivals Recruiting

36

References

Bennett, D. (2011). The NFL's official spin on average career length is A joke

Retrieved April 8,2014, 2014, from http://www.businessinsider.com/nfls-

spin-average-career-length-2011-4

Berkowitz, S., Dougherty, S., Schnaars, C., & Upton, J. (2013, ). Top NCAAF

coach salary. USA Today Sports

Berkowitz, S., & Peter, J. (2014, ). Special report: Coaches hit jackpot in NCAA

system . USA Today Sports

Berri, D. J., Brook, S. L., & Fenn, A. J. (2011). From college to the pros:

Predicting the NBA amateur player draft. Journal of Productivity

Analysis, 35(1), 25-35.

Clayton, J. (2011). What new CBA means in football terms . Retrieved April

8,2014, 2014, from http://espn.go.com/nfl/story/_/id/6790759/what-new-

nfl-cba-means-football-terms

Dawson, P., Dobson, S., & Gerrard, B. (2000). Estimating coaching efficiency in

professional team sports: Evidence from english association football.

Scottish Journal of Political Economy, 47(4), 399-421.

Dosh, K. (2012). Texas tops in football profit, revenue. Retrieved April 8,2014,

2014, from http://espn.go.com/blog/playbook/dollars/post/_/id/2556/texas-

tops-in-football-profit-revenue

37

Duffy, T. (2014, ).

Super bowl and signing day: Seahawks and broncos have more former

two-star recruits than four and five-star recruits USA Today Sports

Fizel, J. L., & D'Itri, M. P. (1997). Managerial efficiency, managerial succession

and organizational performance. Managerial and Decision Economics,

18(4), 295-308.

Groothuis, P. A., Hill, J. R., & Perri, T. J. (2007). Early entry in the NBA draft the

influence of unraveling, human capital, and option value. Journal of Sports

Economics, 8(3), 223-243.

Huguenin, M. (2013). Year-by-year look at five-star college football recruits .

Retrieved April 8,2014, 2014, from

http://www.nfl.com/news/story/0ap2000000320664/article/yearbyyear-

look-at-fivestar-college-football-recruits

Kahn, L. (1993). Managerial quality, team success and individual player

performance major league baseball.46(3), 531-547.

Kahn, L. (2000). The sports business as a labor market laboratory. The Journal

of Economic Perspectives, , 75-94.

Mihailovich, D. (Producer), & Keteyian, A. (Director). (2013). The perfectionist.

[Video/DVD] United States: 60 Minutes.

38

Lee, Y. H., & Berri, D. (2008). a re‐examination of production functions and

efficiency estimates for the national basketball association. Scottish

Journal of Political Economy, 55(1), 51-66.

Litman, L. (2013, ). Alabama chancellor says nick saban is best investment in

university history. USA Today Sports

Maxcy, J. G. (2013). Efficiency and managerial performance in FBS college

football to the employment and succession decisions, which matters the

most, coaching or recruiting? Journal of Sports Economics, 14(4), 368-

388.

NCAA home page. (2014). Retrieved April 9,2014, 2014, from

http://www.ncaa.org/

Rivals.com. (2014). Retrieved January 25,2014, 2014, from

https://footballrecruiting.rivals.com/

Rottenberg, S. (1956). The baseball players' labor market. The Journal of Political

Economy, 64(3), 242-258.

Scully, G. W. (1974). Pay and performance in major league baseball. The

American Economic Review, 64(6), 915-930.

Spurr, S. J. (2000). The baseball draft A study of the ability to find talent. Journal

of Sports Economics, 1(1), 66-85.

39

Weathersby, E. (2011). 10 elite players who were not 5-star recruits . Retrieved

April 8,2014, 2014, from http://bleacherreport.com/articles/647492-10-

elite-players-who-were-not-5-star-recruits

Zak, T. A., Huang, C. J., & Siegfried, J. J. (1979). Production efficiency: The case

of professional basketball. Journal of Business, , 379-392.

40