A SIGNAL IN THE NFL DRAFT: DOES THE NFL COMBINE HA VE A POSITIVE INFORMATIONAL VALUE?
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
Melanie Auguste
May. 2009 A SIGNAL IN THE NFL DRAFT: DOES THE NFL COMBINE HAVE A POSITIVE INFORMATIONAL VALUE?
Melanie Auguste
May, 2009
Economics and Business
Abstract
A previous study suggests NFL teams are inefficient in their draft decisions. Additionally, previous studies find the NFL Combine to be a predictor of draft position but not a predictor of NFL perfonnancc. This has led some to question the overall usefulness of the Combine. The focus of this study is to determine if the NFL Combine has a positive infonnational value to decisions made in the NFL Draft. From an alternative perspective, this study suggests the Combine is only used to identify the best available players in a specific position for a specific year. Therefore the Combine's infonnative value to the NFL is its ability to act as a signal, by separating prospects in specific positions into groups based on characteristics that indicate their perceived value. A Spcannan Rank Correlation is utilized to evaluate this hypothesis. Combine performance is found to significantly relate to draft round for Running Backs, Linebackers, and Defensive Backs. Alternatively, collegiate performance relates to draft round for Running Backs, Wide Receivers, Tight Ends, Defensive Ends, and Linebackers. For these positions, with the exception of Running Backs and Linebackers, collegiate performance appears to act as a signal instead of Combine performance. The Combine can be considered a signal only for Running Backs, Linebackers, and Defensive Backs. Therefore there is evidence of signaling within the NFL Draft for six of the eight skill positions observed. Further, evidence from this study suggests the additional information from the Combine has a positive value when it has an inl1uence on draft decisions. Therefore the Combine appears to have a positive informational value, but further research is necessary.
KEY\VORDS: (National Footballl.caguc, NFL Draft, Combine, Signaling) ON MY HONOR. I HAVE NEITHER GIVEN NOR RECEIVED UNAUTHORIZED AID ON THIS THESIS ACKNOWLEDGEMENTS
I would like give thanks to all of those who have supported me in my journey to and from Colorado College. Thank you to my parents for you love and encouragement. I would also like to thank everyone in the Economics and Business Department for their generous contributions to my education and development. TABLE OF CONTENTS
ABSTRACT 11
ACKNOWLEDGEMENTS IV
INTRODUCTION
II LITERATURE REVIEW 8 Theories on Human Uncertainty and Decision Making...... 8 Theories on Job Market Signaling...... 14 Theories on the NFL Dratt...... 20
1lI THEORY 27 Requirement ( 1) ...... 31 Requirement (2)...... 34
IV DATA & METHODS 39 The Data...... 39 The Methodology...... 42 Combine and Collegiate Performance Ranking Method...... 44 NFL Performance Ranking Method ...... 47
V RESULTS 49 Results of Analysis by Individual Draft Rank .... SI Defensive Backs ..... 51 Linebackers ...... 53 Safeties ...... 55 Defensive Ends ...... 56 Qnarterbacks ... . 58 Running Backs 59 Tight Ends...... 62 Wide Receivers...... 64 Summary of Analysis by Individual Draft Rank...... 65 Results of Analysis by Draft Round...... 66 Summary of Analysis by Individual Draft Round...... 71
VI CONCLUSION 72
APPENDIX A...... 77 APPENDIX B...... 78 APPENDIX C...... 79 APPENDIX D...... 80
SOURCES CONSULTED...... 82 LIST OF TABLES
4, I Individual Test and Statistics Performance Variables"""""""""" """""",, 41
4,2 Overall Combine Performance and Overall Final Year Collegiate Performance Variables", "'''' """"""'"'' """""'" '" """""" '" "", 45
5,1 Spearman Rank Correlation Coefficients for Defensive Backs"""""""""" 52
5,2 Spearman Rank Correlation Coct1icients for Linebackers"""""""""" "", 54
5,3 Spearman Rank Correlation Coefficients for Safeties"""""",,,,,,,,,,,,,,,,,, 55
5.4 Spearn1un Rank Correlation Coctlicicnts for Defensive Ends ...... , ... ,...... 56
5,5 Spearman Rank Correlation Coefflcients for Quarterbacks"""""""""""" 58
5 ,6 Spearman Rank Correlation Coetlicients for Running Backs""""""", '''''' 59
5,7 Spearman Rank Correlation Coefflcients for Tight Ends""""""""" """ ", 62
5,8 Spearman Rank Correlation Coet1icients for Wide Receivers"""""""""", 63
5,9 Correlation Coefficients for Average Overall Performance Ranks vs, Draft Round"""",,,,,,,,,, """"""""""""""'" "'"'''''''''''' """,,",'" ", 66 LIST OF FIGURES
2.1 Signaling Feedback Loop...... 13
2.2 Basic Signaling Model: Presence of a Separating Equilibrium...... 17
3.1 NFL Signaling Feedback Loop...... 30
3.2 Signaling Model for Individual Draft Rank...... 32
3.3 Signaliug Model for Draft Round...... 33
3.4 NFL Draft Pick Market Value Curve...... 35
3.5 NFL Performance vs. Combine Performance and Statistical Performance...... 37
4.1 Spearman Ranking Example ...... 42
4.2 Speannan Rank Correlation Equation...... 43
4.3 Overall Rankings Sum Calculation Example...... 46
5.1 Graph of Combine Rank Averages vs. Draft Round...... 67
5.2 Graph of Final Year Collegiate Statistic Rank Averages vs. Drat! Round...... 68
5.3 Graph of Combine and Final Year College Statistic Rank Average vs. Drafl Round...... 70
CHAPTER I
INTRODUCTION
Every year representatives from various employers visit hundreds of college and
university campuses searching for high-ability people to fill their job openings. Despite
excluding a large portion of the workforce by only focusing on applicants with higher
level education, the size of the applicant pool is still large enough to make distinguishing
among them difficult Applicants try to present as much positive information as possible
to employers. For new graduates without job experience, this information could include
attaining a high grade point average, extra-curricular activities, or summer internships in
a similar field. For some jobs this information is enough for them to determine which
applicants appear to have the best ability for the job. For other jobs, an applicant's
college perforn1ance still may not be enough for cmployers to confidently determine
which applicants possess a high level of ability. Employers struggle to trust the
information given to them by applicants because there is a high incentive for applicants to give faulty information. Regardless, employers need to find new talent. Therefore employers need to lind ways to use and interpret the available information to make good hiring decisions. Interest in how people utilize the available information to make decisions in situations with incomplete in/()rmation has lead to an abundance of academic theories on human decisions under uncertainty. 2
Some studies have suggested that under uncertainty people adapt their behavior
based on what they see in their peers. 1 Others have experimented and found that
additional information in a situation with incomplete information does not always lead to
a better decision2 When making decisions with incomplete information, people seek the
best ways to utilize. analyze, and manipulate the available information to make the most
optimal decision.
In 1973 Michael Spence developed a theory on the job market, which suggests
applicants obtain a specific signal, which employers use to determine an applicants
potential ability level. J This theory is known as job market signaling. This theory is
based on the belief that the process to obtain to the signal is of high cost for individuals of
low ability. This cost for an individual oflow-ability exceeds the return the individual
would receive, thus those of high-ability are the only individuals to obtain the signal.
Later studies have determined that the signal in the job market is the presence of a degree
or credential.
If over time the performance of the individuals with the degree or credential does
not match the employers' beliefs towards ability at hire then the signal should no longer
be used 4 This means if the signal does not retleet employers' beliefs, there should not be
a difference in salary between those with the supposed signal and those without. In a job market where the presence of a particular test performance. degree, or credential is
1 Alchian. Annen A .. Uncertainty, Evolution, and Economic Theory. The Journal (1'Poii[ical Economy 58, no. 3 (Jun. 1950) : 211-221.
:: Morris. Stephen and I-lyun Shag Shin. "The Rationality and F.:fficacy of Decisions under Uncertainty and the Value oran Experiment" Economic 9, no. 2 (Feb. 19(7) : 309-324,
; Spence. Michael. "Job Market Signaling." Quarterly Journal Ec,)nc'lnl,"s87, no, 3 (1973):
, Ibid, 3
different between people of different salary levels then it can be assumed that employers
use the test, degree, or credential as a signal.
The National Football League (NFL) exemplifies ajob market where there is
extremely high uncertainty and every decision involves a risk worth millions of dollars.
Every year hundreds of collegiate football players (applicants) hope to be drafted by the
thirty-two NFL franchises (employers) at some point during the seven rounds of the
annual NFL Draft, To understand the amount of risk involved with every decision,
consider the following: the average amount of guaranteed money received by the first
thirty-two picks of the 2007 NFL Draft was $10.86 million.s The amount of money
awarded to a player just for being drafted provides enough incentive tor prospects to
endure whatever is necessary in order to prove themselves worthy of a draft position.
Adversely, the large amount of money teams must invest on new talent with unknown
potential results in a substantial risk with each draft decision. Therefore with high
incentives for prospects to do close to anything and high risks pushing NFL teams to tind
out everything, there is a need lor an event that allows both to send and receive available
inionnation. The intensive process created by the NFL to tulfill this need is known as the
NFL Combine.
The NFL Combine is considered a "four-day job interview" for the top 320
(approximately) draft prospects each year." Over the course of lour days, a player is asked to perform a series of medical and psychologieaJ examinations, interviews with
5 Bell. larCH. "Guaranteed Money a Windfall for Today's dral! picks." US4 rodav. 28 April 2008. Newspaper Online. Available from http://\vww.usatoday,com. Accessed 24 February 2009.
'\ National Invitation Camp, NFL Scouting Combine History. Availabie from \vw\v.nllcombine,net: Internet: accessed 5 November 2008, 4
teams, as well as tests on skill and physical ability7 As one 2009 NFL Draft prospect
explained,
"The NFL is a business. Everything they do at the combine is very precise and very in-depth. They aren't going to leave anything to chance. If they arc going to invest in you, they want to know everything about you. They do check you out."s
The NFL Combine first began in 1982 for the purpose of attaining medical
information about draft prospects. 'I The medical examinations can disclose infiJrmation
such as an injury that a player may have had during the collegiate season but kept hidden.
Although the medical exams are still a significant part of the Combine, the workout tests
have recently become the center of attention. The most lollowed workout tests are the
sprint tests (10,20, and 40 yards), the bench press, vertical jump, broad jump, 20-yard
shuttle. and the three-cone drill. 10 Most players also take the Wonderlic Personality Test. which gains attention when a player scores very low or very high. Today, every workout test performance fi'om every single player in the Combine is covered by every major newspaper and sporting channel. Those who perlonn well in the workout tests see their dra!1 position increase on experts' mock draft boards II and rankings. A player can increase their perceived ability on the football field by how fast they run, how strong they are, or how high they jump. This creates the perception that players from smaller, less known schools or players with sub-par collegiate performance can still have an
See Appendix A.
it Harmon, Dick. "How Did Collie Fare at Recent Combine." Desert! :V/orning News (Salt Lake 3 March 2009. Newspaper Onlloe, Available at hltp:i/deseretnews,com
') National Invitation Camp, NFL Scouting Combine History, Available from wwvr.ntlcombine.net: Internet; accessed 5 November 2008.
See Appendix B.
A lT10ck draft is a hypothetical draft \vherc experts express the think \A-ill be drafted with each pick in the coming draft. 5
opportunity to be seen and drafted above others from larger schools with better
statistics. 12
Despite the extensive and highly touted event, NFL teams appear to struggle to
make optimal, efficient draft decisions. For example, quarterback Ryan Leaf was taken
second overall in the 1999 NFL Drati and quarterback Tom Brady was taken as the 199 1h
overall selection in the 2000 NFL Draft. Ryan Leaf only spent four seasons in the NFL
totaling 21 starts and Tom Brady has three Super Bowl titles as a starting quarterback.
Instances such as this create criticism towards the NFL. Further skepticism is fueled by
studies indicating that early draft picks are overvalued. 13 The apparent inefficiencies in
draft decisions have led to a recent curiosity about the Combine's relationship to the drall
and future perfonnance. In 2003 one study claimed the Combine accurately predicts draft
status for most NFL positions. 14 This study supports perceptions that draft decisions arc
almost completely shaped by Combine performance. In 2005, a study found the Combine
fails as an accurate predictor of future performance. 15 This finding has led the authors and
others to the question the overall usefulness of the Combine. It is observations such as
these that have fueled criticism of Combine and lead some to believe it is nothing more
than a spectacle. But, the Combine costs every NFL (cam $75,000 per year to
il Be!!, Jarett, "Small-school Stars Getting a Big Chance at NFL Combine," USA Tuday 23 February 2007. Newspaper On-line. Availabie from http:!\vww,usatoday,com. Accessed 24 February 2009.
;''i Massey, Casey and Richard l-L Thaler. "Overcontldence VS. Market Efficiency in the Nmional Football League." National Bureau Research \Vorking Paper No,\V] 1270 (April 2005). A vaiJabJe online at http://w\vw,nbeLorg. Accessed 1 October 2008.
'i McGee, KJ and LN Burkett. "The National Football Combine, A Reliable Predictor of Draf! Status'!" Journal (4Sfrenf!;lh and C()nd,ili(Jl1i,,~ Research /7. no, 1 (Feb. 2003): 6-1 L
Kum1tz. Fe and AJ Adams .. ' fhe NFL Combine: Does it predict performance in the National Football "journal (~(Sfrength and Conditioning Research 22, no. 6. (Nov. 2008): 1721-1727. 6
participate. 16 Therefore it is difticult to believe the Combine does not have any usefulness
or value. If this were true, it would mean all thirty-two teams are investing $2.4 million a
year in something that does not serve a useful value. This does not seem likely. The
Combine's value may not necessarily be based in predicting who will be the next great
player. It appears to be more likely that the Combine's value to the NFL is based on its
ability to help teams to make a better decision in situations with incomplete information
and high levels of uncertainty about each player. This study attempts to address the
question of the Combine's value and usefulness by applying theories from job market
signaling and human decision-making.
NFL teams are trying to find the best available players to fill their various needs.
The draft is a job market where there must be the selection of a player (hire) in every
allotted draft position. Therefore, it is suggested here the Combine is not in place to
inform teams about a player's future performance relative to those already in the NFL.
Instead the Combine is more likely to be a signal that helps teams distinguish among
players respective to what is available in their position and draft year (referred to in this
study as draft class). Further, considering the Combine as a signal and not a predictor,17 it
is likely that decisions are not completely based on Combine performance.
The abundance of collegiate football players creates a large tield of players with seemingly good statistics. This makes it diHieult for tcams to know which players are accurately represented by their collegiate performance. The Combine selection committee
Brandt, Gil, "'The Cost of the Combine," NFL com. 24 February ::009< Blog On-line. Available n'om http: 'blogs.nflcom, Accessed 24 February 2009,
n A predictor is considered a statistic that is an accurate reflection of one's future output; in the cas\.: ofthe !".iFL future statistical A is considered to be an indication of an ability !eve!, but is not used to estimate fmure performance, As long as those with in the differing groups have differing pert()fmance lev('!s thi; is successful, even if it can not predict what those performance levels wili be. 7
consists of representatives from NFL player personnel departments and directors from the
two scoutmg, serVices,18 t h at represent twentY-SiX,'NFL teams, 19 'l'h US, smce'h t e commlttee'
goes through the process of analyzing all of the collegiate players to develop their
invitation list, the Combine gives NFL teams a venue to narrow down their scouting
focus, This study attempts to analyze the Combine performance, collegiate performance,
draft position, and NFL performance of players drafied in eight skill positions
(Quarterbacks, Running Backs, Tight Ends, Wide Receivers, Defensive Backs,
Linebackers, Sateties, and Defensive Ends) to investigate the value of Combine
performance to NFL draft decisions, The study is based on the assumption that if the
Combine has a value, it is a signaling value in which it is only used to group players by
their ability relative to their position and drati class, This is not an attempt to discredit
the idea that decision-making in the draft is be suboptimal. The purpose of this study is to
analyze the possibility of the Combine as a signal among many that helps NFL teams
make more optimal decisions than they would make with only collegiate performance,
This study will proceed by first presenting previous research on human
uncertainty and decision-making, job market signaling, and professional league drafts,
Next, in order to further understand what this study is looking for within the data, these
theories will all be combined and put into the framework oCthe NFL Draft, Following
this. there is an explanation of the data collected and the non-parametric method used to analyze this data. The results are then presented and analyzed, This study will conclude by summarizing the results and providing possible reasoning t()r the results found,
]B: National Scouting Service and BLFSTO scouting scrvh:e
i0Natiollai Invitation Camp. "FAQ: How arc players selected to the NFL C'ombinc?"' Internet; Available from \\w\v,nflcombinc_l1ct. Accessed 5 November 2008< CHAPTER II
LITERATURE REVIEW
Theories on Human Uncertainty and Decision-Making
Alchian (1950) defines uncertainty as a phenomenon that results in multiple,
overlapping distributions of potential outcomes. Therefore one can consider the task of
making a decision under uncertainty as choosing the action with the most optimal
distribution of potential outcomes 1 According to Alchian's theory, there are two causes
of uncertainty. The first cause is called "imperfect foresight." This is when an individual
cannot foresee the result of an action. The second cause is explained as the "human
inability to solve complex problems containing a host of variables even when an
optimum is detinable:,2
Alchian uses a theoretical random-behavior model to argue that without the use of
a traditional "profit-maximization" model, behavior and decisions can still be predicted in
the presence of uncertainty and incomplete information. Within his theory the presence of
adaptive behavior can help model behavior in the framework of uncertainty. 1\vo types of adaptive behavior are identified: imitative and trial-and-error. Imitative behavior occurs when there arc observable successful fimls. The observable attributes will be associated
Alchian, Annen A .. Uncertaint)" Evolution, and Economic Theory'. the journal U·,;!1(.'I"r 58. no, 3 (Jun. 1950) _211-221.
Ibid.
8 9
with success and copied by other firms because imitation creates an alternative to making
decisions without an observed outcome, The second adaptive behavior is trial and error.
Morris and Song Shin (1995) explore a theoretical model of utility for a decision
maker who makes a choice under uncertainty with and without further information, In
their model, further information is obtained when the decision maker has access to an
experiment of some type before making their decision, They conclude using a
mathematic proof that the value of the additional information from the experiment is
positive, only if the true expected utility from receiving the further information is at least
equal to the utility from making a decision solely on the original information3
A real world example of Morris and Song Shin's theory is in the job market
where there are an increasing number of employers who are using tests to help evaluate
applicants4 A number of linns have turned to the use of cognitive tests to assist their
hiring processes, Van Steenwyk (2008) credits this trend to the decrease in the trustworthiness of information presented on resumes, Based on the theory presented by
Morris and Song Shin, one might assume these firms are receiving a higher utility from their hires than prior to the use of tests, But. it may also be possible that firms are using imitation' and are not aware of the true utility they arc gaining from the use of tests, In order for tests to give firms more utility from their hires, the tests would have to be an accurate reflection of an applicant's ability level. This theory is the reasoning behind Van
3 Morris, Stephen and Hyun Shog Shin. "The Rationality and Efficacy of Decisions under Uncertainty and the Value of an Experiment." E'cUiwmic Th<:UfY 9, nCL 2 1997) : 309~324.
1 Van Steenwyk, Jason. 2008. "Using Tests to Screen Employees." Journal I- iI1'<1ni.:wl Planning (I 1 : 5-10,
~ Akhian, :2 J J -221. 10
Steenwyk questioning of the NFL's use ofWonderlic Test6 scores in quarterback
evaluation. Van Steenwyk notes the inconsistency between NFL performance and
Wondcrlic test scores by quarterbacks. For example, Hall of Fame quarterbacks, Terry
Bradshaw and Dan Marino, scored well below the average score on the Wonderlic Test.
Alternatively, Michael Vick scored a decent score but he is now out of the league and
serving jail time. This is an instance where the additional information received may not
have led to decisions resulting in a greater utility. The question of whether the additional
information firms are seeking is beneficial is a topic that needs more research applied to
the theoretical models.
In markets where there is buyer and seller, differing levels of goods can exist.
This creates uncertainty among buyers7 As a result of the uncertainty, theories have
suggested that buyers may use any and all available statistics to judge the potential
quality of goods. K This can be applied to the job market.
There is a level of uncertainty towards the quality of the applicants when an
employer is unable to see an individual's abilities and productivity within the job9 To help employers make decisions about who to hire and what wage to give them, employers try to obtain as much information as possible. For example lirms may ask for education credentials, job experience, background check, or a cognitive test to aide their decision.
Consequently, in the presence of uncertainty, risk-averse employers may show
'; The Woodedic Test is a cDgnitive test that measures a person's intelligence. It is a 12 minute. 50 question rest. The average score is around 21 correct answers. See http:/\vW\v,\vonderlic.com
AkerloC George A, 1970. The Market for "Lemons"; Quality Uncertainty and the Market Mechanism. Qlfartt!r~v Journal olEcu!1omics 84, no, 3 (08) : 488-500.
Slbid.
"Spence, 1'vlichaeL J973. Job \hrket Signaling. OU(1n,!rivJohl'nai lc:c"mfmIlC.I 87. no. 3 (OS} : 355- 374. 11
preferences to particular groups of individuals based on specific statistical information or
characteristics such as test score, age, race, or gender.
Arrow's (1971) model of discrimination in a market with imperfect information
suggests that employers possess preconceived ideas that one group of workers has a
higher productivity that another group of workers (i.e. black workers vs. white
workers). 10 In this model the return to an employer is equal to (MP- Wgmup) Pgmup; the
marginal productivity of a qualified individual (MP) sub the wage paid to those of a
particular group (W group) mUltiplied by the probability a random individual from that
group is qualitied (Pgroop)' When an employer has reason (rational or irrational) to believe
PGruup A > PGroup [J, in attempt to mitigate their risk and recoup their costs, employers pay a
wage (W) schedule that results in W Group A > W Group B.Il Applicants can be grouped by any
characteristic. The most common focus of this type of wage discrimination is the
ditIerences between white and black workers.
Following Arrow, Aigner and Cain (1977) develop a theoretical model that
explains the perceived ditTerences between racial or gender groups can be attributed to a
reliability differential. 12 The reliability dit1erential is developed Irom performance
differences on measures of ability such as standardized tests. As an alternative approach,
Borjas and Goldberg (2001) suggest that the tests or screening processes creating the
Arrow, Kenneth J. "The n1cory' ofDiscflmination." Pr/ncv{()rt Economics DCDw·tm,mi. Industrial Relations SeCfion. Working Paper No. 30}\, (October 1(71). Available online fl'om http: /\vw\\'-lrs.priceton.cdu. Accessed 5 November 2008.
'i Ibid.
. Dennis J. and Glen G. Cain, "Statistical Theories of Discriminmi(Hl in Labor \\i1arkeIs." Industria! (\( lahoi ri!lafions reYiew 30, no, 2 (J (77): 175-187. 12 reliability dilTerential are biased and therefore are not a true indicator of ability, 13 This is further evidence suggesting the possibility that some methods used by employers to obtain additional information are not creating an accurate depiction of the applicants,
Therefore employers' hiring decisions are not providing additional utility,
As Akkrof (1970) explains, "the difficulty of distinguishing good quality from bad is inherent in the business world,,,14 Both employers and applicants within the job market attempt to do what they can to protect themselves from adverse selection,
Applicants invcst in things such as education or vocational training to give employers a
"signal" of their productivity, Employers read these signals and reward those signals they feel reflect the ability level they want in the form of hiring and salary decisions, 15 This creates a feedback loop (Figure 2,1), The theory behind this process is known as job market signaling, a theoretical model that gained attention in the early 1970's, 16
Job market signaling extends the theories of human uncertainty and decision making into the job market. Based on this theory, for employers and applicants, the informational value of observable signals 17 exceeds the productivity value, 18 This means
13 Borjas, George J., and Matthew S. Goldberg. "Biased Screening and Discrimination in the Labor Market." American Economic Revielv 68, no. 5 (1978): 918.
" '\kerlof, George ;\, 488-500,
i'i Spence, MichaeL 1976. Competition in Salaries, Credentials, and Signaling Prerequisites tor Jobs. lhe QU<1r!t?r~v Jourlhil olEconotnics 90, no. 1 (Feb,) : 51-74.
1,. Arrow, Kcnndh 1. 1973. Higher education as a filter. Journal Fconomi{'s 2, no. 3 (7) : 193-2 !6,
Spence 355-374.
afe considen:d to be characteristics that separate emnl"-,,m 111 groups.
ill SpcnL:e. 355-374 13 that the presence of diplomas. degrees, and test scores does not mean an applicants' productivity has been increased. Therefore the attainment of credentials is considered to be more of an economic institution in place to separate individuals into diftering identifiable groups. This institution is believed to aid the decision-making process and help negate some of the risk associated with uncertainty; not necessarily an institution that adds to the productivity of the population.
FIGURE 2.1
Signaling Feedback LOOp19
Employer's belie!:s about Offered jobs and wages the relationship between as a function of observed observed information and information productivity
i ~ Hiring and observation of Applicant's decisions marginal productivity in towards obtaining the relation to observed positive infonnation to information signal employers
Perceived process of action that can occur to promote the use of a particular characteristic to separate applicants into perceived ability levels.
i" Spence. 355-37'+. 14
Theories on Job Market Signaling
Job market signaling theory is based on the idea that employers are uncertain of a person's productivity value upon hire. Therefore the application and hiring processes within the job market is considered to be a "loltery.,,2o Employers try to use observed information to predict an applicant's unobserved productivity value.
Applicants' have a sct of observable characteristics about themselves that can be considered by employers as indicators of their unobserved abilities. Spence (1973) separates these observable characteristics into two categories; (I) Indices, characteristics that cannot be changed by the individual (i.e. sex and race) and (2) signals, characteristics that can be changed by the individual (i.e. education). Education is the most notable and tested signal by those interested in job market signaling21
Some who study the theory of job market signaling argue for education as a filter rather than a mechanism that increases productivity. For example, Arrow argues that higher education is a double filter for employers. 22 Because colleges have an admissions process. colleges first filter the high school students into who they feel is capable and incapable of graduating. Then college filters a second time by passing and failing students. thus granting some a degree and others nothing. By the time employers search for college graduates the potential field has been narrowed down twice. Based on this theory higher education is useful just by being present regardless of whether or not students learn and become more productive.
:0 Spence. 355-374.
" Ibid. 15
Tyler et al. (2000) use data on similar low-end GED scores in states with differing
passing standards to find that the sole presence of a GED increases the earnings of white
24 dropouts by 10 to 19 percent. 23 This study controls for a human capital effect by using
the same scores from states with different passing standards. This means among
individuals with the same score, some had obtained a GED and others had not.
Theoretically the only difference between the individuals observed was the possession of
a GED credential. This study provides evidence in favor of the signaling hypothesis. It
shows that individuals of the same ability are being paid differently by employers based
on the presence of a single characteristic; in this case a credential known as the GED.
Another study uses data from Hong Kong's labor market to lind evidence
supporting the use of education as a signal. Heywood and Wei (2004) find that employed
individuals in Hong Kong consistently received higher earnings with increased education,
but only when the increase in education is signilied by the completion of a credential.25
This finding supports argument that there is a separating equilibrium within the job
market. The presence ofa separating equilibrium is a premise of the signaling theory.
A separating equilibrium is one where applicants are separated in groups of
varying perceived productivity levels based on presence of specific characteristics. 26 Thc
perceived productivity level is represented by the wage schedule. Those with the signal
are given a higher wage than those without the signal (Figure 2.2). In the simplest form of
John I-L, Richard l Murnane, and John R Willett Estimating the Labor Market Signaling Value of the Ged. Quarrer(vJournul (?IEconomics i IS, no. 2 (2000): 431-468.
2-1 Human capital cHeet IS when the credential observed is said to have made a person more productive.
25 Heywood, lohn S" Xiangdong Wei. 2004. Education and Signaling: Evidence from a Highly Labor Ylarket. F,Jucurion EI..-(;l1omics 12, no, 1: 1 : 1-! 6. 16
the signaling model there are two groups; high-ability and low-ability. The group to
which an individual truly belongs is not observed before hire. The low-ability applicants
try to pass themselves off as being a person of high-ability. Therefore. in order to
distinguish from themselves low-ability individuals, individuals of high-ability invest in
retrieving the signal. In the case of education it is some type of credential. In order for a
separating equilibrium to exist in a job market where the productivity cannot be viewed
prior to hire, high-ability individuals have to invest in retrieving the signal. This means a
signal is only useful if the cost of the signal creates optimal returns for an individual of
high ability and sUboptimal returns for individual oflow-ability. If the cost of the signal
does not fit this requirement then there is incentive for low-ability individuals to obtain
the same signal, thus diluting the informative value of the signal. This creates a pooling
equilibrium. In a pooling equilibrium everyone is paid the same wage:
2:-Wage Proportion qfLmv Ability + Alarginal ProducOvify of High AhWty(J - Proportion (?lL01-V Ability)
The presence of a pooling equilibrium discredits the presence of signaling because in a
pooling equilibrium everyone is placed in the same group with the same wage. Further, in
a pooling equilibrium those who are of low-ability are being overpaid and those of high
ability are being underpaid, thus making the firm inetlicient in its wage distribution. If the presence of a signal is able to help firms better distinguish people, even in the event the signal is wrong at times. firms that need those of high-ability stili may be better off because they are hiring more people of the needed ability than they would be in pooling equilibrium.
Spence. 355-374. 17
FIGURE 2.2
Basic Signaling Model: Presence of a Separating Equilibriurn28
Measurement of Signal: Education Credential y ;;: number c;f education years y*:;;;:; education year 1Y.! which a defl)'ee is ?)'anted
Battigalli (2004) applies a model of multistage games with incomplete
information to the job market. He finds evidence supporting the presence of a separating
equilibrium, but only when there is a detinitive distinction between the groupS.29 When
there is not a definitive distinction between groups the equilibrium is closer to a pooling
equilibrium. This can explain why studies find evidence of signaling when there is a
control for education credentials, but evidence against signaling when the models are
based on the number of years of education. It is dil1icult to determine the value of three
years of education compared to four years of education. But if four years of education means a degree and three means no degree. then there is an observable characteristic that defines the ditTerence. RelUrns from the presence of an education credential arc refelTed
2g Spence. 355-374.
Banigalli, Pierpaolo. "Rationalization in Signaling Games: Theory and Applications." Boc(,Of1i Jnnocen::o (iuS!hil'llli Jnsri!utt! ECONomic Research lCiIER \Vorking Paper No, 275. December 2004. Available online from httpJ!papers,ssrn,conL Accessed 5 November 2008. 18
to as sheepskin effects30 The presence of sheepskin effects is also considered evidence
for the presence of a separating equilibrium in the job market.
Originally studies found evidence refuting the signaling hypothesis because there
was a lack of evidence for the presence of sheepskin elTects. These studies claimed the
rates of return to dropouts are as high as the returns to those who complete an additional
course. 31 But, Hungerford and Solon (1987) using similar data, control for the attainment
of credentials by allowing for discontinuities in diploma years.32 The results of their study
provide statistically significant evidence indicating larger returns to additional years of
education when the additional years result in a diploma. This study and its approach
create a model in favor of sheepskin effects and the signaling theory.
Weiss (1995) argues f()r the use of a sorting model as an explanation for the
returns to education. The term sorting refers to both the signaling by workers and the
screening of applicants by employers. The sorting hypothesis states that individuals are
sorted by their education, but education is only a representation of the unobserved
abilities an individual possessed prior to pursuing education. This means an individual
has unobserved characteristics that can become correlated with schooling. Therefore the
sorting model suggests that the attainment of a signal betters only an individual and not a
society. This means the institution in place to create a signal is there to separate people,
and not to affect their productivity.
,,; I-tungerfora, rhmnas, Gary Solon. "Sheepskin Effects in the Returns to Education." Rei/few Fcotlamics & .\tufi.Vfics 69, no. j (1987) : 175-177
Layard, Richard and George Psacharopoulous, "Thc' Screening Hypothesis and Returns to Education" Journal 82. () 985~998.
Hungerford and Solon, 175-177 19
Bedard (2001) tests the sorting hypothesis by analyzing high school dropout rates
in areas with and without a university33 He runs a regression on data from 1966, and
linds there were higher high school dropout rates in areas with greater university access.
In a sorting model, education only an gives an individual a private return; therefore
increased access to higher education only benefits those who were already capable of
going to college but maybe for other reasons were not able to afford it prior. The
marginal return to a high school diploma is decreased by the higher presence of college
graduates in the local job market. Following the theory of signaling, the decrease in
marginal return for those of low-ability leads them to lose their incentive to obtain a
diploma. This accounts for the linding of higher dropout rates for high school students in
areas with greater university access. The evidence supports the presence of a sorting
model, thus further supporting the presence of signaling within the job market.
Theories on human uncertainty and decision-making suggest that people use
available information to increase their probability of making the right decision. In the
presence of many potential choices, the narrower the choice pool, the higher the
probability of making the right choice. Theories on signaling in the job market, suggest
that the definitive characteristics people possess provide a means to eliminating
applicants. Thus employers have a higher probability of finding the best applicant, as
long as the signal is an accurate indicator of ability.
A number of studies have found evidence in support orthc signaling theory. The
majority of those studies looked at education as a signal because it is easily observed and
measurable. The difficulty in studies involving the labor market is the lack of
Bedard, Kell), 2001, Human Capital versus Signaling \1odels: University Access and High Schooi Dropouts. Journal ,d Politico/ l09, no. 4 (08) : 749, 20
measureable indicators of productivity. Professional sports provide an effective venue for
testing labor market theories because there is an abundance of data on the productivity of
the employees. As a result there are a number of studies that have made inferences about
the labor market by using professional sports as a modeL
Theories on the NFL Drat!
The labor market within professional sports is filled with uncertainty about future
productivity. The amount of risk attached to a single wrong hiring decision can result in
losing millions of dollars. As a result those in the tront of1ice of pro/Cssional sports
franchises attempt to gather as much data as possible about potential players to help them
make the best possible decision. For athletes currently in the league there is an abundance
of observable information about their productivity, so typically teams have an accurate
idea of a player's value. For players coming out or the amateur ranks the observed
information may not be a true indicator of their ability to produce at the professional
leveL Therefore teams are most likely to make their biggest mistakes or biggest gains in
their draft decisions.
Many studies have applied models of uncertainty, decision-making, and market
et1iciency to the drafts of major professional sports leagues in North America. Studies on
the National Basketball Association (NBA) Draft find that collegiate productivity is a
determinant of draft position, and dran position is a signiticant indicator of future
protessional productivity.34 Therefc)re this may explain why higher draft choices appear
i<1 Coates. Dennis and Babatunde Oguntimein. "The Length and Success ofNBA Careers: Does Production Predict Profcsslon<:tl OutcomesT' XOrlh Ami!rican Associatiun ECOf7urtHSfS Wo.ckm·"/\;per Series. Working Paper No. 08-06. Augus12008. Available online from http: \yww.holycross.cdu/departments!cconomics, Accessed 5 November 1008 21
to stay in the league longer.'5 This suggests that in the NBA, overall teams arc making
rational and accurate draft decisions. This leads to the assumption that the statistical
information NBA teams collect is positively affecting their utility.
Spurr (2000) tests whether Major League Baseball teams are able to effectively
evaluate talent. The results suggest players drafted higher are more likely to make the
league36 Further, players drafted out of college have a higher probability of making the
majors than players drafted out of high school. Since players out of eo liege have more
available information than players out of high school, it appears that MLB teams are
effectively using additional information to make better decisions on players.
Alternatively, studies on the NFL draft find evidence that NFL teams may be
overvaluing their draft picks and using statistical information that does not have a
significant correlation with future perf(lrmance.
Hendricks et al. (2003) apply models of statistical discrimination and uncertainty
to the labor market for NFL football playcrs37 Their study finds evidence of statistical
discrimination among players drafted between 1972 and 1992. Players from Division
lAA programs who are selected in the early rounds of the draft are have a higher
probability of a long career length than players trom Division IA programs. Alternatively,
Division IA players selected in the later rounds of the draft have a higher probability ofa
long career than Division lA.A. This cvidence is attributed to the fact more Division IA
1513an), Staw and H3 Hoang, "Sunk Costs in the NBA: Why Draft Order Affects Playing Time and Survival in Professional Basketball." ,4dministrarire SCfI!f"'il..'e QuuTrer(r "fv, no, 3 (1995): 474-494,
," Spurr, Stephen, "The Baseball Draft: A Study of the Ability to Find Talent." Journal ofSj')orts E('onomics 1, no. I (2000): 66~85.
Hendricks. Wallace_ Lawrence DeBrock, and Roger Koenker. and Subsequent Performance: The NFL Draft." Journa! Economics 21, no. 4 ()003): 857-886, 22 players are selected in the early rounds and more Division IAA players are selected in the later rounds. Hendricks' suggests that teams are more risk adverse in the early rounds of the drafi. Thus, when deciding between two athletes with similar statistics, the value of a
Division IA experience is favored over a Division IAA experience. Therefore for a player with Division IAA experience to be drafted in an early round, they have to possess an abundance of information that sets them far apart from other Division IA players, In the later rounds when there is less risk involved teams appear value Division IA experience lower and favor the other statistical information available. Further, players from positions where productivity is not easily observable appear to be less valued than those who have multiple statistical measures of productivity. This indicates the preferences NFL teams have when making their draft decisions.
An athlete's draft position can serve as a measurement of their perceived value38
Athletes drafted higher are expected to have a high probability of success and therefore are paid higher amounts, As a result there is a large amount of risk associated with overvaluing a prospect, especially a highly drafted prospect. The risk is much higher for teams with early draft picks not only because ofthe monetary compensation they pay to the players, but also because of the reverse order of the draft. Teams with early draft picks are typically the least successful and therefore are in the most need for quality
Conlin, MichaeL "Empirical Test of a Separating Equilibrium in Nationa! Football League Contract Negoiations." RAND Juurnu/ 30, no.2 (1999): 289-304,
Conlin, Michael and Patrick Emerson. "Multidimensional Separating Equilibria and Moral l-lazard: /\n Empirical Study of Nation a! Football League Contract Negotiations," The ReV1£!lV Ecunomics and Staff~'ifics 65. no.3 (2003): 760-765,
and Richard H. Thaler. "'Ovt'rcontldence VS. \1arket the National Football League." ,\/utiotral Bureau Rest!urch. Working Paper No, W 11270 {April 2005), Available online at http:i;w\\,lrv.nber.org. Accessed! October 2008. 23 players, Wrong draft decisions when a team is in need of quality talent can hinder the future success of the team,
Massey et aL (2005) tInd evidence of top draft picks being inefficiently
3 overvalucd by NFL teams '! Their study uses data on draft day trades to estimate the market value of a draft pick, and then compares that market value to the productivity value oCthe drafted player. Massey argues that top draft picks do not generate enough of a surplus in their productivity to rationally explain the estimated market value paid for right to draft the player. It is further argued that this is an implication of teams overvaluing their ability to cvaluate talent because of the abundance of statistical information, If the additional statistical information NFL teams gather is not an accurate reflection ofpotentiai productivity value of players, then teams could possibly be making decisions that arc not making them better.
A study by Quinn ct al (2007) investigates the productivity value of quarterbacks selected in the draft40 The results show evidence that higher drafted quarterbacks do not necessarily become more productive passers than those drafted after them, The study uses data on quarterbacks drafted between 1999 and 2004 to find a correlation between pass attempts in the tinal year of college play and draft order. It is also found that pass attempts in the final year of college is statistically, negatively correlated with NFL productivity. Because of the large discrepancy in pay between top drafted positions,4l this
O'i Massey et al. Working Paper No. Wl1270,
Quinn, Kevin G. , Melissa Geier, and Anne Berkovitz. "Passing on Success? ProducitiYilY Outcomes for Quarterbacks Choen in the J 999-2004 National Football League Player Entry Drafis," in/ana/irma! Association Economists/Non/? American Association (~n)"p()rIS Economics Working Serius No 07~ 11 (June 20(7). Available online at bUJL.\\.. '-',~'-!)<}!'-.£[O)s,t:c!uccleP~![lrl1"1l",.\'eQIC~1'·lljfi Ace e s sed 5 Nove m ber 2008. Hendricks et a1. 857 H 8S6. 24 indicates inefficiency in the drafting of quarterbacks because highly drafted quarterbacks are statistically more productive,
Examining NFL contract negotiations for newly drafted players, Conlin (1999) finds evidence of a separating equilibrium, In this separating equilibrium the act of holding out through the start of training camp before signing a contract is taken as signal for players who are of high ability, Those who sign after these long negotiations arc found to sign contracts that are more lucrative and have a higher productivity value, This is further supportcd by Conlin and Emerson's (2003) examination of the same type of
NFL contract negotiations,42 Their analysis of the data finds further evidence supporting the notion of a separating equilibrium, This study suggests that within delayed negotiations, players reveal private information about themselves that may be correlated with the observed statistical data,
Multiple studies have explored the NFL Combine in relation to the dralt and future productivity,43 McGee and Burkett (2003) test the Combine'S ability to act as a predictor of drall round for an athlete, For players drafted in the 2000 NFL draft, their study claims to accurately predict the draft round of all wide receivers, running backs, and defensive backs based on their Combine results, For quarterbacks a large part oftheir combine testing involves the Wonderlic test of intelligence, Mirabile's (2005) study
.j2 Conlin. Michael and Patrick r::rnerson. 760-765.
n McGee. KJ and LN Burkett. "The Nmional Footbail Combine: A Reliable Predictor of Draft Slaws'?"' Juurnul SlnlflQ,rh ({nd
Mirabile. :V'LP. "'Intelligence and Football: Testing for Differentials in Quarterback Passing Performance and NFL Compensation>" The ,)f:mrt Juurnal 8, no.2 (2005), A vaiJable Online fi'om http: Vy'\vw.thesportjoumal.org;2005JournaliVoI8-No2fmac-mirabile.asp. Accessed 5 November 2008.
KumitL Fe and AJ /\dams. "The NFL Combine: Does it predict performance in the National Football Leagut'.'· Journal (fSrri!ngfh and R(;sl!arch 2l no. 6. (Nov< 2008): 1721-1727 25 discovers there is no statistically significant correlation between Wonderlic scores and
NFL compensation for quarterbacks draHed from 1989_2004.44 This study shows that
NFL teams may not value the additional information from the Wonderlic Test, a part of the Combine. Kuzmits and Adams (2008) attempt to answer the question of whether the
NFL Combine accurately predicts performance. Their study tinds no significant correlation between Combine performance and NFL performance, with the exception of the 40-yard sprint tests lor running backs.45 'Ibis finding is consistent with Terry's (2007) study, which evaluates the impact of the three most tested abilities at the Combine'S
(speed, strength. and intelligence) on team success. Team average for speed is the only one of the three found to significantly, positively correlated to winning percentage. These studies suggest that among the various tests at the Combine. speed tests suspected to be the only tests that relate to future productivity
As the visible differences between top players are becoming marginal, the
Combine has received increasing attention from both the media and NFL teams. The NFL is unique to its professional league counterparts because ofthe emphasis it puts on physical test results. Consequently. the literature has shown the possibly that unlike the
NBA and MLB, the NFL is not making the most efficient decisions with their draft choices. There is irony in this observation because the NFL appears to collect the most data of the three leagues. Potential draft prospects are required to spend a minimum of
three veal's¥ in college""'-' before becoming'- elioibleb Jor the draft and all attend the Combine
,>,. Mirabile, M,P. The ,""'port Journal X, 00.2 (2005t
45 Kumitz. FT'. and AJ Adams. \721-1727.
,j{, National FootbaH Leauge. VFr Rule Book. Available online from http' ww\\ .nn.cOlTI-'ruh:hook. Accessed 5 ;\lovcmber 2008. 26 or perform at a Pro Day47 The literature on the NFL draft leads one to wonder whether the NFL is possibly collecting too much additional data that is not adding to its utility.
The current literature on the NFL Draft and Combine provoke this study to ask the following questions; what value does the NFL Combine have? And is the Combine a possible cause of the noted inefficiencies in the decision making of NFL teams on their drafi picks? The next chapter combines the theories presented in this chapter in ordcr build a theoretical framework for this study's approach to answering the above questions.
Each college holds a private workuut for Nl"L teams where prospects perform the same physical tests they would at the combine, This is known as a Pro Day, CHAPTER III
THEORY
More infomlation is expected to lead to an increase in predictive power. An
increase in predictive power results in an increase in confidence towards decisions. If the
additional information is only believed to increase predictive power and does not do so in
fact, then the increase in confidence is not warranted. Therefore, confidence in decisions
based on contidence in additional information may lead to overvaluation. This can result
in a decrease in the utility received from decisions, making the decision-making
ineflicient.
In the case of the NFL Draft, there is an abundance of evidence suggesting that
the NFL may be overvaluing draft picks and making decisions based on the additional
information that may not be an accurate reflection of a player's true productivity
potential. This study attempts to combine job market signaling theories with theories
from previous studies on the NFL draft to investigate the informative value of the NFL
Combine. The underlying questions to be evaluated arc; (1) whaf is fhe iniiJrmafional value o/Ihe Nn. Combine? (2) I/fhe Combine has an in/imnative value, is fhe intimnafion posiliveiy afjeeling rhe draft decisions o/;VFL teams}
There are some assumptions in the theory orthis study. First it is assumed that players are looking to provide just enough inli:lnmllion to maximize the perception of 28 their performance potential and owners are looking to gain as much information as possible about players. As a result, owners utilize the four days at the Combine to inquire about the information that they feci they need to verify. The workout tests provide information on players' size, athleticism, and speed. It is assumed players who are already considered to be the best player available in a given year do not have much incentive to participate in these tests because there is nothing to be gained. In these situations players may hold out of participation in fear of harming their status for whatever reasons, such as they may have an injury or they know they are not as fast as some think. Therefore the lack of participation in Combine tests is assumed to be informative.
It is also assumed that teams can accurately assess their need for a particular position. Therefore instead of investigating whether the decision to draft the best available running back is better than the decision to draft the best available quarterback, this study assumes that a team has already decided which position they will target with each draft pick allotted to them. Theretore this study is only investigating whether a running back is better than the running backs taken after him. For example, Team (A) drafis the following positions in each round:
Round J - Running Back Round 2 Quarterback Round 3 Corner Back Round 4 .... Linebacker
The assumption is that Team A has already decided their order of priority. This means regardless of the ability of the best players available at other positions. Team A will only draft a running back in the tirst round, a quarterback in the second round, and so on. 29
In some years there may be a higher need for quarterbacks and in others more of a need for running backs. If a model tries to evaluate the productivity of all draft picks against eaeh other, it may run the risk of incorporating the variance in value of a respective position for teams in that year. For example, supposc running back (A) is the tirst running back taken Draft Year I and is the first overall pick. Running back (B) is the tirst running back taken in Draft Year 2, but was (he ninth overall pick. A model incorporating overall draft position would expect the productivity of (A) > productivity of
(B), despite the filct both are considered (0 be the best running back in their respective year. It could be possible that (B) is better than (A) but the need for a running back in
Draft Year 2 was not as high as in Draft Year I. In this case teams are willing to wait for later picks and spend their money on the positions with a higher need. Therefore this study takes the approach of measuring a player's productivity only relative (0 others drafted in his position and draft class.
The hypothesis of this study states that if NFL Combine performance does hold an informational value, i( is only a signaling value. This means that the information from the NFL Combine is only being used to separate between players in respective positions and draft classes and not to predict the future productivity statistics of a particular player.
Since it has already been ShO\vl1 that Combine performance does not correlate with NFL productivity, this study makes the assumption that teams are only using the Combine to aid them in linding the best available lor a given position in that year.
It is proposed that two requirements are necessary for the Combine to be considered a productive signal; (1) there needs ro be a significant relationship between dra,!! position and ('umbine perjiJrmance; and (2) Ihe relationship hetween ;VFL 30 productivity and the available infiJrmation on performance needs to be stronger with the infimnationfiwn the Combine {han wilhout the inf(JrmationfTom (he Combine. The first requirement (1) is necessary to support the Combine as a sif,,'l1al. The second requirement
(2) is necessary to suggest the Combine has a positive inlluence on draft decisions.
FIGURE 3.1
NFL Signaling Feedback Loop
NFL Teams' belid's about Drallcd players as a thc relationship between function of observed ObSl.'fVcd information and Intixmation productivity --+ i + Teams observation of Collegiate player'~ rmlfgi!IJ.! prodlll.::trvity ;n dcdsion$ towards rdation to observed J-- obtaining the positive Inf()rmatiol1 information and entering the draft
Draft position is only expected to indicate that a player's expected productivity is greater than those in his same position dratled after him in the same year. Consider the previous example of Running Backs (A) and (B). The expected productivity of (A) only needs to he greater than those running backs taken after him in that draft year. and the same is true for (B). If(A) and (B) show performance consistent with what is expected, then regardless of their overall NFL productivity statistics. prior draft beliefs will match observed productivity. This would indicate the presence of the separating equilibrium in which the feedback loopl is completed (Figure 3.1). The presence of a separating 31 equilibrium is a main indicator for the presence of signaling. When observed productivity does not match NFL Team's beliefs toward the observed information then beliefs should be adjusted. This means if Combine performance does not appear to correctly separate players into accurate expected productivity levels, then Combine performance should not relate to the draft position of players.
Reg uirement (J ): Significant Relationship Between Draft Position and Combine Performance
Studies have already suggested the Combine is not a predictor of future success 2
One study is able to use Combine performance in a given year to perfectly predict the dratl round tor Wide Receivers, Running Backs, and Defensive Backs3 This evidence indicates performance at the Combine could be a major influence on NFL teams' draft decisions despite its lack of ellectiveness as a measurement of future productivity statistics. Considering all of the money, energy, and time NFL teams put into their draft decisions it is expected that NFL teams to know Combine performance does not predict
NFL perfonnance. This is why it is suspected that instead of using the Combine as a predictor, teams arc using performance at the Combine to disprove or confirm their beliefs about players. In this case, a player's Combine performance only needs to be at a general level NFL teams need to see based on what they already know. For the player's who have statistics that separate them from everyone else Combine performance is not as
i Spence, Michael. "Job Market Signaling," QW.1r!er(y journal CC<)nOInI"S 87, no, 3 (1973) :
:' Kumitz and Adams, 1721-1727.
KJ and L~ BurketL "The National Football Combine: A Reliable Predictor of Draft Slatus'?" JDurnal and Research 17, no. 1 (Feb. 2003): 6-1 L critical. But in situations where statistics can't separate players there is more pressure to perform at the Combine. There is an expected difference between the Combine performance from thosc taken earlier in the draft and the Combine performance of those taken later. This difference can be analyzed in two ways; either individually within respective positions or by groups based on draft round within respective positions.
Analysis of Combine performance by individual position means if Running Back
(A) is drafted before Running Back (B) in the same draft year, then in order for the
Combine to have a signaling value generally, [he Combine performance jor player (A) needs 10 be greater than Combine perji!rmance oj'player (B) (Figure 3.2). This approach would suggest the highest signaling value for Combine performance statistics because it creates the greatest value for teams. Teams would be able to determine exactly which individual player is better than the others available.
FIGURE 3.2
Signaling Model for Individual Draft Ranking
A general model for what should be relatively observed if Combine performance separates players on an individual level. The data does not have to reflect this model exactly. The concept is that there is a negative linear relationship between individual draft position and Combine performance,
A ,• R ." C ~ ,
Draft Rank (Individual) 33
The alternative approach is to group players in respective positions by draft round. In this approach in order for the Combine to be considered a signal the Combine per/iJrmance olplayers (A) and (B) who are taken in thefirst round need\" to be greater than the perj(Jrmance olplayers C and D who are taken in a roundfollowing thefirsi.
FlGURE 3.3
Signaling Model for Draft Round
A general model for what should be relatively observed if Combine performance separates players by round. The concept is based on the idea that there should be some amount of performance difference when observing the performance of players drafted in the different rounds. There does not have to be a negative correlation between individual draft position and Combine performance. Most likely there will be a negative correlation between the average Combine performance ofthose in each draft round group and draft round.
Draft Position (1 st = 1 ~t Round)
It does not matter if A is chosen first bill the Combine performance of (B) > (A). All that is necessary is the Combine performance or(A) > (el and (D). For example, in a given year there are six running backs taken in the draft (Two are selected in the first round (A and B), two in the second (C and D), and two in the third (E and F). If Combine 34 performance is a signal, then the performance of players (A) and (B) (performance level
"1'3" in Figure 3,3) needs to be greater than the performance of players (C) and (D)
(perf()fmance level "P2" in Figure 33), The performance of players (C) and (D) needs to be greater than the performance of players eE) and (F) (performance level "PI" in Figure
3.3). In this case the distribution ofdratl choices when comparing Combine performance with draft round is expected to be similar to Figure 3.3 below. In this approach it is assum<:d NFL teams are using Combine performance only to place players into groups based on the round in which they should be drafted,
In summary, a negative relationship between individual Combine performance and individual draft position or a negative relationship between collective Combine performance and draft round will provide evidence in favor of the presence of a separating equilibrium within the NFL Draft. This would support the Combine as a signal.
Requirement (2): Relationship Between NFL Productivitv and Available Information
After investigating the impact of Combine performance on draft order, if
Combine performance is found to signifkantly relate a player's draft position the next step is to investigate whether this impact is positive or negative. For the information from the Combine to be considered to positively affect decisions, teams need to be at least as well off as they would be without the information lrom the Combine. In a perfectly ef1iden! equilibrium within a dratl where the Combine affects draft position the following would be expected to be true: 35
Pre-Draft Ranking" Draf! Ranking .... PerfiJrmance Ranking
I f this were the case, the top pre-draft performing running back would also be the top drafted running back and therefore the most productive NFL running back trom his draft class. Casual knowledge and observation of the NFL shows this does not perfectly occur.
Massey (2005) has already shown that early draft picks arc not the most cost effective picks 5 Based on this finding one might think that tools such as the Combine are making teams worse off. But Massey's study answers the question of whether top draft picks arc overvalued, not the question of whether they are over evaluated in terms of their ability relative to their draft class. This is an important distinction when evaluating the market value of draft picks (Figure 3.4).
FIGURE 3.4
NFL Draft Pick Market Value Curve6
Draft Order
; Pre-Draft Ranking How a player ranks prior to the Draft based on observed statistics and Combine performance.
) Massey. Casey and Richard H. rha!eL "Overconfidence vs, Market Efficiency in the National Football League." iVaffona! Bureau Resl!af('h \Vorking Paper NO.\Vl i270 (April 2005). Available online at hHP:i'iwww.nber.org. Accessed 1 October 2008 '.L"G·'. Casey and Richard 1--1, rhaler. "Overconfidence VS. Market r::ffickncy in the National Football League," XatiDf1al Burl!au Research Working Paper No,Wl12iO (April 2005). Availabh: online at http: \vww.nbeLorg, Accessed j October 2008. 36
When taking into account the amount of money paid to draft picks, an early draft pick involves much more risk when compared to a later draft pick. Typically early draft picks are from top Division IA programs and have been in the media spotlight. Later draft picks tcnd to be less known players from programs that have not garnered much media attention. Generally more information is available about those taken early in the draft and
NFL teams mare more familiar with their name, personality, and playing style. A part of the market value difference may be a result of a team's conlidence in early picks and lack of conlidence in later picks. Often, laier picks leave the league and do not become stars.
Therefore in theory, the excess in pay above the surplus value may be a premium a team is willing to pay to secure a player in which they are familiar and comfortable. While the value of the a later pick may be more than the pay they receive, an owner may rather pay them less, and wait to see what they do, in order to pay more for the player they do believe will be successful. So if those taken high in the draft statistically stand out above those later in the draft, the excessive pay may reHeet the premium a team is willing to pay for the security they feel with high draft picks. This may be justified if it is shown that the information available to teams is significantly different for the top drafted players as opposed to later draft picks. One explanation may be in collegiate statistical performance or. when that remains similar among those entered in the draft, then Combine perforn1anee may be the statistical difference.
If Combine performance is pointing teams in a direction that is not a more accurate evaluation of a player's ability than if they were to solely analyze all other available information without the Combine statistics, then the excessive pay may be compensating for an irrational over confidence. Thus in the second step of trying to 37 detcrmine whether Combine performance has a positive affect on decisions in the NFL draft, the data needs to be analyzed with and without Combine performance information to see if the data leads to a diHerent draft ranking order solely based on the other observed statistics (college perfoDnance, size, etc.). If the Combine is helping tcams, then the theory behind the equation in Figure 3.5 should be true for most players drafted.
FIGURE 3.5
NFL Performance vs. Combine Performance and Statistical Performance llthe Combine is helping NF1~ Teams make a good decision about a player then for each player IhejiJ/lowing should be true:
Performance Rankingl I Performance Ranking! II ,,·---···---:--······~.l 51 - ~;-.-."--"-.-, Pre - Draft Rankmg * I i Stalistlcal Rankmg I
*Pre-Draft Ranking is how a player ranks against his peers in the same draft year and position, given the available information including the Combine. Rankings arc explained further in Chapter IV.
If a player's Combine performance and statistical performance (Pre-Drat!
Ranking) is a perfectly accurate ref1ection ofa player's performance relative to their draft
! f . \ class thcn : Pcr ormanc" Ranklllgi Cc 1. The same is true when analyzing statistical ranking. " Pre - Drafl Ranking)
When trying to compare whether the addition of Combine performance creates a more accurate ref1ection of a prospect's performance potential within his draft class, which ever is closest to J will have an absolute value that is the lowest. In this theory, Combine performance does not have to be a perfect predictor of performance. its addition to the observed information just needs to be a more related to future performance than the ini()rmation without the Combine. Thcrdi.m: it may be possibJe that Combine 38 performance is a bad indicator of future performance. but as long as it is creating a closer indication of NFL productivity potential relative to a players' drafl class then Combine performance ean be seen as a positive signaL
Every draft pick allotted in the draft results in the selection of a player. Regardless of whether a team trades the pick away or not. a player must be selected. This means that even when the talent is low, a team is going to expend money and endure an amount of risk on a rookie player. Therefore it makes sense that a team would try only to distinguish who is the best available in the draft class.
In this study, a player'S perfonnance is measured only by how he perfonns relative to others in his position and draft class. This is similar to the job interview process where an employer tries to use the infonnation available to find the best available applicant. An employer does not necessarily predict how the applicant will perform in the future; they just want 10 know who is the best they can hire current. The first step in applying this theory to the NFL Draft is to examine the Combine's relationship to the
Draft. The next step is to examine the relationship between NFL performance and the available information including the Combine and excluding the Combine. This theory is tested and analyzed in the proceeding chapters by specifically examining the following:
• Whether Combine performance has a significant correlation with individual draft position or draft round
o The Combine needs' 10 have a signt/lean! correlation to draft position or draft round in order 10 be considered a signal
• Whether NFL performance has a stronger relationship with the combination of Combine and final year collegiate perionnance than with final year collegiate performance alone.
o Ihe relationvhip hetvreen .vI:1. perfhrmance and fhe comninafion qlCumbine pe~R)rmance CHAPTER IV
DATA AND METHODS
The Data
The data collected for this analysis includes Combine perfonnance, senior year collegiate statistics, and NFL overall career statistics for drafted players from eight
"skill,,1 positions (Quarterbacks, Running Backs, Wide Receivers, Tight Ends, Defensive
Backs, Linebackers. Safeties. and Defensive Ends). Each player is categorized by the position for which they were drafted and not the position they played in college. Data is collected from 1999 to 2005 for ofTensive players and li'om 2002 to 2005 for defensive players. The data is only collected up to 2005 to account for the average career length for a NFL player, which is 3.5 years 2 Typically a player who has not collected NFL statistics aftcr four years will no longer be in the league. Therefore players drafted in 2005 have had a full opportunity to gain NFL playing experience. A player who has had less than
3.5 years of experience may still be developing and could gain a chance to play in their fourth season. NFL performance information is gathered from the NFL's official site,
NFL.com. All major career statistics listed for players arc used in this analysis.
Skill position is a position that is not considered to be a lineman,
'iFF Association "NFL FAQs." nfh,ia' ef).com A(cessed 5 Novcm.bef 2008. 40
Combine perfonnance statistics include Height, Weight, sprint tests (10 yards, 20
yards, and 40 yards), Vertical Jump, Bench Press, Broad Jump, Shuttle Test, the Three
Cone Test, and the Wonderlic Personality Test Crable 4, 1)3 Combine performance also
includes test scores from a player's collegiate Pro Day, In the event that a player
participated in the Combine and a Pro Day, their best scores are taken. All Combine data
is collected trom the same website, NFLDraftScouLcom, in order to maintain
consistency, All individual and overall Combine perfonnance variables are expected to
negatively correlate with draft order. This means the earlier a player is drafted the higher
his Combine scores,
Senior year collegiate performance is collected trom Sports Illustrated4 and/or a
players' respective collegiate athletics website, Each position has slightly different
measures of productivity, Thcrefore the collegiate statistics variables collected vary for
each position Cfable 4, I), A player is omitted if senior year collegiate statistics arc not
available, A player is also omitted if their statistics from college cannot reneet any part of
the position for which they were drafted, This occurs when a player is drafted for a
position they may have never played before (i,e, a quarterback drafted as a defensive
back), All individual and overall senior year collegiate performance variables arc also
expected to negatively correlate with draft order.
Overall. defensive backs and linebackers are the most frequent positions picked in the drall, There are on average 39,5 Defensive Backs and thirty Linebackers dralled each year. Conversely. quarterbacks and safeties are the least drafted players, Typically an average of 9,7 quarterbacks and eight saleties are drafted per year. It is more difficult to
. Sec Appendh B tor
1 Sports Illustrated. http: sportsillustratcd.cnn.comi 41 stand out in a large group. Therefore there is more pressure to perform at the Combine tor positions with a large prospect pool. This observation is exemplified within the results of this analysis.
TABLE 4.1
Individual Test Performance and Statistics Variables
Combine Performance Variables ,I ' , " ""Vari,l!.,I. ' " ,,' I AbbreY,ati()il P(jslti.dnsADDli\i.abie Height ,I Ht . ALL Weight I Wt ALL I 40 yard dash I 40yd ALL I 20 yard dash I 20yd ALL I 10 yard dash . . IOvd ALL __ I I .~ ~ Vertical Jump VJ ALL Bench ALL Bench Press ---i ~rOadJUmp BJ ALL I Shuttle Shut ALL "-,-"-"~-----. !3 Cone Drill Cone ALL I' Wooderlie Wndr ALL Individual Final Year Collegiate Statistical Variables· Varulble I AbbreVlatfOu POSlti The Methodology The method used in this analysis differs from previous studies because it utilizes non-parametric data analysis. Instcad of a regression or regular correlation analysis, this study uses a Spearman Rank Correlation analysis to investigate the relationship Combine and collegiate performances have with draft order and NFL performance. A Spearman Rank Correlation analysis is the same as a Pearson Correlation analysis with one exception. In a Spearman Rank Correlation the collected data is convcrted into rankings. The rankings are applied to a Pearson Correlation model to produce a coef1icient. This coefficient is known as the Spearman Rank Correlation cocftlcient. Thc critical valucs lor a Spearman Rank Correlation are the same as a Pearson Correlation. Therefore the only ditTercncc between a Pearson Correlation and a Spearman Rank Correlation is the act of converting the data to rankings. FIGURE 4.1 Spearman Ranking Example Overall Drat! Player Drat! Rank Test I Score Test 1 Rank Selection # A 5 I 5 I B 14 2 10 4 C 27 3 8 2.5 D 41 4 8 2.5 * Drafl position ranked is in ascending order and test scores arc ranked in descending order. For tied test scores, players are each assigned a rank equal to the average of the respective ranks. First. all of the collected data is ranked. Draft order is ranked in ascending order. Therefore the first player taken for a given position in 11 given draft class has a draft rank 43 equal to J and the lasl player taken has a draft rank equal to 11; where 11 is equal to number of players drafted for that position in that draft class. All of the performance statistics are ranked in descending order. The worst performance in a particular category is given a rank of I, and the best performance is given a rank of 11. In the case of a tie between players the ranks arc averaged. Figure 4.1 provides an example of how the data is ranked. After ranking all of the data, to derive the Spearman Rank Correlation coefficient, a Pearson Correlation is run between thc rankings of two variables. Thc coef1icient is a measure of the relationship between the two variables known in the model as (,) and (y). Every Combine and collegiate performance variable is run through a correlation individually against draft position. The same variables are also put through a correlation individually against NFl. perlormance. Figure 4.2 shows the Pearson Correlation equation used to derive the correlation coef1icients for the data FIGURE 4.2 Spearman Rank Correlation Equation (Same as Pearson Correlation Equution) ~::CX; - x)(y, - y) ,·1 (n -l)apy (x) mean ranking for the observed variab1c* (x,) rank assigned to player for observed variable< * (y,) nmk assigne,d to player fl)r observed variable (y) mean ranking for all those drafted who the observed variable (aJ standard deviation of the ranking:; for variable (Xi (CT,,-) standard deviation of the rankings for variable f)} tl)idl number of observed in the DO';Ili(m and draft class 44 To understand how the rankings tIt into the equation consider an example of a correlation between individual draft rank and the 40-yard dash. In this case (x, - x) is equal to the draft rank of a given player (x,), minus the mean draft rank of all players taken from that position in that year (x). Further. (y, - y) is equal to the player's rank in the 40-yard dash (y,) minus the mean rank of all players for that position in that draft year who ran a 40-yard dash (y). The (o-x) and (0-,) represent the standard deviation of the draft ranks and 40-yard dash ranks, respectively. Finally, (n) represents the total number of players dralled for the respective position in the respective draft year. The section below explains the ranking and variables more specitlcally. Combine and Coilege Perj(Jrmance Ranking Me/hod There are three major performance categories; Combine performance, statistical performance, and total performance. When deriving individual Combine test ranks, each player is given a rank in each individual test and measurement Cfable 4.1). The highest rank is given to the player with the best performance in the test and the worst performance is given a rank of I. Players are given a rank of zero when there is no available data on their perfom1ance in a test. This is a result of a methodological assumption. It is assumed that the lack of o Participation adds nothinb to a plaver's.' draft status therefore thev~' are boiven a score of zero. It is possihle a player performed the test behind closed doors, there may be an error in the records, or a team may have informed the player he does not need to perform that test. Each case is difficult to track and results in an immeasurable benefit for the player. 45 Since each case could be dilferent. for consistency, it is assumed non-participation in a test cannot add to a player's overall ranking. Players are given a ranking for their overall Combine performance. These variables are referred to as the Combine Rank Sum and Combine Rank (Table 4.2). Every player's Combine test ranks are summed together to create their overall Combine Rank Sum. Based on his Combine Rank Sum, a player is given an overall Combine Rank. TABLE 4.2 Overall Combine Performance and Overall Final Year Collegiate Performance Variables Combine Rank Sum A single overall rank based Overall Combine Rank on the player's ComRkSum Statistical Rank Sum Overall Statistical Rank Sum of a player's StatRkSum Total Rank Sum and ComRkSum Sum of all individual NFL NFL RankSum statistical variable ranks A single overall rank based Overall NFL Rank NF'LRk on the player's NFLRkSum The position a player is taken [}raft Rank IJrllftRk in the draft relative to others taken in that position. fhe draft round in \vhich a [}raft Round player is drafted 46 For the eight positions, a Combine Rank Average is calculated for each round. Combine Rank Average is used to evaluate the relationship between draft round and overall Combine performance. For eaeh draft class in the each position, all individual Combine performance variables and overall Combine performance variables are put through a correlation against Draft Rank. Combine Rank Average is run through a correlation against Draft Round for each position. All coefficients are put to a two-tier significance test against the Spearman Rank Correlation critical value at 95% confidence. FIGURE 4.3 Overall Rankings Sum Calculation Example (Applies 10 Combine Rank Sum and Sial Rank Sum) Example of Combine/Stat/NFL Rank Sum Calculation Combine/Stat/NFL Rank Sum = rankivariablc I + rankvariable2 +rankvariable3 ".rankvariablex Example of Total Rank Sum Calculation Total Rank Sum= Combine Rank Sum + Stat Rank Sum Statistical perfonnance ranking is calculated using the same method as Combine ranking. The process for ranking players for their performance in individual statistical variables is the same as the process lor ranking them for individual Combine performance variables. The overall tinal year collegiate performance variables, Stat Rank Sum and Stat Rank, arc derived using the exact same method used to derive the overall Combine performance variables. Similar to the Combine. a Statistical Rank Average is calculated Ii)!' each round in each position. Ali players reviewed have statistics lor at 47 least a portion of the major statistical categories for their assigned position. Since each player is categorized to the position for which they were drafted and not the position they played in college, there are some instances where a player's college statistics do not match entirely with the statistical categories for a position. For example a Defensive End that is moved to a Linebacker in the NFL may not have interception statistics. It is assumed that NFL owners take into account the lack of statistics that match up. Therefore unlike Combine ranking, in the absence of performance players are still given a rank. Statistical Rank Sum, overall Statistical Rank, and Statistical Rank Average are put through the same Pearson Correlation process as Combine Rank Sum, overall Combine Rank, and Combine Rank Average Cfable 4.2). Every player is also given an overall performance ranking. A player's overall perti)rmance ranking is calculated by adding together Combine Rank Sum and Statistical Rank Sum. This ranking is referred to as the Total Rank Sum (Figure 4.2). This variable is also put through a correlation with Draft Rank to determine how well Combine performance and Collegiate performance combined together relate with draft order. All Combine and statistical pertormance variables, including overall perfonnance, are expected to negatively correlate with draft order. Therefore it is expected that those taken carlier in the draft have higher performance rankings than those taken later in the draft. /vF'L Performance Ranking ;'vie/hod The method used lor ranking NFL periormance is the same method used to rank Combine performance and collegiate perfonnance. Each player is given a ranking in the major statistical categories for their position. 'rhc difference is in the variables used for 48 the correlations. Only two NFL performanee variables arc used; NFL Rank Sum and NFL Rank Cfable 4.2). NFL Rank Sum is a sum a player's ranking in the major NFL statistical categories (Figure 4.2). NFL Rank is a rank given based on the Total NFL Rank Sum. Total NFL Rank Sum and NFL Rank are each run through a correlation against draft rank. All Combine and final year collegiate performance statistics are run through a correlation against NFL Rank to examine how they relate to NFL productivity. All NFL pertormance variables are also expected to negatively correlate with drat!. The next chapter analyzes the results from use this method to examine the relationships within the data. CHAPTER V RESULTS In order for the Combine to be considered a productive signal two trends must be present in the results; (1 j Ihere needs 10 be a significanl relationship be/ween drafi position and Combine performance; and (2j the relationship between NFL productivity and Ihe available infiJrmation on perjiJrmance needs to be stronger with the inclusion of Combine performance than withoulthe inji)rmationfiwn Ihe Combine. The first (1) is a necessary for considering the Combine as signaling institution. The second (2) is necessary to suggest the additional information from the Combine has a positive influence on draft decisions. The relationship between Combine performance and the draft position is evaluated in two ways. The first method is to define draft position for each player as his individual DraH Rank. 1 In this method, each variable is individually put through a Spearman Rank Correlation twice; first against individual Draft Rank and then against NFl. Performance. The second method is to define draH position for each player as his Draft Round2 In this method, players are not grouped together by their respective position and drall i Indl'vidual Draft Rank vvhere a player is drafted rdative to others in their position and draft class whether a is the first back, second back, etc.). 2 Dratl Round the round in vvhich a player was drafted 49 50 class,} Instead players are grouped by their respective position and draft round, For every position individually, eaeh round is assigned an average Combine performance rank and an average linal year collegiate performancc rank, These ranks are calculated by averaging thc Combinc performance ranks of all those taken lor a specific position in thc respective draft round, For each position there are two Speannan Rank Correlations run, One correlation is run betwcen Draft Round and average Combine performance, A second correlation is run between Draft Round and average final year collegiate performance, In summary, individual Draft Rank is an evaluation of individual players' Combine performance compared to where they were taken in the draft relative to others in the position, Draft round is an evaluation of the overall Combine performance of those in a specific position, taken in a specific round, Combine performance only significantly relates to draft position when draft position is defined as a player's Draft Round, This is only lound lor three of the eight positions evaluated; these include Running Backs, Linebackers, and Defensive Backs, Alternatively, lor five of the eight positions final year collegiate performance signillcantly relates to draft round; Defensive Ends, Linebackers, Tight Ends, Running Backs, and Wide Receivers, Therefore the results suggest that for at least three NFL positions, the Combine is a signal, Evaluation by individual drati rank provides evidence to support the Combine as a positive influence on draft decisions, The combination of results irom the analyses of both individual dran rank and draH round provide results thut fulfill the requirements necessary to consider the as Combine a positive signal, Draft Class the year in \vhich a player was drafted 51 Results of Analysis by Individual Draft Rank The results are presented separately for each otlensive and defensive position. The majority of Spearman Rank Correlation coefficients are negative, indicating an overall general pattern of deteriorating performance the later a player is drafted. The variables under "Draft Rank Correlations Coefficients" section of the results tables are individually correlated against individual Draft Rank. The variables under the "NFL Performance Correlation Coetlicients" section of the results tables are individually correlated against individual NFL Rank. 4 All correlation coel1icients that are significant are in bold and are highlighted in gray. Significance is based on the two-tail Spearman Rank Correlation critical value at 95% contidence for the given number of players in the draft class. Delensive Backs Overall there is no consistent relationship between draft rank and overall Combine performance. Additionally, there is no indication of a relationship between collegiate performance and individual draft rank. The 40-yard dash and lO-yard dash' are the only Combine or collegiate performance variables that have a significant correlation with draft rank in more than one year. Alternatively, the variable combining Combine performance and collegiate performance, named Total Rank Sum, is significantly correlated with draft rank in each of the last three draft classes observed. This is an indication that when observed >1 NFL Rank how a player's NFL productivity ranks in comparison to those dratted in his same position and year. " Abbreviated in all tables as and -.' 1 (, Abbrt'viakd in all tables as "TotRkSum," 52 individually, neither Combine nor collegiate performances have much of a relationship with draft decisions. But when evaluated with each other there is some level relation to draft decisions. TABLE 5.1 Spearman Rank Correlation Coefficients for Defensive Backs' These are the coefficients that resulted from each individual correlation. Under "Draft Rank Correlation Coefficients." these coefficients represent the listed variable's relationship to Draft Rank in each of the draft classes observed. Under "NFL Performance Coefficients," these coefficients represent the listed variable's relationship to NFL performance in each of the draft classes observed. *Coe/ficients il1 boM and gray are significant hased on the critical value of a two-fail test with 95% confidence. Critical values available n'om: Olds. E.G. "Distribution of Sums of Squares of Rank Differences for Small Samples." Annals (~lAf(.l!hemalic,,1 Statistics' 9, no. 2 (1938). The variables measuring NFL performance, named NFL Rank Sum and NFL Rank. are significantly correlated witb draft rank for every observed draft class. This shows a general trend of declining perfonnancc the later a player is dralied. The moderate correlation indicates this trend is not as consistent as would be expected. This Abbreviated in all tables as "NFl.RkSum" and "NFLRk" 53 may be a reason for skepticism towards draft decisions. Despite the moderate correlation between NFL perfi)rmance and Draft Rank, there is not a collegiate performance variable that has a stronger correlation with NFL performance than Draft Rank. Therefore there is nothing suggesting that the available information on performance without Combine performance would lead to better decisions. For Defensive Backs, it appears that if Combine performance is influencing draft decisions this influence is positive. This observation in the results appears consistently in the analyses of multiple positions. Linehackers Neither Combine performance nor final year collegiate performance has a significant relationship with individual draft rank consistently. There arc only two pcrf()rmance variables that significantly correlate with draft rank in more than one draft class. The lirst is the 40-yard dash and the second is Tackles for a Loss. S Although each variablc has a significant correlation with draft rank for two draft classes, none of these classes arc consecutive. Despite thc lack of a consistent relationship between performance and Draft Rank, the 2005 draft class provides evidence suggesting the Combine positively relates to draft decisions. For the 1005 draft class the variables representing overall Combine performance, 'I Combine Rank Sum and Combine Rank ~ sitmificantlv'-"~' correlate with Draft Rank . For the same draft class, the variables representing overall coliegiate performance, named Stat k Abbreviated in all tables as " Abbreviated in all tables as "ComRkSum" and "ComRk" 54 Rank Sum and Stat Rank, 10 signilicantly correlate with Draft Rank. Additionally Total Rank Sum also significantly correlates with Draft Rank. In this draft class Combine and collegiate information appear to relate to draft decisions both individually and together. TABLE 5.2 Spearman Rank Correlation Coefficients for Linebackers' UNEIl\(,KERS Yr i n I .:lOyd nasH ~ioi°;:k! C~~~k S~~t~k:1 ComRk I StatRk ) Nl;LRkSum / .'iFLRk I ~O(j~ i ~3 i D.3:S ,.0.118 ·0156 ·00J5 .oi37,1 ·0037 .1 ·(j254 I ·0.404 -0.392 I i ..:.oO.J i _4 , -iLf) ; ~(}.499 -(U76 -tUM -(U)70 __ ~~ 126 I -0.112 ·0.304 ..0.479 r20()4-+-31t -0.312 j -0277 -f).20u +0.118 I +0,240 -0_l32 -0,153 -0.535 -0.497 2(J05 J5 I ·(j.O"9 I .0.404 I ·0.438 ·0,380 I -0.343 ·0.386 I ·0.367 ·0.702 -0.714 NFL I'ERFOlL\fANCE CORRELA nON i y: ! 40yd I Tkh'(-) I T~)lRk ComRk StatRk i C Rk StatRk) I r I n i i Sum Sum Sum I ,om , :-~'2;)O2l23-1 0,109 i -(1-294 I -G,33} -()_249~(~-~-0.335 ~o~'~~--l i iOii3T 29 j'0004 i ·0.427 I ·Ol3S ·(1191 004] . ·0180 0.066 ...... j 1''2004:;1 !'014! ·OJ44 1':()0~04J ! ·0198 '''0.050 '" I ·lil70 . t i 005 1 ]5:()~i I -O.33iT:OJ28 J .O'32oj:;;2iJi"r----':033i"'''[ ·0.168 -: These are the coefficlents that resulted from each mdlVldual correlation. Under "Dran Rank CorrelatlOn Coefficients," these coefficients represent the listed variable's relationship to Draft Rank in each of the draft classes observed. Undt..'f "NFL Performance Coefficients,"' these coefficients represent the listed variable's relationship to NFL performance in each of the draft classes observed. 'Coefficients in bold and gray lire significant based on fhe critical value of a two-tal/ test witlt 95% confidence. Critical values available Ii'om: Olds, E.G. "Distribution OfSUlllS of Squares of Rank Differences for Small Samples." Annals (fl.vfalhemalicaiS~tatistics9.no. 2 (1938). Consequently. NFL performance has the strongest correlation with draft rank for the 2005 drafl class. Based on this result it may be assumed that overall, NFL teams made good draft decisions about linebackers in 2005. Therefore, despite the apparent of lack of ability to individually predict NFL perlonnance. the utilization of both Combine and Abbreviated in all tables "StatRkSum" and "StatRk" 55 college performance together appears to have aided in producing the best overall draft decisions. This could be considered additional evidence suggesting that Combine performance positively inf1uences decisions. Sajelies One of the di fllculties with Safeties is the small population size. The largest number of safeties observed in any draft class is cleven. This may be a result of classi(ying each player's position as they are classified in the draft on the official NFL website. The safety position in many views is very close to a defensive back position. It could be possible that tor many collegiate safeties their best NFL potential position was unknown and therefore were classified as a defensive back at the time of the draft TABLE 5.3 Spearman Rank Correlation Coefficients for Safeties* SAFE lIES Tot n I"T StatRk NFLRk I NFLRk Tkls Sum Coefficients," these coefficients represent the listed variable's relationship to Draft Rank in each of the druft classes observ-ed. Under "?\lFL Perfonnance CoefIkients," these cOGfficienl<:; represent the listed variable' 5 relationship to NFL performance in each of the draft classes observed. *Coefficients in bolt! and gray afe significant based 011 the critical value of a two-tail test with 95~1j confidence. Critical v:tlucs available from: Oids, E,G, "Distribution of Sums f.)f of Rank Differences for Small Samples," Annals Atmhcmatica! .\!atistics 9, no . .2 (1938). 56 Defensive Ends Draft Rank for Defensive Ends also lacks a consistcnt correlation with either Combine performance or final year collegiate performance. Although 'rackles for a Loss and Sacks both significantly correlate with draft rank lor two draft classes. neither of these drati classes are consistent. TABLE 5.4 Spearman Rank Correlation Cocftlcicnts lor Defensive Ends* are carre Coefficients," these coefficients represent the listed variable's relationship to Draft Rank in each of the drat! classes observed. Under -'NFL PerfOimance Coefficients," these coefficients represent the listed variable's relationship to NFL performance in each of the draft classes observed. *Coe/ficients in bold and gray are significant based on tlte critical value of a two-tail test with 95% confidence. Critical values available from: Olds, E.G. "Distribution of Sums of Squares of Rank Differences for Small Samples." Annals /vlathemalical Slmistics 9, no. 2 (1938). In 2002 NFL teams would have made better decisions if their dran decisions were based only on tinal year collegiate statistics.rhis is a possible example of a year where Combine perlonnancc may have diluted the value ofstatistical performance. This only 57 occurs twice (once with Running Backs) throughout all of the results, therefore it is more likely to bc coincidence rather than cause. Altcrnatively, the 2003 draft class is an additional example of Combine performance positively inl1uencing draft decisions. For this draft class overall Combine performance and Total Rank Sum arc significantly correlated with Draft Rank. It is for this draft class that NFL performance has the strongest correlation with draft rank. Although overall statistical performance is not significantly correlated with Draft Rank, the fact that Total Rank Sum has a stronger correlation with Draft Rank than overall Combine performance indicates the collegiate statistics information is adding to Combine performance information. When Combine and statistical performance both inl1uence draft decisions, it appears teams make their best draft decisions. Quarterbacks As noted by many that follow professional football and the NFL draft the quarterback position is considercd to be the hardest position to draft. II The results found in this study support this thesis. Only collegiate Passer Rating l2 for the 2005 draft class is significantly correlated with Draft Rank. Further, NFL performance significantly correlates with draft rank lor only two of the seven draft classes observed. These results exemplify NFL teams' struggle to consistently make positive decisions about quarterbacks with the available information. '; Moore, Michael 1.. "Drafting a Quarterback, the Biggest Gamble of All." Thefootbailexpertcom. April 22. 2008. Available from http://\vww.thefootbalJexpert.com. Accessed 5 ~ovember 2008. l~ Abbn:viatcd in Tank: 5.5 as '·PassRaL" 58 TABLE 5,5 Spearman Rank Correlation CocfIicients for Quarterbacks' These are the coefficients that resulted from each individual correlation, Under "Draft Rank Correlation Coefficients," these coefficients represent the listed variable's relationship to Draft Rank in each of the dratl classes observed, Under "NFL Performance Coefficients," these coefficients represent the listed variable's relationship to NFL performance in each of the draft classes observed, "'Coefficients in holt! and gray lire significant based on the critical value of a two-tail test with 95% confidence. Critical values available from: Olds, E,G, "Distribution of Sums of Squares of Rank Differences for Small Samples." Annals ofMalhemalicuISlalislics9.no. 2 (1938). Running Backs For Running Backs, final year collegiate performance has a consistent relationship with Drafl Rank, Final year collegiate Rush Yards, Rush Attempts, and Rushing Touchdowns each significantly correlate with draft rank for four of the seven draH classes observed, 13 The consistent relationship between collegiate performance and n Abbreviated in all tables as '"c'RushYds," "C.RushAtls," and "C.RushTDs." 59 Draft Rank suggests college statistics for Running Backs may have a strong influence on draft decisions, TABLE 5,6 Spearman Rank Correlation Coet1icients for Running Backs* are Coefficients," these coefficients represent the listed variable's relationship to Draft Rank in each of the draft classes observed. Under "NFL Performance Coefficients," these coefficients represent the listed variable's relationship to NFL performance in each of the draft classes observed. *Coe.fficients in hold and gray are significant based 011 the critical value of a two-tail test witlt 95% confidence. Critical values available fj'om: Olds, E,G, "Distribution of Sums of Squares of Rank Differences for Small Samples," Annals ;,\,fafhemuffcal Stu/istic.,,' 9, no. 2 (1938). 60 In regards to Combine performance, the 40-yard dash is the only Combine perfomlance variable that has a consistent relationship with Draft Rank, For each of the last three draft classes observed the 40-yard dash test significantly correlates with draft rank, The 40-yard dash may be a growing factor in teams' draft decisions, NFL perf'ormance is correlated with Draft Rank far all draft classes observed, The trends observed in the Draft Rank correlations do not continue when analyzed against NFL perf'ormance, The exception to this is the 2005 draft class, Every variable that is signi ticantly correlated with draft rank far this draft cIa,s also significantly correlates with NFL performance, This is the ideal result that NFL teams want; every statistic that significantly inf1uences draft decisions is also signi1icantly related to NFL performance, Overall the results far Running Backs sltow a consistency in factors that relate to Drall Rank, This indicates a general "formula" that may be developing for drafting Running Backs, This is an area where additional research on fallowing draft classes could reveal additional inl'ormation on whether the trends that appear in this study have continued after the 2005 draft class, The 1999 drall class is one of only two classes where neither final year collegiate rushing yards, rush attempts, rushing touchdowns, nor overall collegiate perf'ormance are significantly correlated with draft rank, For this same draft class, both Stat Rank Sum and Stat Rank have a stronger correlation with NFL performance than draft rank, This means that if NFL teams in 1999 drafted solely on overall statistical performance they would have made belter decisions, In the six drafts t(lllowing 1999, of tinal year collegiate Rush Yards, Rush Auempts, Rushing Touchdowns, ,md overall statistical perl'ormance, at least one is signillcilnlly correlated with draft nmk for all draft dasses except 2002, This is 61 further support for the importance of collegiate performance in draft decisions for Running Backs. An important trend seen in other positions is in the 2005 draft class. This is the only draft year where overall statistical performance and overall Combine performance both significantly correlate with draft rank. As observed in the other positions, NFL performance has a strong correlation with Draft Rank. This is further evidence in favor of the Combine as a positive int1uence on draft decisions. Tighr Ends The Tight End is an intriguing position in the NFL. In a simplistic sense a Tight End is used partially as a receiver and partially as an offensive lineman. In 2002, the Tight End position as a whole accumulated 1607 receptions, 16,812 receiving yards. and 128 touchdowns. 14 By the end 01'2005, the position increased its statistical production to 1.912 receptions, 20,006 receiving yards, and 140 touchdowns. 15 Apparently since 2002, the role of a Tight End has become more of a receiver than a lineman. This may be an explanation for why the 40-yard dash test significantly correlates with individual draft rank only after 2001. Although the 40-yard dash has a consistent relationship with draft rank, the 4()- yard dash fails to have this consistent relationship with NFL performance. In fact. NFL periDrmance is only correlated with draft rank !()f three ofrha seven drail classes. H Williams, Brett. "Tight Ends: NFL Teams are Benefiting from Their Increased Usc. Arc You?" hicachem:port.cOlTl. October 16,2008 i'lbid. 62 Indicating there a lack in ability of NFL teams to consistently make good draft decisions i()f Tight Ends, TABLE 5,7 Spearman Rank Correlation Coefficients for Tight Ends' lIGIiTENDS DRAFT Rk\iK CORRELATION i StatRk "FLRk Sum 0.425 ·0,742 ~O.331 i .0,890 -0501 ·(U34- I 2002 23 .o,7IG ·6.589 .0,565 .0.427 .0,541 .o.l102 .0,773 14 ·0530 16 .0,545 8 ·0.903 i 2003 14 ·0.127 ·0.202 I ·0.118 I ·0.091 ·0.019 ·0.129 2004 16 0.067 0.141 0.116 0.089 I 0.022 0.103 2005 8 ·0.317 I ·0.092 0.012 .0115,1 ·0,157 ·0167 These are the coefficients that resulted from each individual correlation. Under "Draft Rank Correlation Coefficients;' these coefficienls represent the listed variable's relationship 10 Draft Rank in each of the draft classes observed. Under "NFL Performance Coefficients," these coefficients represent the listed variable's relationship to NFL perfonnance in each ofthe draft classes observed. *Coejjiciellb'in bold and gray are significant based on the critical value of a two-fail test witlt 95% cOl~fidellce. Critical values available from: Olds. E.G, "Distribution of Sums of Squares of Rank Differences lor Small Samples:' Annalv :'l4athematica! .')'tatistics 9, no,:1 (1938), One of the few draft classes to have a strong correlation between NFL performance and dralt rank is the 2002 draft class, For this drafl class Combine pert Wide Receivers The Combine appears to have a minimal relationship to draft decisions for Wide Receivers. The only Combine variable with any significant correlation with Draft Rank is the 40-yard dash. Alternatively, overall collegiate performance variables correlate with Drafi Rank for all but two draft classes. Additionally, an individual collegiate performance variable is significantly correlated with draft rank for all but two draft classes as well. TABLE 5.8 Spearman Rank Correlation Coefficients for Wide Receivers' \\ mERE('fEVERS , 2004 27 I 0.150 I 2005 27 ,I -0,441 C.Ree CRee TotRk ComRk StatRk C~~L~~__ ~~~~~ __ 1-~=~~~~~ I Yds i TJh Sum Sum Sum ComRk StatRk - 1999 24! n {l"O 'r ·tHO? +--~;_}38T-T-::Q37! -0.576 1 .;).386 ! -~L3g2 r~-~ --- +(J.348 ~~---+--- ~0.423 ; ;------"':--27 i ;.~~ I nm+--:;iTiJs---r -0.078 ---·0.099 - -=0))07 : ------+0.106- .m ____ +_ OOW----< --je------+ ------:------;------t------~------+----~-~ 25 0041 () i}2S -(I l49 - -U.14U -0 J-16 -0156 -u 345 I2! i -(Ll3: : 2()02---t 2S ,.ri:476~+-"-~(i 05~i- -;. 139 --T------~--'f"'---_U_ 199 ----:---::ltJ79-----i---~~2Q ------~()~;}i2·------~·---:iJ!54---; ------T--- -"--"---j------______t ___ ------+-.------~,,- ----~------+------f ------, 20i}J 2--+ -u 132 2S'9 ---(!:-~-~~---r---.3~+ 224 .(J (J25 -0_252 -() 05} -(Ug7 2{J~~~_~::~~-~ -~--(.\T53----r___ :_~:_~~~ ___ +--~.~) 393 *0.,427 i ----~X~~~~~-.----~~:3.~~=:- ..0.446 !_ --:----==~~~~-----~~=~:r~ 2u05 2" *0.439 -OJJ46 -OIG} -d.lo: -(37) -0323. )44 -{j30t: -0275 Yhe-scta-;c the coem~r~nt-s-ih~tre-;~.iTi~(n~o-m each fndT~·T;:i"u-af"c(;;n:!~tro~-~-U nd~~ :TjrafiRa~-k--(:;-o~ehillon--; ('",efl')c'''"'''· these coefliclents represent the listed variable's relationship to Draa Rank in each of the draft classes observed, Under "NFL Performance Coefficients," these coefficients represent the listed variable's relationship to NFL perfonnance in each of the draft classes observed_ 64 *Coefjicients in bold and gray are significant based on lite critical vallte of a two-tail test with 95% conjidence. Critical values available from: Olds. E.G. -'Distribution of Sums of Squares ofR.nk Differences for Small Samples." Annals ofi,/athemalicu/ Stalisties 9, no. 2 (1938). None of the collegiate performance variables consistently correlate with NFL performance. but NFL performance is still significantly correlated with draft rank for every draft class. The Wide Receiver position appears to be one position where Combine performance may not be as highly regarded in draft decisions. Summary o/Results based on Individual Drafi Rank The results from analyzing players based on individual Draft Rank do not provide strong support for the use of the Combine. There is not a consistent correlation between Combine performance and Draft Rank. This means there is not evidence to support the use of Combine performance to distinguish players individually. Overall, there is not a consistent negative relationship between Combine performance and individual draft position. Based on analysis by individual Draft Rank. the Combine cannot be considered a signal. It is analysis based on a player's Drafi Round that provides the evidence necessary to consider the Combine as a signal. There is strong evidence to indicate that if Combine information influences draft decisions, this influence is positive. Out ofthc sixteen draft classes observed ielr defensive players (lour draft classes per position) and twenty-eight draft classes for offensive players (seven draft classes per position), only two draft classes show an instance in which teams would have made a better decision had they only used final year collegiate statistics. Therefore there is not substantial evidence in these results to suggest that Combine performance dilutes the value of statistical perl()rmance. Further. for every 65 dran year in which statistical perfonnance and Combine performancc both significantly correlate with draft rank, NFL performance has a strong correlation with draft rank every time, This occurs four separate times, for four separate positions, providing more support for inferring that the relationship between Combine performance and draft decisions is positive for NFL teams, Thus, the results from analysis by individual draft position indicate that NFL performance has a stronger relationship to available performance information including the Combine, This provides the necessary evidence to suggest the Combine's int1ucnce is positive, Since this trend is observed on based on individual performance, there is not a need to test for it in an analysis based on draft round, Results of Analvsis bv Draft Round As a second method of analysis, Combine and statistical performance is compared across the draft rounds for each position, In this method, players arc not separated into groups based on their draft class, they are only separated based their dran round, 16 A Spearman Rank Correlation is run between Draft Round and the variable for overall average Combine Rank, named Combine Rank AverageI7 The same correlation is run between Draft Round and the variable for overall average final year collegiate statistical rank, Stat Rank Average, 18 Defensive Backs, Linebackers, and Running Backs are the positions in which there is significant negative correlation hetween Combine Rank Average and Dral! Round, Defensive Ends, Linebackers, Running Backs, Tight Ends, '0 Draft Round the round in \vhich the player was selected in the draft Abbrev!(ited in all tables as: "ComRkAvg," ;?; A.bbreviate in all tables as "StatRkAvg," 66 and Wide Receivers each have significant linear correlation between Stat Rank Average and Draft Round. A negative linear correlation between perfonnance and Draft Round is an indication of a distinction in performance based on draft position. These results suggest that for three positions. the Combine does int1uence draft position as a signal. Further. the linear relationship between final year collegiate performance and draft position suggests that collegiate performance is a signal in the draft for tlve positions. TABLE 5.9 Correlation Coefficients for Average Overall Perfonnance Ranks vs. Draft Round* Draft Round and StatRkA ve. agamst Draft Round The coefficients represent the overall relationship between combine/collegiate performance of the players and their draft round. *Coe[ficients in bold and gray are significant hased on the crttieall·alue of a two-tail test with 95% confidence. Critical values avaiiable from: Olds. E.G. "Distribution of Sums of Squares of Rank Differences for Smali Samples" AnnaL, of :Hathemarical Statistics 9, no. 2 (1938). For Defensive Backs, Linebackers. and Running Backs the results show that generally. those taken in later rounds have a lower overall Combine Rank than those tak-':l1 in rounds b':IC}n: them, /\s can be seen in the graphs (Figun: 5,l). it is not a perfect 67 linear relationship, but the presence of a linear pattern mimics the concept of the signaling model. The averages represent a distinctive level of performance for which owners may be using to aid their draft decisions. Similar to the job market where there are individual diiTcrences in the job hires, there are exceptions to the Combine. Sometimes a player drafted in a higher round than what is projected by their Combine performance. In these situations it may be assumed that owners know information not known to the public, which can explain and rationalize a player's lack of performance. The results here provide a general idea of the differing performance levels of those taken in the seven rounds of the draft. FIGURE 5.1 Graph of Combine Rank Averages vs. Draft Round (For Defensive Backs, Running Backs, and Linebackers) Defensive Hacks Running Backs 24 16 ~22 t 20 ::i: 16 ~ 16 ~ ',4 15 12 ~ 10 8 c OrllnRmmtl Ijnelmckers 68 DetCnsive Ends, Tight Ends, and Wide Receivers do not have a significant correlation between Combine Rank Average and Draft Round, For these positions collegiate performance appears to be more of a key to distinguishing players, Therefore, it may be inferred that the Combine is merely a tool to see the players once more, To some extent these results can be partially attributed to the nature of the positions, Teams depend on Tight Ends and Wide Receivers to run precise routes and catch a footbalL FIGURE 52 Graph of Final Year Collegiate Statistic Rank Averages vs, Draft Round (f'()r Dejimsive Ends. Tight Ends, and Wide Receivers) I)cfensiv~ Emk Tight Ends ,2 " '" , 10" 12" .;." 8 lG , , ~ 5~ , , , 0 Draf! R"uft Wide Rt'l·eivcrs ~.C '2 'C ,0 ., "; Wi'; J j ,S lhdr R",,,,,j 69 Regardless of how fast a player runs, how high he jumps, and how strong he may he, ifhe can not catch a football and understand how to run a route then he is useless as a receiver. Defensive Ends are relied on to pressure the Quarterback, and therefore they need to be elusive, good with their hands, and understand defensive stunts. If a Defensive End cannot do these things then their physical statistics do not matter. Thus. NFL owners may find their most important information is in the statistical performance of these positions rather than Combine performance. These results provide evidence in favor of linal year college performance as a signal for Tight Ends, Wide Receivers, and Dcfensivc Ends. Therei()re, for these same positions, there is no evidence to support an argument for the use of Combine performance as signal. FIGURE 5.3 Graph of Combine and Final Ycar College Statistic Rank Average vs. Draft Round (For Running Backs and Linebackers) Running Backs Linebackl'l'S 16 n 20 14 E 1;; , ...... C;:;mRkA'li ~ 10 ' ~ SI~tl{}..Av;; Q1 i.l' 6 . 8 ' , . o For Linebackers and Running Backs both Combine Rank Average and Stat Rank Average are signiJicamly correlated with draft round (Figure 5.3). This can also be related to thl.' nature of their positions. Every team has a running back and uses the 70 running back within their offense. Therefore there are a high number of running backs with a high number of carries. yards. and touchdowns. The same is true for Linebackers. There are typically three Linebackers on the tield for each team, meaning Linebackers also have a high number of players with great statistics. So while statistics may be a first filter to narrow down the selection pool. that pool may still be too large to distinguish a player's abilities based on statistics alone. Thus. the Combine may play the role of the second filter for NFL teams to further evaluate these players. This would explain why both final year collegiate perf(xmance and Combine performance appear to be a signal for drafted Linebackers and Running Backs. Summary o(Resulls based on Drafi Round The results found by analyzing Combine performance by draft round suggest that Combine performance is a signal for Defensive Backs. Alternatively, for Defensive Ends, Tight Ends, and Wide Receivers. there is an indication that it is not Combine performance but final year statistical performance, which serves as a signal in draft decisions. Linebackers and Running Backs are unique because it appears both Combine performance and statistical performance are signals in the draft process for these positions. Analysis by drafi round indicates a signilicant relationship between perlormance, collegiate or Combine, and draft position for six of the eight positions. This supports the suggestion that there is the use ofa signaling in the NFL draft It is assumed that if NFL perft)fmance is significantly correlated with individual draft position. then it is significantly correlated with draft round. It is shown that NFL performance consistently 71 signiticantly correlates with individual draft rank for all positions except for Tight Ends. Therefore for five positions the results of this study provide evidence suggesting the signals used in draft decisions are positively affecting NFL teams. More specifically, for the positions in which Combine performance is a signal, the additional infonnation from the Combine is positively aiding the decisions. CHAPTER VI CONCLUSION This study attempts to answer two questions: (1) what is I he in/imnalional value of the NFL Combine? (2) Ilthe Combine has an i'1/ormalive value, is Ihe in/ormation positively affecting Ihe drafi decisions ofNFL teams? A previous study suggests that the Combine may not have a positive informational value because it fails to accurately predict performance in the NFl,. I The study conducted in this paper approaches the Combine from a difIerent perspective. It concludes with the alternative view that the Combine is a signal that positively inl1uences the draft decisions for Defensive Backs, Running Backs, and Linebackers. The use of the Combine as a signal means Combine performance is not expected to be an accurate predictor of future performance statistics. It means Combine perfonnance is only expected to separate players into groups based on their perceived value relative to those drafted in their same position and draft year. In this study the assumption is made that teams predetermine their need for positions. and therefore have already decided which positions they are targeting in each round. For each individual team typically the first round represents a teanl's tirst draJi pick, the second round the second pick, and so on through the seventh round. Based on i KumilL Fe and AJ Adams. "The \iFL Combine: Does it predict performance in the National Footbaii League," Juurn<1! of and ( Rest!arch 22, no. 6_ (Nov. 2008): 11'21-1727. 72 73 the NFL value chart,2 a team's first rouud draft pick is worth the most and their seventh round is worth the least. Therefore a prospect's perceived draft round is a measure of their potential value to an NFL Team compared to others drafted by the team. A player with a perceived draft round of one is said to be worth the highest value for the team that drafts him. Multiple players can have the same draft round value. An individual teams' private needs (i.e. system, coach's preferences) is what can make players orthe same draft round value worth more to some teams and less to others. This may be a reason why Combine performance is shown to have a significant relationship with draft round but not individual draft position. A Spearman Rank Correlation analysis is used to examine the relationship between collegiate performance, Combine performance, draft position, and NFL performance. In this analysis all Combine and collegiate performance statistics are converted into rankings. This allows lor players to be analyzed based on how their per/ormance compares to other players in their same position and draft class. Draft position is defined in two ways. First, a player's draft position is defined as his individual draft rank3 Second, a player's draft position is defined as his draft round." For each position Combine performance is compared across draft rank and then across draft rounds. Combine perfom1ance is shown only to relate to drafl round. This means the Combine separates players into groups, but the lack of a consistent relationship with : See Appendix [) Individual Draft Rank where a player is drafted relative to others in their position and draft is the first running back. sc(ond 1 Draft Round the round in \vhich a player was draHed 74 individual draft rank indicates that it does not distinguish which players are perceived to be of higher ability within each group. Combine per/ormance is found to signiticantly correlate with draft round Jar Defensive Backs, Linebackers, and Running Backs. Based on this, the information from the Combine should be considered neither a waste nor a predictor, but instead a possible signal lor NFL team owners looking to separate players in these three positions into groups based on their draft round value. Alternatively, for Defensive Ends, Linebackers, Running Backs, Tight Ends, and Wide Receivers, final year collegiate statistics significantly relate to draft round. Thus, for these five positions it appears collegiate performance is a signal that helps separate draft prospects into groups based on their draft round value. Differences in individual team's needs is a possible explanation for why there is evidence of signaling when analyzing performance across draft rounds, but not when analyzing individual draft position. Another explanation can be the teams needs to narrow their potential draft pick pool in order to pursue more in depth research lor each prospect. A learn looking to draft a linebacker in the third round does not want to wasle their time researching a linebacker that will be drafted by another team in the firsl round. Therefore informative value of the Combine appears to be in its ability to separate Defensive Backs, Linebackers, and Running Backs into groups based on perceived draft round value. For Defensive Backs and Wide Receivers the Combine's value appears to be its ability serve as a venue to further observe prospects. There is evideuce supporting the argument that the Combine does positively affect dmn decisions. For only two of the forty-four draft classes observed does it appear 75 that the NFL teams would have made better draft decisions if they did not consider the information from the Combine, But every time Combine performance and statistical performance signiticantly relate to draft position, NFL performance has a strong relationship with draft position, This means it is likely the additional information from the Combine is helping teams make better decisions than they would have made solely based on collegiate statistics, Overall the Combine appears to serve the same value as personality tests given to job applicants in thejob market: the Combine's value \0 the NFL is in its ability to help group players by their perceived drafi round, Based on the results of this study, there is evidence to suggest that the Combine is a useful signal for the NFL draft Although Combine and collegiate statistical performance are factors in drafi decisions, they are not the only factors that influence draft decisions, There are other factors in draft decisions accounting for the draft order that cannot be measured by statistics, The Combine also involves individual interviews and medical examinations, in addition NFL scouts routinely visiting schools and excessive film evaluation of potential prospects, This can explain why Combine and statistical performance measures do not strongly relate with individual draft position, As the Combine becomes a larger event there may be more emphasis put on performance at the Combine, As top performers gain more recognition and press. teams may be more inclined 10 focus on the Combine, The results from this study are suggestive. but are not conclusive. Therefore this topic would benetit from further research, A recreation of this study utter a couple more draft years have passed can 76 determine if there is a change in the relationship between Combine performance and draft order. APPENDIX A 2009 NFL Combine Schedule l February J gill 24111 18th-21st Day 1 Arrivals: Group I(PK, OL), Group 2(OL), Group 3(TE) Interviews 1 National Invitational Camp. '·Player's Schedule,"' Internet; Available online from http: wVvw.nflcombine.nct/p!ayersischedu!e. Accessed 5 November 2008. APPENDIX B Combine Workout Test Descriptions I As described by the official NFL website www nf/.com/combine 10,20, and 4(}.yard dash The 40-yard dash is the marquee event at the combine. It's kind of like the IOO-meters at the Olympics: It's all about speed, explosion and watching skilled athletes run great times. These athletes are timed at 10,20 and 40-yard intervals. What the scouts are looking for is an explosion from a static start. Bench press The bench press is a test of strength -- 225 pounds, as many reps as the athlete can get What the NFL scouts are also looking for is endurance. Anybody can do a max one time, but what the bench press tells the pro scouts is how often the athlete frequented his college weight room for the last 3-5 years. Vertical jump The vertical jump is all about lower-body explosion and power. The athlete stands !lat-footed and they measure his reach. It is important to accurately measure the reach, because the differentia! between the reach and the flag the athlete touches is his vertical jump measurement. Broad jump The broad jump is like being in gym class back in junior high schooL Basically, it is testing an athlete's lower-body explosion and lower-body strength. The athlete starts out with a stance balanced and then he explodes out as far as he can. It tests explosion and balance, because he has to land without moving. 3 cone drill The 3 cone drill tests an athlete's ability to change directions at a high speed. Three cones in an L-shape. He starts ttom the starting line, goes 5 yards to the first cone and back. Then, he turns, runs around the second cone, runs a weave around the third cone, which is the high point of the L, changes directions, comes back around that second conc and finishes. Shuttle run The short shuttle is the first of the cone drills, It is knovln as the 5-10-5. What it tests is the athlete's lateral quickness and explosion in short arcas. The athlete starts in the three-point stance, explode out 5 yards to his right, touches the line, goes back 10 yards to his left, left hand toucheS the line, pivOL and he turns 5 more yards and finishes. Wonderlic' The Wondcrlic Test is a personality test that is used to assess the aptitude of an individuaL The test is approximately' t\vC1Ve-ITlinutes long for a lOtal of fifty questions, The highest score possible is a 50. and a score of20 is considered to be a score equivalent to average intelligence. , National Football teague. NFL Combine Workouts. Available online from http: wW\\'.nfl.comicombinei\vorkouts. Accessed 5 November 2008 2 Wonderlic Personality Test Available online from http)!w\vw.\vonderlic>com. Accessed 5 Nove-Inber 2008. 78 APPENDIX C QUARTERBACK COLLEGIATE STATISTICAL VARIABLES I Rushing Vards __ ' ___' _____ CRushYds , +I LRushinJLI:ouchdowns ---,-1-_"C~.R"u",s",·hC'T""D",s ' i LRushing Attempts __ "-+I __C""',,, R,ushAtts i 1 Passin2 Attempts _____~-----C~.~A~tt~--~ Passing_Completions ! Completion Percentage CCom% I Total Passing Vards crotYds ! I Total Passing Touchdowns 'CTDs ~l~otal IncetercePtions_____~ __ l-,~<::-·I_N_T_",_, __ ~ Ll'asser _R_atinJ[_,~ "'_____ , _____ .L ____PassRat __ ~ 79 APPENDIXD NFL DRAFT V ALUE CHART ESPN.com, "NFL Draft·Pick Value Chart." Internet; Available from http;!!sports.espn.go.com. Accessed 23 February 2009. 80 APPENDIX D (Continued) NFL DRAFT V ALUE CHART ESPN.com, "NFL Dral1-Pick Value Chart." Internet; Available Irom http;! sports.espn.go.com. Accessed 23 February 2009. 81 SOURCES CONSULTED Journal Articles Aigner, Dennis J, Glen G. Cain. "Statistical Theories of Discrimination in Labor Markets." IndusTrial & Labor Relations Review 30, no. 2 (1977): 175-187. Alchian, Armen A. "Uncertainty, Evolution, and Economic Theory." The Journal of PoliTical Economy 58, no. 3 (1950): 211-221. Altonji, Joseph G., Charles R. Pierret. "Employer Learning and Statistical Discrimination." QuarTerly Journal oj"Economics 116, no. 1 (2001): 313-350. Arrow, Kenneth J. 1973. "Higher education as a filter." 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