College Basketball Recruiting and the NBA Draft: Data, Theory and Statistical Models

Over the last week or so we have presented data on school’s success in developing high school recruits into NBA draft picks.  What we have presented thus far is raw data summarized at the school level.  These results provide offseason wins and bragging rights for some fan bases and losses for others.  One of our favorite responses came from a University of Wisconsin blogger who made a link between our brand equity study and the draft efficiency results.  In the Wisconsin case, the combination of high fan equity combined with low draft efficiency is something that should give fans (and athletic directors?) something to think about.

But while summarized data is great, there are some limitations.  The biggest limitation is that the data limits our ability to draw deeper insights.  We know that Boston College players develop better than Duke players (adjusted for recruiting rankings) or that Purdue players have more success than Indiana players, but we don’t know why?  With respect to college basketball recruiting, one question that is of interest to us is how does the composition of a recruiting class impact the likelihood that a given recruit is successful in developing into a draftable player.  Our starting theory for our analysis was that players would have better chances to make the pros (controlling for the player’s individual talent) when their teammates were less highly regarded.  The theory is that less talented teammates would result in a player seeing more playing time, and being more of a focus of the offense.  In our earlier analysis of NFL draft efficiency, we found evidence for this theory being true.

In general, what we do on the website is use theory to design statistical models and then take these models to data.  When we did this for the college basketball draft efficiency data, we got some surprises.  For this analysis we used a tool called logistic regression.  Logistic regression is useful when we are trying to predict yes/no type events.  In this case we were interested in predicting the probability that a recruit of some quality level (5-Star, 4-Star, 3-Star or other) is drafted.  Our theory would suggest that having more 5-Star players would reduce the probability of any given player being drafted.

For the statistical analysis, we began by predicting whether a player was drafted based on the composition of the team, the school’s expenditures on the team, the team’s historical success and other factors.  What we found was that for 4 and 5-Star players the best predictor was the number of other 5-Star players on the team.  We tried a variety of specifications and used some extra tools such as Factor Analysis, and this general result that draft efficiency is positively correlated with recruiting success was robust.  For the 3-Star player, the best predictor was the school’s level of investment. Very few of the variables we included in the model were significant.

While we didn’t get what we expected, we did get some interesting results.  For the elite high school recruit, our results do suggest that it is better to go to a blue blood program.  Given the lack of significance of variables related to exposure, such as whether the team participated in the NCAA tournament, our conjecture is that these results suggest that better teammates equates to more competition in practice and for playing time, and it is this competition that is the key to developing NBA playersThis result would suggest that the highly recruited athlete is doing the right thing by choosing Kentucky, Kansas or North Carolina.

The other interesting take-away from the results is the lack of significant variables and the overall fit of the model.  In this case, it appears that we are missing a big part of the story.  While our model results tell us about the “average” importance of team composition, it doesn’t tell us about the talent developing ability of specific schools and coaches.

Our model results can be used to evaluate individual schools.  To do so, we use our statistical model to predict the draft efficiency of each school (based on historical recruiting results, investment in the program, conference affiliation, historical successes, etc.) and compare this to the actual draft efficiency.  When we do this comparison, we get some thought-provoking results.  The overall “winner” of this analysis was Georgia Tech.  During our ten year study period, Georgia Tech had four 5-Star recruits and twelve 4-Star recruits.  All of the 5-Star recruits and a quarter of the 4-Star recruits were drafted.  Other high scoring schools included Ohio State, Kentucky and UCLA.  Perhaps the most interesting result we can extract from this analysis is which schools struggle to convert talent into NBA players: out of the 68 BCS schools evaluated, Duke finished at 51 and Michigan State at 61.  In the case of Michigan State, only two of the six 5-Star recruits were drafted.  Even worse, none of the twelve 4-Star recruits were drafted.  So while Tom Izzo and Mike Krzyzewski are great coaches when it comes to tournament success, a high school recruit may want to think twice before choosing these schools.

Mike Lewis & Manish Tripathi, Emory University 2013.

Nevada & BYU Best at Converting Talent into NBA Draft Picks: Ranking the Best of the Rest (Non-BCS)

In our current series on college basketball programs’ abilities to transform their available high school talent into NBA draft picks, we have decided to start with summary data for each school.  We plan on concluding the series with a statistical model that predicts the likelihood of a player being drafted based on the player’s recruiting ranking, the school’s investment in the program, the rankings of the player’s teammates and other factors.  We decided to start with the summary efficiency rankings simply because these rankings are more accessible to fans and tend to generate more conversation.

Our series continues with an examination of recruiting classes from 2002-2011 in the Non-BCS Conferences (The Best of the “Mid-Majors”).    The chart below lists our efficiency rankings (for more details on our methodology, please click here).  The University of Nevada Wolfpack were the leaders in converting talent into NBA draft picks.  The Wolfpack were followed closely by BYU.  It should be noted that there was a minimum threshold of recruiting talent over the ten year study that was needed to be considered for this analysis.

Nevada and BYU not only are on top of the “Best of the Non-BCS” ranking, but they are also the two best teams in the country overall based on this talent conversion metric. Also, although Colorado State and North Texas are at the bottom of this top 10, their conversion rates would put them near the top of any of the BCS conference rankings.  Finally, Gonzaga and Memphis are not on this list, despite producing 3 and 9 draft picks, respectively, during the period of this study.  This is due to when we control for the amount of talent that was recruited to these schools, their conversion rates are less than stellar.

In the period of the study, Nevada did not have any 5-Star recruits in its basketball program.   Nevada had 50% of its 4-Star recruits, 17% of its 3-Star recruits, 14% of its 2-Star recruits, and 6% of its non-rated recruits drafted into the NBA.  This is incredible given that the national overall average for getting drafted was 13% for 4-Star recruits, 3% for 3-Star recruits, 0.8% for 2-Star recruits, and 0.4% for non-rated recruits!

Similar to Nevada, BYU did very well in converting lower-ranked talent.  BYU had 14% of its 3-Star recruits drafted into the NBA.  Remarkably, BYU had 13% of its non-ranked players drafted; this is almost 33 times better than the overall national average!

PREVIOUS POST: RANKING THE BIG EAST

South Florida & Marquette Best at Converting Talent into NBA Draft Picks: Ranking the Big East

In our current series on college basketball programs’ abilities to transform their available high school talent into NBA draft picks, we have decided to start with summary data for each school.  We plan on concluding the series with a statistical model that predicts the likelihood of a player being drafted based on the player’s recruiting ranking, the school’s investment in the program, the rankings of the player’s teammates and other factors. We decided to start with the summary efficiency rankings simply because these rankings are more accessible to fans and tend to generate more conversation.

Our series continues with an examination of recruiting classes from 2002-2011 in the Big East.    The chart below lists our efficiency rankings for the Big East (for more details on our methodology, please click here).  The University of South Florida (USF) was the leader in the Big East in converting talent into NBA draft picks.  The Bulls were followed by Marquette and then Connecticut.

 

In the period of our study, USF had no 5-Star or 4-Star recruits at all.  However, 9.5% of 3-Star recruits at USF were drafted into the NBA (The overall national draft rate for 3-Star recruits during this period was 3%).

Marquette performed better than traditional Big East powers UConn, Syracuse, and Georgetown in the period of our study.  This is largely due to 13% of 3-Star recruits and 14% of non-ranked recruits from Marquette being drafted.  This is incredible considering that the national draft rate for 3-Star recruits was 3%, the rate for non-ranked recruits was 0.4%! While Georgetown and Syracuse were both slightly above average with respect to their 5-Star recruit drafting rates, they were both below the national average for being drafted with respect to their 4-Star recruits.  This is potentially problematic, as 4-Star recruits reflect a large portion of the recruiting classes for both schools.

PREVIOUS POST: RANKING THE SEC

NEXT POST: RANKING THE BEST OF THE REST

Vanderbilt & Florida Best at Converting Talent into NBA Draft Picks: Ranking the SEC

In our current series on college basketball programs’ abilities to transform their available high school talent into NBA draft picks, we have decided to start with summary data for each school.  We plan on concluding the series with a statistical model that predicts the likelihood of a player being drafted based on the player’s recruiting ranking, the school’s investment in the program, the rankings of the player’s teammates and other factors. We decided to start with the summary efficiency rankings simply because these rankings are more accessible to fans and tend to generate more conversation.

Our series continues with an examination of recruiting classes from 2002-2011 in the SEC.    The chart below lists our efficiency rankings for the SEC (for more details on our methodology, please click here).  Vanderbilt was the leader in the SEC in converting talent into NBA draft picks.  The Commodores were followed by Florida and then traditional power Kentucky.  To all of our friends in Lexington, we realize that Coach Calipari has done an excellent job in producing NBA draft picks.  Our analysis covers the recruiting classes of 2002 to 2011, and thus Calipari only comes in at the tail-end of the sample.  We are trying to look at long-term trends.  It is quite likely that if we only looked at the Calipari era, Kentucky would be on top.

In the period of our study, 14.3% of 3-Star recruits at Vanderbilt were drafted into the NBA (The overall national draft rate for 3-Star recruits during this period was 3%).  The Commodores only had one 5-Star recruit during the time-frame of our study, and that 5-Star recruit was drafted.  Thus, Vandy was able to effectively convert the limited high-level of talent that it recruited, and it was able to transform lower-ranked talent into NBA material at a rate far above the national average.

During the time period of our study, Kentucky and Florida had 32% and 21% of their overall recruits drafted, respectively.  This puts both schools in the top 10 in the country for overall percentage of recruits drafted.  While Kentucky had 72% of their 5-Star recruits drafted (the national average was 51%), they did not do as well with lower-rated recruits as compared to Florida.  Florida had 26% of their 4-Star recruits drafted (the national average was 13%), and also had 3-Star and non-rated recruits drafted during the time period of our study.

PREVIOUS POST: RANKING THE BIG-12

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O’Bannon Versus the NCAA: Remove Profit Motivation (Part 5)

For those of you following along, we have done a series of posts regarding the Ed O’Bannon lawsuit.  Our take on this issue has been a little different than most as we have emphasized the value that each entity (athletes, schools) provides to one another.  This discussion is at heart about whether and how college athletes should be compensated.  To conclude the series we will give our take on this overarching issue of compensation for college athletes.

I have seen a number of proposals (Whitlock, Barnhart) for how athletes should be compensated.  A common approach is to look at total revenues and then determine the appropriate split between athletes and schools.  These proposals largely use professional sports as a model.

My take on the issue is conflicted.  On one hand, I think the current state of affairs borders on immoral.  NCAA players have few rights and operate under significant constraints.  Scholarships are renewed on a yearly basis so essentially athletes have one year contracts.  In contrast, coaches operate in a free market system and can sell their services to the highest bidder.  Coaches also typically have contracts that continue to pay them even if they are fired.  Transfer rules are particularly one sided.  If an athlete transfers, he must sit out for a season and the school can limit the athlete’s choices.  Coaches can, of course, move on whenever a better opportunity arises (often the new suitor will pay the coaches buyout).  The hypocrisy of these asymmetric rules is dramatically highlighted when NCAA sanctions are levied.  Often the coach, on whose watch the infractions occurred, moves on while players then suffer the consequences.

My starting point in this discussion is that the NCAA and college sports need significant reform.  A system that allows coaches and schools operate in the free market while restraining the players is unethical and exploitive.  However, I do believe that the argument is not entirely clear cut.  The NCAA platform does provide significant value to players.  In addition to educational benefits, athletes are given an opportunity to perfect their craft and to build their personal brands.

On balance, I think the facts suggest that the players should be paid.  The dollars being collected are just too significant for the current system to be viewed as fair.  Men’s basketball and football are essentially managed as professional franchises and it is unconscionable for the athlete to exist on poverty level stipends while coaches and athletic directors are paid millions of dollars.

However, and this is a big however, just asking whether the players should be paid misses a big part of the fundamental issue.  The missing piece is whether colleges should be in the business of paying players?  My answer to this question is no.  I just don’t see any way in which paying players is remotely consistent with these institution’s fundamental missions.

While some folks may feel that I am being naïve due to the large dollars involved, I don’t think this is the case.  Paying the players is likely to fundamentally change the economics of athletic programs.  The revenue bases of schools like Texas, Ohio State and Florida will make it very difficult for other schools to compete and still remain profitable.  To maintain competitive balance, schools and leagues would likely need to adopt some form of revenue sharing and salary caps.  Will the Big Ten fund the MAC?  Will Florida write a check to Western Michigan?  Short of a significant revenue sharing program or a strict salary cap across conferences, the economics of big time sports would quickly change.  Currently the revenues provided by the Big Ten network and the SECs television contracts means that many schools operate with essentially guaranteed profitability in the major sports.  These profits often fund money losing programs like women’s golf and men’s wrestling.

If a substantial amount of revenues are shifted towards paying players in the major sports (for now we will ignore title 9 requirements that might require paying female athletes at comparable rates).  Schools would likely need to make further cuts in non-revenue programs or even re-evaluate continued D1 participation.  It is one thing for a school to participate in big time sports when the profits are guaranteed.  It is another when the institution would be operating in a financially risky environment.

The other point that is often raised is that the dollars are too big for schools to drop out.  To take an extreme example, the 2012-13 budget for the University of Texas is listed as $2.347 billion.  This budget also lists the athletic program as a self-supporting unit with a budget of $137 million.  So while sports may be the public face of many large research institutions, these sports are a relatively minor part of the overall university.

As marketers we are well aware of the important role played by big time sports. High profile sports may attract future generations of students and may be the foundation for the alumni community.  But, sports are but one way to market a school (e.g. the Ivy League).

To bottom line this discussion, if I were a university president and was faced with an environment where college sports explicitly became professional organizations, it would be an easy decision.  I would take this “structural change” as an opportunity to reposition my school to be more consistent with the larger institutional mission.  And remember this is coming from a guy whose primary hobby is college sports.

My ultimate conclusion is, therefore, that for schools to save their athletic programs it is necessary to remove the profit motivation from the system.  This is, however, different from saying that profits should be removed.  As I see it the main problem is that we have evolved to a system coaches and athletic departments can harness the loyalty of alumni and other fans to make themselves amazingly wealthy.

Iowa State & Kansas Best at Converting Talent into NBA Draft Picks: Ranking the Big-12

In our current series on college basketball programs’ abilities to transform their available high school talent into NBA draft picks, we have decided to start with summary data for each school.  We plan on concluding the series with a statistical model that predicts the likelihood of a player being drafted based on the player’s recruiting ranking, the school’s investment in the program, the rankings of the player’s teammates and other factors. We decided to start with the summary efficiency rankings simply because these rankings are more accessible to fans and tend to generate more conversation.

Our series continues with an examination of recruiting classes from 2002-2011 in the Big-12.  The chart below lists our efficiency rankings for the Big-12 (for more details on our methodology, please click here).  Iowa State was the clear leader in the Big-12 in converting talent into NBA draft picks.  The Cyclones were followed by traditional power Kansas and then Texas.

In the period of our study, 15% of 2-Star recruits and 13% of non-rated recruits at Iowa State were drafted into the NBA.  This is very impressive given the overall national draft rates: 0.8% for 2-Star recruits and 0.4% for non-rated recruits!  Furthermore, two 3-Star recruits were drafted from Iowa State.  Iowa State did a remarkable job of converting its available talent into NBA draft picks.

Perennial power Kansas finished second in the rankings.  Kansas had an overwhelming 30% of its overall recruits drafted into the NBA.  The Jayhawks also had 39% of its 4-Star recruits drafted (compared to the 13% national 4-Star average).  Third place Texas had 66% of its 5-Star recruits drafted (compared to the 51% national 5-Star average).

PREVIOUS POST: RANKING THE PAC-12

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Washington & USC Best at Converting Talent into NBA Draft Picks: Ranking the PAC-12

In our current series on college basketball programs’ abilities to transform their available high school talent into NBA draft picks, we have decided to start with summary data for each school.  We plan on concluding the series with a statistical model that predicts the likelihood of a player being drafted based on the player’s recruiting ranking, the school’s investment in the program, the rankings of the player’s teammates and other factors. We decided to start with the summary efficiency rankings simply because these rankings are more accessible to fans and tend to generate more conversation.

Our series continues with an examination of recruiting classes from 2002-2011 in the PAC-12.  The chart below lists our efficiency rankings for the PAC-12 (for more details on our methodology, please click here).  The University of Washington was the clear leader in the PAC-12 in converting talent into NBA draft picks.  The Huskies were followed by USC and Cal.  Traditional power UCLA finished 5th.

In the period of our study, Washington produced nine draft picks, and 22% of the overall recruits for UW were drafted into the NBA.  66% of 5-Star recruits, 31% of 4-Star recruits, and 12.5% of 3-Star recruits from UW were drafted.  This is truly remarkable given the overall national draft rates: 51% for 5-Star, 13% for 4-Star and 3% for 3-Star!

USC finished second in the PAC-12 rankings.  The Trojans had 29% of their 4-Star recruits drafted, and had two 3-Star recruits drafted in the first round.  Cal finishing third potentially speaks to the importance of the head coach in the efficiency rankings.  Mike Montgomery, the head coach for Cal, was at Stanford in the late 1990s and early 2000s, when the Cardinal enjoyed an excellent talent to NBA draft pick conversion rate.  Current head coach Johnny Dawkins has produced a grand total of 0 draft picks for the Cardinal from his recruiting classes.

PREVIOUS POST: RANKING THE BIG 10

TOMORROW: RANKING THE BIG 12

 

Purdue & Illinois Best at Converting Talent into NBA Draft Picks: Ranking the Big 10

We spend a lot of time on the site talking about statistical models.  Statistical models are great for identifying trends and relationships between variables when we have a significant amount of data.  Models are also useful for moving us beyond arguments based on examples and anecdotes.  We think this is particularly important when discussing sports.  Every guy in every bar has a theory that they can support with an example.

In our current series on college basketball programs’ abilities to convert recruits into NBA draft picks, we have decided to start with summary data for each school.  We plan on concluding the series with a statistical model that predicts the likelihood of a player being drafted based on the player’s recruiting ranking, the school’s investment in the program, the rankings of the player’s teammates and other factors. We decided to start with the summary efficiency rankings simply because these rankings are more accessible to fans and tend to generate more conversation.

I wanted to use today’s rankings of the Big Ten schools as an excuse to delve into a specific comparison between two schools.  I have two reasons for this.  The first is that looking at the data for a couple of schools will highlight why our statistical model gives the results it does.  The other reason is that I (Lewis) want to provide some recruiting material for my Illini.

The chart below lists our efficiency rankings for the Big Ten.  At the top, we have two solid programs in Purdue and Illinois.  These two are followed by the recent and traditional powers: Ohio State, Indiana and Michigan State.  While Ohio State has the most draft picks, they also had the greatest recruiting success with players like Greg Oden, Mike Conley, BJ Mullens, and Jared Sullenger coming through Columbus in the last decade.

(For more details about the methodology, click here)

Now back to my second motive.  As an aside, I thought about titling this piece “Why Jabari Parker, Cliff Alexander and Jahlil Okafor Don’t Need to Travel Far from Home.”  In our rankings of the ACC, the Duke Blue Devils finished in the middle of the pack.  What I’d like to do (and I know this is self-indulgent) is to compare the Illini with Duke.  In the table below I give the rankings of members of Duke’s and Illinois’ recruiting classes from 2001 to 2002 (I collected these by hand so please excuse any omissions).

Over the relevant drafts, Duke had 11 players selected compared to 4 for Illinois.  While this may seem to be a reason for a student athlete to choose Duke, when we look at the input, things are much less clear.  From 2002 to 2010 Illinois had 1 top twenty recruit.  In contrast, Duke had 13.  If we look at top thirty recruits, Illinois still had 1 while Duke had 15.

I think the explanation for these results is pretty simple.  When an athlete chooses a school in a power conference, but without a roster loaded with McDonald’s All-Americans, that athlete has more chances to see the floor, and even when on the floor the athlete has a better chance to be the focal point for the offense.  Going all the way back to 2002, Dee Brown was a featured star at Illinois while the similarly rated Sean Dockery was a role player for Duke.  Another highly rated player from Illinois Michael Thompson ended up transferring from Duke to Northwestern.  And while some attrition is natural, it is interesting that Thompson was rated higher (30th) than every single Illinois recruit in the period from 2002 to 2010.

So what is the take away?  In terms of the preceding comparison, it is that what the glitz and glamour of playing at a high profile school is attractive, the high profile nature of a Duke is likely meaningless when it comes to getting to the next level.  In fact, the tendency of very highly rated players to choose schools like Duke means that the player’s chances of making the pros might actually be a bit less at a Duke than an Illinois.

But, as noted, the comparison of Duke and Illinois is anecdotal.  What we really need to reach the preceding conclusions is more data.  My comparison of Illinois and Duke is mainly intended to foreshadow the statistical analysis we will provide next week.  This analysis is designed to tease out the effects of player quality, within roster competition, school investment and on-court success on player development.

PREVIOUS POST: RANKING THE ACC

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Converting High School Talent into NBA Draft Picks: Ranking the ACC

The NBA Draft can be a time for college basketball fans to cheer about the “success” of their basketball program.  Kentucky, Duke, North Carolina, and Kansas fans can boast about the number of alums currently in the NBA.  This year, ESPN is taking that discussion one step farther by describing the quality of NBA players produced, and ranking the “NBA Pedigree” of colleges.

Our take is a bit different as we will examine the process of taking high school talent and converting it into NBA draft picks. In other words, we want to understand how efficient are colleges at transforming their available high school talent into NBA draft picks? Today, we launch our NBA draft series by ranking the schools in the ACC based on their ability to convert talent into draft picks.

The initial approach is fairly simple.  Each year, (almost) every basketball program has an incoming freshman class.  The players in the class have been evaluated by several national recruiting/ranking companies (e.g. Rivals, Scout, etc…).  In theory, these evaluations provide a measure of the player’s talent or quality*.  Each year, we also observe which players get drafted by the NBA.  Thus, we can measure conversion rates over time for each college.  Conversion rates may be indicative of the school’s ability to coach-up talent, to identify talent, or to invest in players.  These rates may also depend on the talent composition of all of the players on the team.  This last factor is particularly important from a recruiting standpoint.  Should players flock to places that other highly ranked players have selected?  Should they look for places where they have a higher probability of getting on the court quickly? Next week we will present a statistical analysis (logistic regression) that includes multiple factors (quality of other recruits, team winning rates, tournament success, investment in the basketball program, etc…). But for now we will just present simple statistics related to school’s ability to produce output (NBA draft picks) as a function of input (quality of recruits).

Our first set of rankings is for the ACC.  At the top of the list we have Boston College and Georgia Tech.  Boston College has done a good job of converting low-ranked talent into NBA picks (in this time period they had two three-star players and a non-rated player drafted).  Georgia Tech, on the other hand, has converted all of its five-star recruits, and several of its four-star recruits.  A result that may at first glance seem surprising is the placement of UNC and Duke.  However, upon reflection these results make a good deal of sense.  When players choose these “blue blood” programs they face stiff competition for playing time from both current and future teammates.

Here are some questions you probably have about our methodology:

What time period does this represent?

We examined recruiting classes from 2002 to 2011 (this represents the year of graduation from high school).  While the chart above ranks the ACC, we compiled data for over 300 Division 1 colleges (over 12,000 players).

How did you compute the conversion rate?

The conversion rate for each school is defined as (Sum of draft picks for the 2002-2011 recruiting classes)/(Weighted Recruiting Talent).  Weighted Recruiting Talent is determined by summing the recruiting “points” for each class.  These “points” are computed by weighting each recruit by the overall population average probability of being drafted for recruits at that corresponding talent level.  We are using ratings data from Rivals.com.  The weights for each “type” of recruit were 0.51 for each five star recruit, 0.13 for each four star, 0.03 for each three star, 0.008 for each two star, and 0.004 for each not ranked.  

Second-round picks often don’t even make the team.  What if you only considered first round picks?

We have also computed the rates using first round picks only, please see the table below.

NEXT: RANKING THE BIG 10

*We can already hear our friends at Duke explaining how players are rated more highly by services just because they are being recruited by Duke.  We acknowledge that it is very difficult to get a true measure of a high school player’s ability.  However, we also believe that over the last ten years, given all of the media exposure for high school athletes, this problem has attenuated.

Mike Lewis & Manish Tripathi, Emory University 2013.

How Would Paying Players Change College Sports: O’Bannon vs the NCAA (Part 4)

In our series on the O’Bannon case and the associated issue of paying college athletes, we have focused on the value that athletes and universities provide to each other.  Another perspective that should be considered is how a shift to paying players might impact fans.  This is a tough issue to contemplate given that the ultimate impact on fans or customers would be a function of the specific system used to compensate athletes.

Our view is that the fan’s perspective should be considered in terms of how paying players would affect competitive balance levels across a mix of very different schools.  Perhaps the most frequent source of concern about competitive balance has been the New York Yankees in professional baseball.  The fear has always been that that large market teams like the Yankees will use their greater revenue bases to attract all the top talent, so that teams in small markets such as Kansas City or Milwaukee will be unable to field competitive teams.  The opening day payroll of the Yankees this year was $228 million while the Houston Astros lagged the field with a payroll of just $22 million.  However, concerns about competitive balance in MLB have faded in recent years as the teams such as the St. Louis Cardinals, Tampa Bay Rays, Colorado Rockies, and the Detroit Tigers have played in the World Series.  Notably, all major US professional leagues have adopted some form of revenue sharing or payroll constraints in order to maintain competitive balance and team profitability.

College sports have their own issues with competitive balance. The University of Texas athletic program is a $150+ million business while the 50th ranked (in terms of revenues) Northwestern program produced only $56 million.  This allows Texas to pay its football coach more than $5 million per year.  Some revenue sharing already occurs but it is at the conference level.  It must be noted that Northwestern’s spot in the top fifty is largely due to its membership in the Big Ten Conference (it has been reported that the Big Ten Network distributes more than $20 million per school).  Whether or not college sports operate with an acceptable level of balance (The SEC has won the last seven BCS Championships) is debatable, but the prohibition against paying athletes can be viewed as an incredibly rigid salary cap.  Paying players means that some other structure for maintaining competitive balance would be needed.

To a large degree, the conference structure of college sports increases the complexity of coming up with solutions for maintaining competitive balance.  Currently, conferences operate with extensive revenue sharing agreements.  But an extension to sharing revenue with non-members would require a paradigm shift.  In addition, Title IX regulations that strive to equalize expenditures on men’s and women’s sports are another source of complexity.  This means that revenue sharing is implicitly required within institutions.  If college football players receive salaries does that mean that women golfers would also need to be compensated?

All this is fine, but the question remains as to how big time college sports would evolve if college players could be paid and how might these changes affect the fans?  While considering the impact on the fans may seem a bit tangential, at the end of the day it is the fans that are the ultimate source of revenues and profits associated with college athletics. We, at Emory Sports Marketing Analytics view the entire situation as driven by marketing considerations.

The O’Bannon case began with a complaint about the embargo against athletes profiting from their own images.  A relatively minor change might allow athletes to market their own images to the highest bidders while still preventing direct compensation from colleges to players.  We would expect that such a change would have significant effects on recruiting, with the end result being an even greater concentration of elite recruits at high brand equity schools.  As high school athletes begin to make their college decision based on their personal brands, we expect that we would see many situations that are analogous to LeBron James’ decision to move to the high profile Miami market.  The potential would also exist for schools to gain recruiting advantages by more aggressively marketing their individual athletes.  While, we could argue that the situation described above already exists (e.g. Kentucky basketball) we expect that the trend would accelerate.   The preceding scenario would likely lead to a “rich getting richer” scenario.  The open question would be whether this increase in the advantages of more marketable schools would create dangerous levels of imbalance.

Allowing players to sign licensing deals would also mean that players would be able to sign with agents while still in school, since they would need representation when negotiating with video game, clothing and shoe companies.  Undoubtedly, shoe companies in particular would become even more powerful players in college basketball.  Shoe companies already sponsor AAU and college teams, and it’s not farfetched to imagine a scenario where a player such as Andrew Wiggins’ college choice would be made by a team of agents and other representatives working in conjunction with shoe companies.  A further question would then arise as to what schools could promise athletes in terms of marketing support?  Would high profile athletes insist on being featured on billboards or in other marketing communications?

A more extreme, and perhaps fairer, solution would be to allow athletes to participate in a free market system where they could sell their services to the highest bidder.  We say “fairer” since the college sports marketplace already includes many examples of coaches and athletic directors becoming extremely wealthy.

Moving to a totally free market would be a tremendously interesting experiment.  Just as in MLB, the college sports landscape is composed of schools that vary greatly in terms of market potential and current popularity.  Texas, Florida, Notre Dame, Ohio State and others have resources that would enable them to greatly outspend even other members of the power conferences.  Imagine a scenario where the power schools can outspend other institutions by a significant multiple.  We would also ask the question of what would happen to transfer rules.  How could the NCAA prohibit transfers or require athletes to sit a year when such a regulation would harm players earning capacities?  Would colleges need to negotiate compensation and contract length with prospective student athletes?  The real danger in moving to a free market system is that suddenly many schools would be entering a world of significant financial risk, where previously profitability was almost guaranteed (for examples of this look at the investments in programs made by Big Ten schools such as Northwestern and Illinois).

If our conjectures are true, a move to a free market could well have a negative effect on the capacity of the industry (and therefore on consumer welfare – which is a common consideration in anti-trust cases).  We expect that many schools would need to take a step back from competing at the highest level, unless some system of revenue sharing was put in place.  The challenge would be in creating a revenue sharing or salary cap system across a variety of conferences.  If anyone doubts the challenge this would involve, just consider the case of creating a college football playoff system.  For the last twenty years we have seen the College Bowl Coalition, The Bowl Alliance and multiple versions of the BCS.  Our guess is that this would lead to a system of four or so “super conferences”.  And even within these conferences we might evolve to a Harlem Globetrotters versus the Washington Generals model where perennial winners like Ohio State and Florida finance perennial losers like Illinois and Vanderbilt, so that they have someone to play.

In sum, our speculation is that any move towards paying players would essentially greatly reduce the incentives of many schools to play sports at the highest levels. Opportunities to leverage a school’s brand equity would shift the competitive balance while paying players directly would greatly increase school’s financial risks.  Absent strong revenue sharing mechanisms and some type of salary cap (would college players need belong to a union?) we would guess that a significant set of schools would move to lower levels of competition.  This would limit both consumer choice and, ironically, the choices of prospective student athletes.

Mike Lewis & Manish Tripathi, Emory University, 2013.