Goizueta Bracket Buzz Contest

bracketbuzzIn support of the Goizueta Marketing Communications Group, Manish and I have been asked to predict the game that generates the most “buzz” for each round of the NCAA tournament.  By buzz we mean the most pre-game (the period 24 hours before tip-off) social media noise.  From a marketing research perspective, this is an interesting endeavor.  Social media has great promise as a marketing research tool as it provides a source of organic and unconstrained data on consumer opinion.

During each round, Manish and I will identify the game that we expect to generate the most fan interest and provide some logic for our choices.  For example, in the first round my pick is the Kentucky-Kansas State matchup.  From the UK side, I think this game will generate the most noise because Kentucky has probably the most unhinged and irrational fans (a nicer man would say passionate) in all of college basketball.  These fans should be especially eager given last season’s early exit from the NIT.  Kansas State also has a deep tradition, and the fans are likely to find the UK matchup intriguing.  The matchup also includes coaches that embody the best and worst of college basketball.  Manish’s first round pick is the Ohio State-Dayton matchup.  Dayton basketball had the most loyal fan base among the non-major conferences in our previous study.   The Ohio State University has a large following, and this is a matchup of two schools in the same state.

To assess buzz, we will use a social media monitoring tool called Topsy Pro to track all of the pre-game mentions on Twitter for each game.  Click here to learn more about the buzz contest.

Why The NCAA Needs To Pay Former Players, Not Just The Current Ones

The continuing debate about whether high-level collegiate basketball and football players should be paid seems to be moving in the direction of these athletes receiving some form of compensation above their scholarship.  In the last year we have seen steps towards forming a college athletes’ union, and increased rhetoric from the Big Five conferences about the need to start providing increased compensation to athletes.  (Of course, a cynic might view the statements by the Big Five conferences as justification for gaining increased control over lucrative television dollars)

At one extreme, we have folks that advocate for no additional payment beyond the athletic scholarship.  An increasingly popular viewpoint is that athletes should be provided an additional living wage type stipend.  At the other extreme, we have individuals that advocate the use of a professional sports-type model.  For example, Roger Noll used a 50-50 revenue split (similar to that used in the NBA) to value player contributions as part of the Ed O’Bannon lawsuit.

A complicating factor in this debate is that the structure of consumer demand is possibly very different between college and professional sports.  Our specific concern is that the affinity between graduates and their colleges may mean that colleges start with more natural and stronger fan bases.  As an example, consider the difference between the University of Florida and the city of Jacksonville.  A UF graduate is by definition a member of the “Gator Nation”.  The graduate belongs to a community of graduates that may tend to use the university’s football team as a natural focal or bonding point.  In contrast, a resident of Jacksonville is supposed to root for the Jaguars merely because of where they live.  Of course, this is a simplification, but hopefully our point is clear.

One way to test the preceding conjecture regarding natural and stronger fan bases is to analyze the relationship between team winning percentage and team revenues for both college and professional sports.  If the relationship between revenues and wins is the same for the professionals and colleges, then it makes more sense to view the college game as essentially a professional league.  If there is no relationship between revenues and wins at the college level, then player quality doesn’t matter (and consequently players probably shouldn’t be paid).

In honor of the upcoming NCAA Men’s Basketball Tournament, we modeled the relationship between revenues and win percentage for the NBA and Division 1 Men’s Basketball programs using data from the last decade.  The models for each league had similar inputs or specifications.  The dependent variable in both models was revenue.  In the case of the NCAA, we used the revenue attributed to men’s basketball in each school’s annual Title IX filing.  For the NBA, we used an estimate of home ticket revenue: average ticket price multiplied by home attendance.  In the case of the NBA, the home box office revenue is a proxy for overall revenues (the correlation between our home revenue estimate and Forbes total revenue estimates is about 0.8).  The explanatory variables for each equation included current season winning percentage, past playoff (or NCAA Tournament appearances), past championships, arena capacity, metro area population (or student population), and team level fixed effects (also conference fixed effects for colleges).  Finally, we also used log transforms on winning percentage and revenues so that the coefficients could be interpreted as elasticities.  An elasticity tells us how much one variable (revenue) changes as a function of another variable (wins percentage).

Elasticity Graphic

Our results suggest that NBA revenues are twice as sensitive as college basketball revenues to winning rates.  In the case of the NBA, the elasticity of revenues to win percentage was 0.20 and the R-squared for the model was 0.83.  At the college level, the elasticity was 0.097 and the R-squared was 0.90.  The college model also included an interaction term between winning percentage and membership in a major conference (ACC, SEC, Big 12, Big Ten, Big East and Pac 12).

Where does this leave us in the debate of how much to pay players?  We will defer on providing an exact percentage because doing so would require several more analyses and even more assumptions.  But, it does appear that the two extreme points of view that we mentioned earlier are misguided.  The college players do generate significant revenues, but their degree of responsibility for revenues is far less than the professionals.

One interpretation of our model is that it speaks to the different roles of brand equity in sports revenues.  At the pro level, revenues are twice as sensitive to winning rates as at the collegiate level.  Our feeling is that college revenues are driven more by the permanent nature of the fan base, and by the brand equity created over time.  We have made an earlier argument along these lines, that while Ed O’Bannon should be able to profit from the use of his image, the revenues that would be generated would have as much to do with Kareem Abdul Jabber and Bill Walton as they have to do with Ed O’BannonSo what should be done?  We would like to see a three-way split of revenue.  The colleges get their share, the current players get a piece, but the players that built the college brands should also get something.  As professors that have seen the difficulties of obtaining an education while playing a major sport, we would like to see some type of program that at a minimum provides educational grants for past players.  Furthermore, given that we seem to learn more about the health consequences of big-time football each day, it also seems reasonable to establish a trust fund for future player health issues.

Mike Lewis & Manish Tripathi, Emory University, 2014.

What Schools Recruit the Best, the Worst and Perhaps a Bit too Well?

For the final entry in our college basketball recruiting series we have taken a look at how well different schools recruited for the period from 2002 to 2011.This is the culmination of our other analyses that looked at factors that are expected to affect recruiting such as a team’s fan base support and ability to convert recruiting hauls into draft picks.  In this last entry we take a look at how schools recruit versus how we would expect schools to recruit.

What do we mean by “expect schools to recruit?”  Basically, our premise is that recruits are interested in playing for teams that have supportive fan bases, play in high profile conferences, are successful on the court, have significant financial resources, produce NBA players and have storied histories.  Our analysis begins with a model that predicts recruiting results (we use Rivals recruiting points as the dependent variable) as a function of these factors (revenues, last season winning rates, previous NCAA tourney appearances, previous final fours, recruit conversion into draft picks, conference, etc…).

We then compare a school’s actual recruiting results with the model’s prediction for each year in the data.  We then look at the ten year average of the difference between the actual and the predicted results (the residuals) to classify schools as over and underachievers. Because our results have the potential to stir up emotions, before we get into specific results we should make a couple of points clear.  First, the meanings of over achieving and under achieving recruiting results can be interpreted in multiple ways.  One interpretation is that schools (and coaches) that “over” achieve do a great job in attracting recruits.  However, given that the model controls for factors such as winning rates, being on the list of over achievers can also imply that the school underachieves on the court with the given talent.  Likewise, at the bottom of the list, “under” achieving can be interpreted as either lousy recruiting or an ability to get the most out of recruits.

The Top 10 list for the high majors is led by Texas at number one (I can almost hear Texas fans saying that this proves that Rick Barnes is a poor game coach), UCONN at 2, Florida at 3, Villanova at 4 and Memphis at 5.  Duke was number 10.

At the very bottom of the list of high majors we have Boston College, Houston and Arkansas.  In the cases of Boston College and Arkansas, these are fascinating results.  These schools regularly make the tournament and win games.  They just don’t seem to be able to draw elite recruits.  If I am a college AD looking for a new coach, I would take a close look at the coaches at these schools.  Perhaps these are coaches that if surrounded by super start recruiters could build elite programs.

While we aren’t going to spend much time on the mid majors in this analysis, our analysis did yield one very interesting finding for this group.  The school at the very bottom of the list is Butler.  Again, this is a result that can be spun in either direction.  Perhaps Brad Stevens is truly a basketball savant who can succeed with any players.  Alternatively, maybe schools like Illinois and UCLA dodged a bullet because Stevens would not have been able to recruit at the high major level.

Finally, maybe the most interesting element of our analysis is that we are able to identify recruiting results that are statistically unlikely.  If we agree that our model captures the key drivers of recruiting (expenditures, revenues, past success, current success, conference affiliation, conversion of recruits to NBA picks, etc…) then exceptional recruiting hauls should be a bit troubling.  These unusual results mean that either a given coach or program have a “specialness” not included in the model.  We will let readers speculate as to what this “specialness” might be. Our list includes three programs: Kentucky, Texas and Villanova.

The Kentucky results are especially dramatic.  Our calculations (which are a bit of the back of the envelope variety) suggest that the probability of Kentucky’s results occurring by chance is just 1%.  But again, we do acknowledge that there may be something special about this program that our model doesn’t capture.  However, we should also note that we do not find a similar “specialness” for schools such as North Carolina, Kansas, Duke and UCLA.  And to take things just a step farther if we just look at John Calipari’s results across Memphis and Kentucky our estimated probability of his recruiting results is less than .1%.  As before we acknowledge that we may be omitted a variable or two that captures coach Calipari’s recruiting gifts, but our model doesn’t identify other high powered recruiters such as Thad Matta, Bill Self or Coach K as outliers.

Mike Lewis & Manish Tripathi, Emory University 2013

College Basketball Recruiting Series

One of my (Lewis) favorite things in sports is college basketball recruiting.  Given the growth of the recruiting guru industry, it’s safe to say that I’m not alone in my fascinations.  For example in the case of the University of Illinois, If you took a look at the message boards you might think there is as much interest and speculation about the recruitment of Cliff Alexander (the number 3 ranked player in the 2014 class) as there is in the this year’s team.

Over the next couple of weeks, our plan is to take an in-depth, data-based look at the world of college basketball recruiting.  Our emphasis will be on judging how well teams really recruit and whether players make rational decisions about where to play ball.  As always, the key to these analyses will be that we will use statistics and data to go beyond the conventional wisdom and drill down to the fundamental issues.

As a starting point for our series, we are re-running an earlier analysis that looked at fan support across teams. This study is important for two reasons.  First, intuitively we expect that players will be more attracted to programs that have strong support.  This is a rational criterion because support likely translates to plentiful resources and television exposure.  Second, this study highlights the nature of our approach to these studies.  Rather than rely on simple metrics such as attendance, that are a function of team performance we examine fan support after controlling for short-term fluctuations in team performance.  In other words, we control for the fact that it is easy to be a Duke or Kansas fan, while it takes real character to support a team that may struggle on the court (e.g. Maryland & Illinois).

We have four analyses planned.  As noted the first one focuses on the “fan equity” enjoyed by each teams.  These rankings provide a sense of the customer or brand equity of each team.  The second analysis will take a look at each school’s ability to produce NBA draft picks as a function of their recruiting rankings.  This is something that recruits should definitely consider.  The third analysis will examine draft pick production as a measure of team success.  This analysis really gets at the value of choosing a high profile, blue blood program.

The fourth analysis is probably the one that we are most enthusiastic about.  In the fourth study, we examine recruiting success after controlling for a myriad of factors such as current winning percentage, markers of historical success and financial investment.  As we will discuss later this analysis as some significant implications for how we should evaluated coaches and may even provide some evidence that some teams recruit “too” well.

Mike Lewis & Manish Tripathi, Emory University 2013.

Basketball Conference Realignment: Winners and Losers

College sports are changing rapidly.  From the soon to be instituted college football playoff to the potential changes the Ed O’Bannon lawsuit forces on schools, we are clearly in a time of change.  The subject of today’s post is another example of these changes, as our focus is on conference realignment.  The cynic, who in this case would be correct, would say that the realignment activity of the past few years has been driven by money.  It has been the quest for new television markets (Rutgers to the Big Ten) and powerful brands (Nebraska, also to the Big Ten) that has led some conferences to grow, and for many teams to make moves.

The topic of realignment is top of mind today because it is the first day of the American Athletic Conference.  This new AAC is largely comprised of refugees from the Big East and Conference USA.  Today’s analysis looks at how the shuffling across conferences has increased the overall brand equity of each league.  For this analysis we use the results of our previous college basketball brand equity analysis.  The one significant change is that for this analysis we do not separate out the conference effects when computing team-level brand equity.  Each league’s rank is then the sum of its teams. We perform the analysis for both 2012 and 2014.

The analysis yields some expected and surprising results.  The Big Ten leads the way both in 2012 and 2014, with the ACC following behind in both years.  However, while the Big Ten has a large lead in cumulative brand equity in 2012, the gap is almost negligible in 2014 (In terms of percentages the brand equity of the ACC basketball programs was 81.7% of the Big Ten’s in 2012, but with the changes scheduled to occur, the ACC will have 97.2% of the Big Ten’s equity in 2014).

Of course, the most interesting part of the table concerns the new Big East (Catholic 7) and the new American Athletic Conference.   The Big East drops from being the 3rd ranked conference to being the 6th best conference in 2014.  However, it should be noted that this drop is primarily due to the reduction in the league size. In terms of average equity the remaining Big East schools still have the 3rd highest average score.

For the new American Athletic Conference the story is not very hopeful.  The new American Athletic Conference is projected to rank 9th behind the power 5 conferences, the Big East, the Mountain West and the Atlantic Ten.  This was a somewhat surprising finding given that the American Athletic Conference will still contain schools like Cincinnati, Memphis, and UCONN.  But the numbers suggest that Dayton, UNLV and New Mexico have sufficient fan equity to move their leagues past the American Athletic Conference.

The other big story is the positions of the PAC 12 and the Big Twelve.  In 2012, the Big Twelve had a 22% advantage in terms of brand equity, but we forecast that in 2014 it will trail the PAC 12 by 7%.  These types of changes are important as there is a bit of a game that occurs within conferences.  Schools in weaker conferences are likely to have a greater incentive to jump to stronger leagues because they fear being left in a dying league without great options.  The Big Twelve has recently lost Colorado, Texas A&M and Missouri.  If Texas were to leave, the conference would likely disintegrate.

We would also like to make a couple of notes regarding some assumptions implicit in the model.  Our use of revenue premium based brand equity as of 2012 means that each school’s brand equity can be viewed as partially a product of their affiliation in that year.  This is important if a league’s value is more than just the sum of its teams.  For example, the Big Ten pursued Rutgers largely to secure entry into the NY television market.  The logic behind this move would seem to be an assumption that competition with Big Ten teams will improve Rutgers’ attractiveness within the market.  Our analyses do not (as of now) include this type of potential synergy.  The new ACC has at least partially adopted a television based strategy as the members are widely distributed across the nation.  The hope has to be that this cross country coverage creates synergies that simultaneously create interest in the teams and the league.  However, given the current lack of brand equity and the aggressiveness of stronger leagues to form lucrative television networks, this will be a tough haul.


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!


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.



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.



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.