Previsión de ventas

The panel dataset used in this paper’s empirical analysis is constructed using data from the various sources outlined below so that the unit of observation is the university’s football or basketball team in a particular year. Since I am looking at the revenues generated by a university’s football or basketball team, I will often refer to the team and the university synonymously.

2.3.1 Revenues Data

The U.S. Department of Education, under the Equity in Athletics Disclosure Act (EADA), requires all institutions with intercollegiate athletics programs that receive federal student aid funding to report their athletic program revenues. These data are collected separately each year through an online survey for revenues attributable to both men’s and women’s athletics across all sports offered at the institution over the academic year. For instance, these revenues data coded with calendar year 2003 are revenues generated for the academic year beginning in the fall of calendar year 2003 and ending in the spring of calendar year 2004. Although these data includes both revenues and expenditures, I am not able to use both revenues and expenditures to compute the profitability of each university’s sports pro- gram. This is because the EADA survey specifies that the total reported revenues must cover total reported expenses.47 However, since I am interested in estimating the marginal revenue product of a star player, I focus just on the reported revenues and collect revenues for each institution from 2003–2012 that are attributable to Division 1 FBS college football and Division 1 men’s basketball programs.

The revenues reported in the EADA survey are for all revenues attributable to a university’s

47

See Getz and Siegfried (2012) for a discussion of how accounting for revenues and expenses in academic institutions is quite tricky, partially due to how revenues and expenditures are accounted for across different departments within an academic institution.

college football or basketball program and includes: revenues from appearance guarantees and options, an athletic conference, tournament or bowl games, concessions, contributions from alumni and others, fund-raising activities, institutional support, program advertising and sales, radio and television, royalties, signage and other sponsorships, sports camps, state or other government support, student activity fees, ticket and luxury box sales. Rev- enues include more than earned income, such as gate receipts, and the basis for determining whether revenue should be included is simply whether the item was attributable to the university’s football or basketball program activities. Furthermore, these reported revenues are actual amounts earned or received, not pledged, budgeted, or estimated amounts. What isnot included in the revenues data are capital assets and related debts (i.e. money specif- ically identified to pay for capital assets) or money for indirect facilities.

The advantage of using these revenues data is that they are available over a long time hori- zon for the entirety of universities fielding Division 1 FBS football and Division 1 men’s basketball teams and they include a comprehensive list of revenue sources. The downside of using these data is that revenues are not reported by category so that only an aggregate revenue measure is available. Brown (2011) uses college football program revenues collected by the Indianapolis Star for the 2004-2005 season that are disaggregated into categories like ticket sales, game day sales, contributions, and NCAA conference distributions that he claims are more likely associated with the quality of a team’scurrent players. His contention is that if the aggregate revenues measure includes things like students fees, government and institutional aid or endowment/investment income that are associated with past team qual- ity and less dependent on the quality of the team’s current players, then the analysis might overstate the effect of current players on team revenues. This observation is likely correct although, given the panel structure of my data, these concerns can easily be controlled for by including measures of past team performance as well as year, conference, and team fixed effects. This is not possible with the Indianapolis Star data that only covers one season, limiting the analysis to the cross-section.

Finally, revenues are reported in nominal U.S. dollars and I convert them to real 2012 U.S. dollars using the headline Consumer Price Index for all urban consumers computed by the Bureau of Labor Statistics. Tables 8 and 9 report summary statistics for football and basketball program revenues, along with other variables used in the empirical analysis. From the tables we see that the average Division 1 FBS football program over the sample 2003–2012 generated almost $24 million dollars in annual revenues with average revenues for 90% of teams being less than $55 million. Not surprisingly, the revenue distribution is right- skewed as a few teams generate very large revenues. For instance, the University of Texas at Austin tops the list with just over $111 million in 2012. Likewise, over 2003–2012, the average Division 1 men’s basketball program generated almost $4 million dollars in annual revenues with average revenues for 90% of teams being less than about $10 million. As with football program revenues, the revenue distribution for basketball programs is right-skewed with a few teams generating very large revenues. The University of Louisville’s basketball program tops the list with just over $44 million in revenues for 2011.

2.3.2 Sports Statistics Data

For each academic year and the 104 Division 1 FBS football programs that I have rev- enues data for, I collect team performance statistics from Sports Reference.48,49 Particular statistics of interest that will be used to construct control variables for the empirical iden- tification strategy are: wins, the current coach, the team’s bowl game appearances and performance, and the team’s schedule strength.50 Several measures of the team’s defense quality are also collected including: points allowed per game, total yards allowed per game,

48

Each academic year corresponds to the football or basketball season over the same time period. 49

Sports Reference LLC. 6757 Greene St. Suite 315 Philadelphia, PA 19119. The football data are accessed from http://www.sports-reference.com/cfb/, while the basketball data are accessed from http://www.sports- reference.com/cbb/.

50_{This statistic is denominated in points above or below average where zero is the average. For details}

on how schedule strength is computed for football, please see http://www.sports-reference.com/cfb/about/ glossary.html.

passing and rushing yards allowed per game, and passing and rushing touchdowns allowed per game. The Sports Reference website also contains historical information on each college football coach’s win record over their entire career, which are collected and included with the team performance data. In addition to team level data, I collect performance statistics for 25,221 individual football players over the 2003-2012 sample period. These data include the number of games played by each player in each season along with various performance statistics like touchdowns and yards for offensive players and accolades such as if the player was voted to the All-American Team or nominated for the Heisman Trophy.

I also collect team performance statistics from Sports Reference for each season and for each of the 282 Division 1 men’s college basketball programs that I have revenues data for. In particular, I collect: wins; the current coach; the team’s NCAA tournament appearances and performance; the number of teams in each athletic conference that are ranked by the Associated Press in the NCAA tournament that year, their tournament performance, and the total number of teams in each conference; and the team’s schedule strength.51 _The

Sports Reference website also has historical information for each college basketball coach’s win record and NCAA Tournament appearances over their entire career, which are collected and included with the team performance data. In addition to team performance data, I collect performance statistics for 18,855 individual basketball players over the 2003–2012 sample period. These data include the number of games played by each player in each season, the number of points scored by each player in a season and accolades such as if the player was voted to the All American First or Second Teams, was awarded the Naismith or Wooden Awards, or named most outstanding player in the NCAA Tournament.

51_{For details on how schedule strength is computed for basketball, please see http://www.sports-reference.}

2.3.3 Star Player Measures

If we are interested in measuring a college athelete’s MRP to inform the debate on whether or not college athletes are being unfairly exploited, we need a way to separate an individual player’s contribution from the team’s contribution to revenues. Conceptually this is a difficult task since a football or basketball team is a collection of individuals whose direct individual performances and complementarities with the performance of their teammates all influence the team’s ability to generate revenue. One way we might attempt to distinguish between the individual player and the team is to focus the analysis on exceptionally good players measured by some metric of performance. While this will not allow us to estimate the MRP of an average player on the team, focusing on the very best players that would command the highest wages in a competitive labor market will give us an upper bound on the economic rents being extracted from players by NCAA member institutions.

Star Football Player Measures

I construct six different measures of star player using performance statistics for 25,221 in- dividual football players. The first measure is if the player was selected to the consensus All-American Team. Selection to the consensus All-American team is an honor given each year to the best college football players at their respective positions. Selection to the team is recognized by the NCAA and determined by a group of selector organizations.52 The All- American measure of star allows me to measure star players acrossall positions in football since the best player in each position are voted to the All-American team. The second and third measures are if a player was a Heisman Trophy finalist or nominated for the Heis- man Trophy. The Heisman Trophy is an award given each year to the most outstanding player in college football. Selection for the Heisman is determined by sports journalists,

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Since 2009, the full list of selector organizations are: Associated Press, Football Writers Association of America, American Football Coaches Association, Walter Camp Foundation and The Sporting News. If three of these organizations select a player, he automatically receives the consensus honor.

previous Heisman winners, and a fan survey collected by ESPN.53 Typically, around ten players each year are nominated for the Heisman Trophy and I have defined a finalist as a player who finished in the top five or better in the Heisman voting among all players who were nominated. Unlike the All-American team measure, these measures are restricted to Quarterbacks, Running Backs, and Wide Receivers since these positions make up 90% of all positions nominated for the Heisman over the period 2003-2012.54

The last three measures of star player are computed from individual player performance statistics. One downside to using performance statistics is that there is not very good data coverage for positions other than Quarterbacks, Running Backs, and Wide Receivers. Al- though this may seem like a limitation, it may also imply that these are the positions that people focus their attention on. So to the extent that star players are able to generate revenues through their salience to fans, it is likely that not much is lost by focusing on these positions here. In determining how to measure star players based on performance, I choose to focus on touchdowns scored and yards generated because these are likely to be the more visible metrics that directly impact a team’s ability to win games, play in lucrative bowl games, and generate fan excitement. Hence, the fourth measure of star player is if a Quarterback, Running Back or Wide Receiver was among the top 10 players in scoring touchdowns or generating yards within their position for that season. I choose the simple rule for being in the top 10 in one or the other category (or both) to avoid having to ar- bitrarily pick relative weights in combining statistics, which would otherwise be needed for an index measure to rank players according to multiple statistics.

The last two measures of star player are the same for Running Backs and Wide Receivers (top 10 in touchdowns or yards) but changes how star Quarterbacks are defined. The fifth

53

For detailed information of the balloting and selection process please see http://heisman.com/sports/ 2014/9/15/GEN 0915140346.aspx?.

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There are a total of five Defensive Linemen, four Linebackers and one Defensive Back nominated over the ten years in the sample.

measure designates a Quarterback as a star if they are among the top 10 Quarterbacks in touchdowns, or yards, or in their pass efficiency rating (PER). The PER is a common met- ric used in sports statistics to rank Quarterbacks by taking into account interceptions, pass completions, and pass attempts in addition to yards and touchdowns.55 The sixth measure designates a Quarterback as a star if they are among the top 10 Quarterbacks ranked only by their PER rating.

Table 10 gives a sense of how rare these star players are over the sample period for each definition of star player. For example, All-American players make up just under 0.5% of all players while Heisman Finalists are the most rare with only 0.08%. Among Quarterbacks, All-American Quarterbacks are the most rare (0.37%) since there are only 11 of them cho- sen whereas Quarterbacks are overrepresented among players nominated for the Heisman Trophy.56 Even for the most permissive category (5), star players comprise about only 1% of all players and 3% of all Quarterbacks, Running Backs, and Wide Receivers. Also, only 6% of Quarterbacks are designated as star players under measure (5) while star Running Backs and Wide Receivers comprise 2.5% and 2% of players in their positions. Although how one defines a star player is somewhat subjective, the purpose of presenting multiple measures is to see how estimates of the MRP change depending on the definition since the question under consideration is relative to how star players are defined.

While the previous literature has primarily focused on future NFL draftee status to define a star college football player, I prefer the measures in Table 10 for several reasons. First, these measures of star player are better able to capture a player’s potential contribution to team revenues in each year they played for the team. Strictly speaking, the NFL Draft reflects professional scout expectations of future performance at the professional level verses

55

The forumula is (8.4×Y ards+330×T ouchdowns−200×Interceptions+100×Completions)/Attempts.

56_{Typically there is just one All-American Quarterback per year, however, the reason there are 11 and not}

10 is that there were two Quarterbacks chosen in 2008 as the six selector organizations were equally divided over Sam Bradford and Colt McCoy.

the player’s actual performance in college relative to their peers in a given year. Hence, draft status is not directly connected to a player’s performance in a particular year in which they might be contributing to school revenues other than in the season immediately before they were drafted.57 There are also cases of outstanding college athletes that do not fare well in the NFL draft. For example, Oklahoma quarterback Jason White won the Heis- man in 2003 and led his team to the national championship but was not selected in the NFL draft.58 _{Even more problematic is that NFL draft status may have less to do with}

college performance than we think. Berri and Simmons (2011) looked at 121 quarterbacks from 1999–2008 and found that nearly 20% of the variation in Quarterback draft position is explained by just the NFL combine factors.59 When performance measures like wins produced, net points and Quarterback score are added explanatory power only rises less than 3%. Overall they find that combine factors appear to be more important than the actual college performance of the Quarterbacks in terms of NFL draft pick.

The six measures of star football player just mentioned are “discrete” in the sense that they do not account for the fact that there might be variation in how much stars are contributing to a team’s revenues. For instance, suppose two players are among the top 10 players in touchdowns or yards in a given year but one only played in half their team’s games while the other played in all their team’s games that season. The current discrete measures will treat these two players as identical since the star designation is binary. However, my dataset contains the number of games played by each player as well as the number of games played by the team in a given season. Therefore, the richness of my dataset allows me to compute a more precise “continuous” measure of star player by multiplying the binary star designation by the proportion of games played by that star player in that season. So

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Alternatively, I could use the NFL draft to designate a player as a star foreveryyear they played college

football if they were drafted in their last year. However, if star players actually have an effect on revenues such that revenues are higher when a team has a star player, this measure will likely underestimate a star players effect on revenues as he will be mechanically designated a star player in years of low or average revenues independent of his performance in that year.

58_{Hunsberger and Gitter (2014) page 4.}

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in the previous example, one team would have 0.5 of a star player while the other team would have 1 star player. Likewise, if one team had three star players, one of which only played in 75% of the games that season, the team would have 2.75 star players under this continuous measure rather than 3. This continuous measure is more precise in allowing for a star player’s contribution to team revenues in a season than the discrete measure. The continuous measure also allows for more variation in the number of star players on a team, which will result in more precise estimates of a star player’s MRP.60

Star Basketball Player Measures

Using the performance statistics for 18,855 individual basketball players I construct eight different measures of star player. The first measure (Award Winners) is if the player was