Conference Call! NCAA Conference Realignment and Football Game Day Attendance. By: Mark Groza Spring 2007
Table of Contents Acknowledgments 3 Abstract 4 Introduction..5 Literature Review. 6 Empirical Model.. 8 Data Description....10 Results... 12 Conclusion.14 Bibliography..17 Appendices 18 2
Acknowledgments I would like to thank Dr. Francesco Renna for his guidance throughout the process of writing this paper. Dr. Renna is a tremendous motivator and is able to keep his students, including me, on task and on schedule. I would also like to thank Dr. Randall King for his help in forming a model and interpreting the results. My curiosity in Sports Economics is a direct result of Dr. King. Dr. King has evolved into the resident expert in Sports Economics at the University of Akron and currently teaches a Sports Economics section during the summer. 3
Abstract Between the 2004 and 2005 football seasons, 17% of Division I-A college football teams switched conference affiliation. Since the decision to change conferences is voluntary, it is safe to assume universities made this decision in order to increase the profile and therefore revenue base of their athletic departments. Football is by far the biggest money making sports for Division 1-A athletic departments. This study attempts to estimate the importance of conference affiliation and the impact a change in conference affiliation has on football game day attendance. The results of this analysis indicate that while certain conferences enjoy greater attendance, switching conference does not result in a short run increase in football game day attendance. Keywords: NCAA football, football revenue, conference realignment, game day attendance 4
1. Introduction Collegiate athletics have long played an integral role in higher education in the United States. The National Collegiate Athletic Association (NCAA) was created over a century ago to create a uniform set of rules in college football and to protect the integrity of college sports. Today the NCAA is comprised of a number of divisions which are divided based on the size and the competitiveness of its member institutions. In football, the NCAA s Division I-A consists of the nation s most competitive and profitable football teams. As of the 2006 football season, 119 universities were among the ranks of Division I-A. All but four of the 119 universities belong to 11 conferences across the country. Between the 2004 and 2006 seasons 21 football teams in the Division 1-A changed conference affiliation. In 2006 17.5 percent of the football teams in Division 1- A were playing in a different conference then they were in 2003. College football is a multi-million dollar a year industry. In 2005 29 Division 1- A football programs netted over $10 million in profits. During that same season another 36 football programs netted over $1 million in profits. In total, the 119 division 1-A football teams netted over $721 million during the 2005 football season 1. Due to football s incredible earning power, it is often used to fund non-revenue sports in athletic departments. As a result of federal legislation, athletic departments across the country are required to keep many non-revenue sports in existence; 2 this puts a tremendous amount of pressure on the budgets of athletic departments. Athletic directors continually look to their football programs to carry this financial burden. Football 1 Department of Education, Department of Post-Secondary Education, EADA survey. 2 Title IX requires universities across the nation to offer an equal number of athletic scholarships to women as they do to men. This forced athletic department to increase the number of women s sports, most of which loose a great deal of money. ---Title IX, Education Amendments of 1972 5
programs generate a great deal of their revenue through gate receipts. Football game day attendance is an excellent proxy for revenue. Teams with a large fan base are able to generate more apparel sales, get invited to bigger bowl games, and negotiate better TV contracts. With all of this being said, athletic departments want to maximize football attendance. The goal of this paper is to estimate the impact conference affiliation has on game day attendance, and to see the affect changing conference affiliation has on game day attendance. The paper is organized as follows. A brief review of the pertinent literature is contained in the next section. The empirical model is outlined in section 3. The data sources and data descriptions are contained in section 4. The results of three separate estimations are contained in section 5. Finally, section 6 includes the conclusions and limitations of this analysis. All relevant tables are located at the end of the paper following the bibliography. 2. Literature Review A large body of research has confirmed the importance of intercollegiate athletics on universities across the nation. Research has indicated universities can attract better academic students by sponsoring a successful football program. For example, McCormick and Tinsley (1987) concluded football programs that are successful for a 10-15 year period strongly increase SAT scores of incoming freshmen to their respective universities. This conclusion was reaffirmed by Tucker (2005). Tucker (2005) concluded that a successful football program has the ability to attract more and better applicants in as little as five years. 6
Equally important to advertising and attracting potential students, successful college athletic programs generate money for their given universities. Rhoads and Gerking (2000) concluded that there is a strong positive correlation between a bowl win and alumni contributions to their university. These contributions are not exclusively earmarked for the football or athletic programs but rather to the universities general fund. Research indicates alumni and lay fans alike are much more likely to contribute money to their school if its football team is having a successful season. However, many Division 1-A football programs lose money each year. This is a trend athletic departments and universities would like to see reversed. While alumni donations are a big part of the revenue created for universities by their athletic departments, the most important source of revenue for the athletic department is football gate receipts. Price and Sen (2003) estimated a demand model for game day attendance in Division 1-A football. They attempted to predict the factors that influence game day attendance. They concluded that of the broad range of factors that affect football attendance conference affiliation has by far the strongest influence. Kabir and Sen (2003) predicted the football team s conference has a strong impact on game day attendance. To reach this conclusion, they performed a cross-sectional analysis of the 1997 football season. The analysis in this paper uses pooled data across three seasons in order to verify the robustness of Kabir and Sen s (2003) conclusion Besides Kabir and Sen, (2003) very little research has been done on the importance of conference affiliation on a university and its football program. Maxcy (2004) argued the NCAA s power conferences act as a monopoly and enjoy incredible revenues. The six so called power conferences are very good at consolidating money and 7
power. 3 Other works have dealt with competitive balance across conference lines. Sutter and Winkler (2003) concluded that despite attempts to create parity in college football great competitive imbalances still exist. These works have dealt with the monopoly nature of large conferences. An in-depth analysis of the impact changes in conference affiliation has on game-day attendance is an area scholarly research is lacking. 3. Empirical Model There are two main objective of this analysis. The first is to accurately estimate the importance of conference affiliation on football game day attendance, and the second is to estimate the impact a conference change has on game day attendance. As Kabir and Sen (2003) concluded there are a number of factors that impact game day attendance. These factors include: characteristics of the home team, characteristics of the visiting team, when the game was played, and conference affiliation. Considering these things the equation that predicts the influence of conference affiliation on game day attendance is as follows 4 : LogATT i,t =β i,t +β 2 HOMET i,t +β 3 OPPT i,t +β 4 TIME i,t +β 5 DCONF i,t +ε i,t (1) LogATT represents the total game day attendance for each team s home games in log form. This figure includes all paid and unpaid attendance as reported by the NCAA. HOMET is a set of five variables that represent characteristics of the home football team. These variables include: LogSTDSIZE, the size of the home team s stadium in log form; HTBOWL, the number of bowl games the home team has played 3 The six power conferences make up the BCS Bowl Championship Series. These conferences include the Big Ten, Big East, Big Twelve, Southeastern Conference, Pacific Athletic Conference and the Atlantic Coast Conference. 4 While traditional demand models include price and income those variables are omitted from this model for one main reason: It is nearly impossible to estimate the average price paid per ticket by fans. Many fans get in to games free (i.e., faculty, staff, students) and football tickets are often sold at different prices. Athletic departments do not keep public records of what percentage of fans get into home games free. 8
in; LRWINPCT, the winning percentage of the home team over the previous three seasons; LASTSEASON, the winning percentage of the home team the previous season; STRENGTH, the strength of schedule played by the home team. It is predicted that all of these variables have a positive influence on game day attendance. Historically good teams that play in many bowl games, and play difficult schedules should have more game day attendance then teams that do not. OPPTEAM is a set of two variables that represent the quality of the visiting team. These variables include: OPPPRVSEA, the winning percentage of the visiting team the previous season; OPPLRWINPCT, the winning percentage of the visiting team over the previous three seasons. Since the quality of the visiting teams draws more fans to games, the sign for all of the variables in OPPTEAM are expected to be positive. TIME encompasses three variables that represent when the game was played. SATURDAY is a dummy variable that takes the value of 1 if the game was played on Saturday and a value of 0 if the game was played on any other day of the week. NIGHT is a dummy variable that takes the value of 1 if the game was played at night and a value of 0 if the game was played during the day. It is unclear as to the impact on attendance these two variables will have. Fans may like the traditional Saturday afternoon game or they may prefer the novelty of a weeknight game. Finally, YEAR are dummy variables that represent which season the game was played. The 2006 football season is used as the base year in all of the models. The predicted sign for the variables in TIME are either positive or negative. 9
DCONF is a set of dummy variables that represents the home team s conference. 5 To allow this regression to be full rank the dummy variable that represents the South Eastern Conference is excluded from the model. Therefore, the South Eastern Conference acts as the base conference. The second goal of this paper is to estimate the impact changing conferences has on game day attendance. In order to estimate this, a model similar to model 1 is constructed. The major difference is CHG_TO_CONF replaces DCONF. CHG_TO_CONF is a set of dummy variables that represents which conference the individual team moved into. 6 Model 2 is an ordinary least squared model (OLS), estimating the impact moving into certain conferences will have on game day attendance. LogATT i,t =β i,t +β 2 HOMET i,t +β 3 OPPT i,t +β 4 TIME i,t +β 5 CHG_TO_CONF i,t +ε i,t (2) There is a fundamental statistical limitation to model 2. There are unobservable characteristics that are specific to each team and model 2, which is a simple OLS, does not allow each team to have its own constant term. In order to correct for this limitation a third model is created using the fixed effect method. This fixed effect method allows each team to have its own constant term. DT represents a set of dummy variables for each of the 119 football teams. LogATT i,t =β i,t +β 2 HOMET i,t +β 3 OPPT i,t +β 4 TIME i,t +β 5 CHG_TO_CONF i,t +β 6 DT i,t +ε i,t (3) 4. Data Description Due to NCAA regulations, highly accurate attendance figures are kept by every college football team and then reported to the NCAA. Attendance data for the three seasons used in this analysis is gathered from the NCAA s official website 5 See appendices: Table 1 6 See appendices: Table 2 10
(NCAA.org/stats). The season included in this analysis are the 2004, 2005 and 2006 football seasons Over the course of those three seasons 2,219 total games were played in Division 1-A. 179 of those games were played against members of the NCAA Division 1-AA (a lower division). To protect against anomalies, and due to lack of available information about Division 1-AA teams, these 179 games are excluded from this analysis. Of the 2,040 games played among Division 1-A opponents, 131 were played at neutral sites; these neutral games include bowl games. Neutral games are also excluded from this analysis because there is no specific home team. What remain are 1,909 games over the course of the three seasons. For easier interpretation of the results, attendance is estimated in log form. In addition to attendance figures, the NCAA also keeps very accurate figures on the historical record of the teams in Division 1-A. Each year, the NCAA publishes a Football Records Book. This compilation includes the records of each team over the course of the last few seasons, the total bowls each team has played in, and the size of each team s stadium. The HOMET variables YEARSFOOT, HTBOWL, LASTSEASON, LRWINPCT, and all of the VISTEAM variables were gathered from the 2004, 2005 and 2006 NCAA football records books. The variable STRENGTH, which was a part of the HOMET variables, was collected from the NCAA s statistics website. This variable is calculated by dividing the total wins of all the opponents by the total games played by all the opponents. A specific team that played a difficult schedule will have a high number for STRENGTH. For example, if team A played a schedule where every team they played won every game, 11
STRENGTH would equal 1. Likewise, if team B played a schedule where every team they played lost every game then STRENGTH would equal 0. The TIME dummy variables SATURDAY, NIGHT, and YEAR were created by reviewing each team s individual schedule for each of the three seasons. This information is also available through the NCAA s statistical website. Finally, the variables DCONF and CHG_TO_CONF were created by referencing the Conference Report on the NCAA s official website. If a team switched to a certain conference before the particular season starts, each game played after that time is given a value of 1 for CHG_TO_CONF. The descriptive statistics of all variables included in this study are reported in table 3. 5. Results The OLS results of model 1 are reported in table 4. In this particular model the SEC was used as the base conference and the 2006 season was used as the base season. All but one of the conference dummies yields a parameter estimate that is statistically significant at the 99% level. Model 1 indicates there is a great deal of variation in game day attendance across conference lines. The SEC, Big Ten, Big Twelve and Pac Ten are the four conferences that draw the greatest number of fans to their home football games. The MAC, Sun Belt, WAC, and Conference USA drew the least number of fans. For example on average a MAC game is expected to draw 49% less fans then a SEC game. 7 The result of model 1 confirms Kabir and Sen s (2003) conclusion that conference affiliation does matter for game day attendance. 7 While the MAC coefficient in model 1 was -0.67 the relative effect was -0.49. Because the models used in this paper are Semi-logarithmic Equations the relative effect is not equal to the coefficient of the dummy variable. Halvorsen and Palmquist (1980) affirm this discrepancy in their paper The Interpretation of Dummy Variables in Semi-logarithmic Equations. 12
Table 5 includes the results from model 2. Model 2 included variables that represented into which conference teams moved to. This OLS regression yields a number of statistically significant parameter estimates. From model 2 it appears it matters what conference teams move into. Teams that moved to the Big East experienced increases of attendance by 17.4%. However, teams that moved to the ACC, WAC, Mountain West, and Conference USA lost attendance of 12.5%, 18.2%, 14.2%, and 19.7%, respectively. From the results of model 2 it appears as if the conference realignment hurt all teams that moved except those teams that moved into the Big East. As explained earlier, the results of this OLS may be biased because of heterogeneity. The results of model 3 should be more robust then the results of model 2. The fixed effects results of model 3 are reported in table 6. When the fixed effect is accounted for all but one of the CHG_TO_CONF variables become insignificant. The WAC is the only conference that yields a statistically significant parameter estimate. Model 3 indicates that teams switching to the WAC can expect 26% fewer fans. It appears that moving to the other five conferences had little impact on game day attendance. Interestingly, there were some other variables that were significant and affected game day attendance. The previous season winning percentage (LASTSEAS) positively affected game day attendance. A 10% increase in winning percentage correlates into a 1% increase in fan support. Model 3 indicates fans prefer night games as they draw about 5% more fans then do day games. Also, the variable OPPLRWINPCT is statistically significant; visiting teams that have been successful over the last three seasons contribute to increased game day attendance. 13
6. Conclusion In order to observe the impact conference affiliation has on game day attendance, an OLS regression was estimated which includes a number of variables that are predicted to influence attendance. Included also in this first regression are conference dummy variables, each of which represents a different conference. The results of this regression show conference affiliation, in terms of game day attendance, does matter. Teams that are members of certain conferences draw more fans then teams that belong to other conferences. To observe the impact conference realignment has on game day attendance a second model is created. This OLS model includes variables that represent which conferences teams moved into. As earlier researchers have discovered the results of this type of OLS is biased do to heterogeneity. There are unobservable characteristics that are specific to each team. In order to control for the characteristics, a third regression was estimated. The third, and final regression, allows each individual team to have its own constant term. By creating dummy variables for each team, this regression allows the intercept to change. This fixed effect method gives us a more robust estimate of the impact changing conference affiliation has on game day attendance. From the results of model 1 we can conclude there is undoubtedly variation in game day attendance across conference lines. Teams that belong to larger conferences enjoy greater fan support than do teams that belong to smaller conferences. It is safe to say this conclusion has long been assumed by athletic departments across the nation. This may help to explain why so many teams over the last few years have switched 14
conference affiliation. University officials have agreed to switch conferences in hope of being a part of a conference that draws more fans. By being in a larger conference one would assume attendance would go up. However, as the results of model 3 indicate, moving to a different conference does not, in the short run, guarantee more fans. The results of model 3 indicate changing conferences is not a magic bullet when it comes to game day attendance. Athletic departments should not expect an immediate increase in fan support when switching conferences. There are a few limitations to this study. Do to the lack of time passed since the conference realignment this analysis is only able to show the impact on game day attendance in the very short run. Only two seasons have passed since the majority of movement took place. It is very likely that the real impact of a conference switch can only be seen after five or more seasons have passed. Another possible explanation to the counterintuitive results is that it is possible teams had to prove their drawing power prior to being admitted into the given conferences. In other words, the ACC may have required Boston College to increase their attendance before they were admitted into the conference. Boston College may have worked very hard prior to the 2003 season to increase game day attendance. It is possible teams were admitted to certain conferences because they were able to show their fan drawing power years prior to actually being admitted into the new conference. Future research can build on this analysis by including more seasons to analysis. By adding more seasons after the change, the long run impact of a conference change can be observed. Unfortunately, at this time this data is not currently available. Future 15
research can also used lagged attendance in order to see if in fact an increase in attendance caused the conference change. 16
7. Bibliography Department of Education, Office of Post-Secondary Education. Equity in Athletics 2005 Report. http://ope.ed.gov/athletics/year_search.asp Groza, Mark. The MAC Attack! Game Day Attendance at Mid-American Conference Home Football Games. The University of Akron. Fall 2006. Halvorsen, Robert and Raymond Palmquist. The Interpretation of Dummy Variables in Semilogartihmic Equations. The American Economic Review Vol. 70, No. 3 Jun 1980 pp 474-475. Maxcy, Joel G. The 1997 Restructuring of the NCAA: A Transactions Cost Explanation. Economics of Sports. Edited by John Fitzel & Rodney Fort 2004. McCormick, R.E. & Tinsley, M. Athletics versus academics: Evidence from SAT Scores. Journal of Political Economy. 95. 1103-16. 1987. Price, Donald I. and Kabir C. Sen. The Demand for Game Day Attendance in College Football: An Analysis of the 1997 Division 1-A Season. Managerial and Decision Economics 24: 35-46 2003. Rhoads, Thomas A. & Shelby Gerking. Educational Contributions, Academic Quality, and Athletic Success. Contemporary Economic Policy. Vol. 18 No. 2 April 2000. Sutterf, Daniel & Winkler, Stephen. NCAA Scholarship Limits and Competitive Balance in College Football. Journal of Sports Economics. Vol. 4 No 1 Feb. 2003. Tucker, Irvin B. Big Time Pigskin Success, Is There an Advertising Effect? Journal of Sports Economics. Vol. 6 No 2 May 2005. 17
8. Appendices Table 1. Description of Dummy Variables Variable Explanation SEC South Eastern Conference ACC Atlantic Coast Conference BIGEAST Big East Conference BIGTEN Big Ten Conference CUSA Conference USA MAC Mid-American Conference SUNBLT Sun Belt Conference MTWEST Mountain West Conference BIGTWLV Big Twelve Conference WAC Western Athletic Conference PACTEN Pacific Athletic Conference INDY Independent team 8 8 Independent teams include any teams that were not affiliated with a conference during the 2004 or 2005 football seasons. 18
Table 2. Description of CHG_TO_CONFERANCE Variables Variable c_acc c_bigeast c_cusa c_sunblt c_mtwest c_wac c_indy Explanation Changed to the Atlantic Coast Conference Changed to the Big East Conference Changed to the Conference USA Changed to the Sun Belt Conference Changed to the Mountain West Conference Changed to the Western Athletic Conference Became an Independent team Notes: The SEC, Big Ten, Big Twelve, PAC Ten, and Mid-American Conference had no new team admitted between 2004 and 2005. 19
Table 3. Summary Statistics of Variables Standard Variable Source of Data N Mean Deviation Min Max ATT NCAA Atten. Report 1909 46143.68 26728.73 3151 111609 LogATT See 4 below 1909 10.54 0.678 8.056 11.585 STDSIZE NCAA Records 1909 5532.08 22370.85 16000 107501 LogSTDSIZE See 4 below 1909 10.8352 0.4231 9.6803 11.5852 HTBOWL NCAA Records 1909 15.647 12.79 0 53 LRWINPCT NCAA Records 1909 0.5209 0.1882 0.0833 0.9487 LASTSEASON NCAA.org 1909 0.5209 0.2209 0 1 STRENGHT NCAA.org 1909 0.5127 0.0754 0 0.6885 OPPWINPCT NCAA Records 1909 0.4991 0.2209 0 1 OPPLRWINPCT NCAA Records 1909 0.5003 0.1883 0.0833 0.9487 NIGHT NCAA.org 1909 0.298 0.4575 0 1 SATURDAY NCAA.org 1909 0.8958 0.3057 0 1 2004 NCAA.org 1909 0.3243 0.4682 0 1 2005 NCAA.org 1909 0.3274 0.4694 0 1 2006 NCAA.org 1909 0.3484 0.4766 0 1 Note: 1. NCAA Atten. Report refers to the Single Game Team Report Div 1-A: Attendance provided by the NCAA. NCAA.org/stats 2. NCAA Records refers to the Official 2005 NCAA Division I-A and I-AA Football Records Book and the Official 2006 NCAA Division I-A and I-AA Football Records Book. www.ncaa.org/library/rules/2004/2004_football_rules.pdf www.ncaa.org/library/records/football/football_records_book/2005/2005_d1_football_records.pdf www2.ncaa.org/portal/media_and_events/ncaa_publications/records_books/fall/football/index.html 3. NCAA.org refers to the NCAA s official statistical website found at: http://web1.ncaa.org/stats/statssrv/rankings?dowhat=archive&sportcode=mfb 4. The log of ATTENDANCE and STDSIZE were created in the statistical program. This was done in order to observe a percent change influence. 20
Table 4. Regression Analysis Estimates of OLS with Conference Dummies; Dependant Variable LogATT, (SEC Base conference, 2006 Base Year) Independent Predicted Parameter Relative effect Variable Sign Estimate (T. stat.) of Dummy Variable 9 Intercept 4.0672 (11.89)*** LogSTDSIZE + 0.5457 (16.76)*** HTBOWL + 0.00839 (8.28)*** LRWINCPT + 0.31644 (4.40)*** LASTSEASON + 0.31177 (5.48)*** STRENGTH + 0.55665 (4.32)*** OPPPREVSEAS + 0.02032 (0.36) OPPLRWINPCT + 0.14446 (2.19)*** SATURDAY (+/-) -0.02384 (-0.92) NIGHT (+/-) 0.04017 (2.27)*** 4.0% 2004 (+/-) 0.00045 (0.03) 2005 (+/-) -0.03691 (-2.12)*** -3.6% SUNBLT (+/-) -0.54948 (-11.89)*** -42.3% ACC (+/-) -0.12953 (-4.11)*** -12.1% BIGEAST (+/-) -0.21908 (-5.72)*** -19.6% BIGTEN (+/-) 0.0192 (0.60) MAC (+/-) -0.67377 (-16.34)*** -49% PACTEN (+/-) -0.08209 (-2.51)*** -7.8% WAC (+/-) -0.54258 (-13.78)*** -41.9% MTWEST (+/-) -0.28210 (-7.59)*** -24.6% CUSA (+/-) -0.51242 (-14.50)*** -40% BIGTWLV (+/-) -0.07768 (-2.50)*** -7.5% INDY (+/-) -0.16135 (-3.20)*** -14.8% Summary Statistics N 1909 Adjusted R Square 0.7879 Root MSE 0.31233 F Statistic 323.14 Note: The individual coefficient is statistically significant at the *90% level or **95% level or ***99% level. 9 Relative effect of dummy variable on logatt. According to Halvorsen and Palmquist (1980) when dealing with a Semi-logarithmic Equations the relative effect of a dummy variable must be calculated from its coefficient by taking the exponent of the dummy coefficient (c) and subtracting 1: {exp(c)-1}. 21
Table 5. Regression Analysis Estimates (OLS) with CHG_TO_CONF variable; Dependant Variable LogATT (2006 base year) Independent Predicted Parameter Relative effect Variable Sign Estimate (T. stat.) of Dummy Variable Intercept 0.59513 (1.94)** LogSTDSIZE + 0.81649 (26.60)*** HTBOWL + 0.01180 (11.18)*** LRWINCPT + 0.07801 (0.99) LASTSEASON + 0.40152 (6.47)*** STRENGTH + 1.08053 (7.89)*** OPPPREVSEAS + -0.01230 (-0.20) OPPLRWINPCT + 0.25866 (3.59)*** SATURDAY (+/-) -0.02290 (0.81) NIGHT (+/-) -0.01967 (-1.05) 2004 (+/-) -0.02774 (-1.42) 2005 (+/-) -0.02099 (-1.10) c_sunblt (+/-) 0.11460 (1.49) c_acc (+/-) -0.13378 (-2.62)*** -12% c_bigeast (+/-) 0.16019 (3.07)*** 17.4% c_wac (+/-) -0.20016 (3.01)*** -18.1% c_mtwest (+/-) -0.15370 (-1.40) c_cusa (+/-) -0.22031 (-4.36)*** -19.7% c_indy (+/-) -0.36000 (-4.72)*** -30.6% Summary Statistics N` 1909 Adjusted R Square 0.7459 Root MSE 0.34184 F Statistic 312.17 Note: The individual coefficient is statistically significant at the *90% level or **95% level or ***99% level. 22
Table 6. Regression Analysis Estimates (Fixed Effect method) with CHG_TO_CONF variable; Dependant Variable LogATT (2006 base year) Independent Predicted Parameter Relative effect Variable Sign Estimate (T. stat.) of Dummy Variable Intercept 11.9791 (6.31)*** LogSTDSIZE + -0.20315 (-1.12) HTBOWL + -0.01067 (-1.22) LRWINCPT + -0.14436 (-1.42) LASTSEASON + 0.10127 (2.20)*** STRENGTH + 0.06179 (0.41) OPPPREVSEAS + 0.02614 (0.66) OPPLRWINPCT + 0.14027 (3.01)*** SATURDAY (+/-) -0.00931 (-0.50) NIGHT (+/-) 0.04703 (3.53)*** 4.6% FOUR (+/-) -0.02934 (-1.99)** -2.9% FIVE (+/-) -0.03947 (-3.06)*** -3.8% c_sunblt (+/-) -0.12501 (-0.98) c_acc (+/-) -0.12871 (-1.01) c_bigeast (+/-) 0.07521 (1.08) c_wac (+/-) -0.30926 (-4.09)*** -26.6% c_mtwest (+/-) 0.08937 (0.79) c_cusa (+/-) 0.07734 (1.55) c_indy (+/-) -0.12701 (-1.46) Summary Statistics N 1909 Adjusted R Square 0.8983 Root MSE 0.21631 F Statistic 124.86 Note: The individual coefficient is statistically significant at the *90% level or **95% level or ***99% level. 23