The impact of star power on gate revenues in NBA and MLB

1. The impact of star power on gate revenues in NBA and MLB

2. Intro Exer Sci c0-intro Original ideas • Stars at the gate: The impact of star power on NBA gate revenues. Berri DJ, Schmidt MB, Brook SL. Journal of Sports Economics, 5: 33-50, 2004.

3. Intro Exer Sci c0-intro Competitive balance • On-field domination of one or small number of organizations • May reduce level of uncertainty of outcome • Reduce level of consumer demand • Relationship between uncertainty of outcome or competitive balance and demand for tickets to sporting events • Game day attendance or aggregate season attendance • Sport leagues have used various ways to promote competitive balance • Reserve clause, draft, payroll cap, revenue sharing, luxury tax

4. Intro Exer Sci c0-intro Competitive imbalance in professional sports in US • NBA relative lack of competitive balance in professional sport leagues in US • Despite draft, payroll cap, revenue sharing, free agency • MLB attendance was maximized when probability of home team winning was about 0.6 (Knowles 1992; Rascher 1999) • Consumers prefer to see home team win but not wish to be completely certain with the outcome

5. Intro Exer Sci c0-intro Teams at bottom of ranking • How do these team maintain demand with the certainty of an unwelcomed outcome • Shift focus from promotion of team performance to promotion of individual stars • Presence of stars had substantial effect on TV rating (Hausman 1997) • Even after controlling for team quality

6. Intro Exer Sci c0-intro Objective • Comprehensive study of relationship between team attendance and both team performance and star players • Using empirical model

7. Intro Exer Sci c0-intro Data • From 1992-93 to 1995-96, 4 seasons • Dependent variable: consumer demand: gate revenue reported in Financial World • Better than attendance because 43 of 108 (40%) teams sold out every home game • Independent variables • Team performance, franchise characteristics, market characteristics, racial variables

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9. Intro Exer Sci c0-intro Independent variables • Team performance • WCHM20 • Star power: various definition, use ‘all-star votes received’ of all players in the team • Superstar variables for MJ, Shaq, G. Hill, Barkley • Franchise characteristics • Stadium capacity, expansion team expect to have positive effect on attendance and revenue • Teams at capacity (DCAP) =1, stadiums with excess capacity can increase both quantity and price, stadiums with full capacity can only increase price • Roster stability: minutes played by returning player over both current and prior seasons

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12. Intro Exer Sci c0-intro Results • Variables on team performance significant • Stadium capacity positively significant • DHILL negative • Piston’s failure on winning led to decline at the gate that star power of Hill could not overcome • None other superstars was significant • Individual player do not have significant impact on revenue beyond contribution to team wins • Level of competitive balance in conference not significant • Different from MLB

13. Intro Exer Sci c0-intro Difference between 2 models • STARVOT • Authors think still significant • DCAP, OLD, DEXP5, POP

14. Intro Exer Sci c0-intro Affect of wins and star attractions • Use double-logged model • GATE responsive to changes in stadium capacity and wins • Relative effect of wins and star power revealed in marginal values (Table 4) • Players on the team need to receive 370,000 votes to generate the revenue a team receives from one win • More than votes received by entire team • It is performance on the court, not star power, that attracts fans in NBA

15. Intro Exer Sci c0-intro Affect of market size • Increase in population will increase gate revenue • Moving to a city with an additional million persons worth 399,503 • Such increase in revenue would increase the value of a win by 1648 • Additional persons in population enhance the monetary value of on-court performance

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17. Intro Exer Sci c0-intro Conclusion • Although star power was significant, ability of a team to generate wins appears to be the engine that drives consumer demand • The true power of star power may lie in the revenue received by the star’s opponent • Enhance attendance on the road

18. Intro Exer Sci c0-intro Ace effect in MLB • Starting pitchers the most crucial player in determining the outcome of baseball games • Effect of ace starting pitchers, the best starting pitcher of the team, on attendance in Major League Baseball during 2006 and 2007 • Ace: the best starting pitcher of each team, identified according to win-loss record and ERA in the season. • Teams without the ace starting pitcher, due to either lack of good starting pitcher or having more than 1 good starting pitchers, were excluded from this study.

19. Intro Exer Sci c0-intro Data and variables • data was obtained from Retrosheet (http://www.retrosheet.org) • 4114 games • dependent variable: the ratio of attendance of the specific game to the team’s average attendance per game

20. Intro Exer Sci c0-intro Data and variables • Ace of the home/visiting teams • dependent variables • dummy variables for the games started by ace pitchers of the home teams • interleague games • games played in weekend (Fri, Sat, Sun) • games played in the second half of the season (July, August, September, and October) • games played at night • ordinary least square regression

21. Intro Exer Sci c0-intro 主隊 Ace R2=0.194

22. Intro Exer Sci c0-intro 客隊 Ace R2=0.194

23. Intro Exer Sci c0-intro Conclusion • the best starting pitchers of home and visiting teams would attract more fans to MLB games

24. Intro Exer Sci c0-intro Data and variables – CPBL • CPBL 2002-2007, 32 team-seasons • dependent variable: total attendance of home games in the season • independent variables • star power: number of players started in the all-star game in the current season • winning percentages of the current and previous season • making playoff in the current and previous season • winning championship in the current and previous season • dummy variables for each year

25. Intro Exer Sci c0-intro Results -- CPBL R2=0.501

26. Intro Exer Sci c0-intro Results – model 4 • dependent variable: total attendance of all games in the season R2=0.552

27. Intro Exer Sci c0-intro Conclusion: CPBL • More starters in all-star games would attract more overall attendance

28. Intro Exer Sci c0-intro Data sources: Retrosheet

29. Intro Exer Sci c0-intro Data sources: Lahman database • Yearly stats of each player/team • http://seanlahman.com/

30. Intro Exer Sci c0-intro Other resources • Disable list database • Player yearly salary website • http://mlbcontracts.blogspot.com/ • Franchise values • US leagues • http://www.forbes.com/lists/2011/33/baseball-valuations-11_land.html • http://www.forbes.com/lists/2010/30/football-valuations-10_NFL-Team-Valuations_Rank.html • http://www.forbes.com/lists/2011/32/basketball-valuations-11_land.html • European football and US leagues • http://www.rodneyfort.com/SportsData/BizFrame.htm

31. Intro Exer Sci c0-intro Data sources: pitch FX • Pitch-by-pitch • Speed, location, movement, results • http://www.brooksbaseball.net/

32. Intro Exer Sci c0-intro pitch FX

33. Intro Exer Sci c0-intro pitch FX

34. Intro Exer Sci c0-intro pitch FX

35. Intro Exer Sci c0-intro pitch FX