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

2. Intro Exer Sci c0-intro2 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-intro3 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-intro4 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-intro5 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-intro6 Objective Comprehensive study of relationship between team attendance and both team performance and star players Using empirical model

7. Intro Exer Sci c0-intro7 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. 9 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-intro12 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-intro13 Difference between 2 models STARVOT Authors think still significant DCAP, OLD, DEXP5, POP

14. Intro Exer Sci c0-intro14 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-intro15 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-intro17 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-intro18 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-intro19 Data and variables data was obtained from Retrosheet (http://www.retrosheet.org)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-intro20 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-intro21 主隊 Ace R2=0.194

22. Intro Exer Sci c0-intro22 客隊 Ace R2=0.194

23. Intro Exer Sci c0-intro23 Conclusion the best starting pitchers of home and visiting teams would attract more fans to MLB games

24. Intro Exer Sci c0-intro24 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-intro25 Results -- CPBL R2=0.501

26. Intro Exer Sci c0-intro26 Results – model 4 dependent variable: total attendance of all games in the season R2=0.552

27. Intro Exer Sci c0-intro27 Conclusion: CPBL More starters in all-star games would attract more overall attendance

28. Intro Exer Sci c0-intro28 Data sources: Retrosheet

29. Intro Exer Sci c0-intro29 Data sources: Lahman database Yearly stats of each player/team http://seanlahman.com/

30. Intro Exer Sci c0-intro30 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/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/2010/30/football-valuations- 10_NFL-Team-Valuations_Rank.html http://www.forbes.com/lists/2011/32/basketball- valuations-11_land.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-intro31 Data sources: pitch FX Pitch-by-pitch Speed, location, movement, results http://www.brooksbaseball.net/

32. Intro Exer Sci c0-intro32 pitch FX

33. Intro Exer Sci c0-intro33 pitch FX

34. Intro Exer Sci c0-intro34 pitch FX

35. Intro Exer Sci c0-intro35 pitch FX