the impact of star power on gate revenues in nba and mlb

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

Intro Exer Sci c0-intro 2

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.


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

Intro Exer Sci c0-intro 4Competitive 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


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


Objective

Comprehensive study of relationship between team attendance and both team performance and star players Using empirical model


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


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


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


Difference between 2 models

STARVOT Authors think still significant

DCAP, OLD, DEXP5, POP


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


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


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


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.


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


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


主隊 Ace

係數a

.839 .007 120.115 .000

.122 .013 .142 9.535 .0005.002E-03 .008 .009 .614 .540

.103 .008 .199 13.435 .000

.189 .008 .362 24.591 .0002.073E-02 .009 .032 2.266 .023

( )常數跨聯盟DN日期編號WEEKENDSP1

模式1

B 之估計值標準誤未標準化係數

Beta 分配

標準化係數

t 顯著性

\依變數：人數比a.

R2=0.194


客隊 Ace

係數a

.840 .007 121.365 .000

.122 .013 .142 9.553 .0004.996E-03 .008 .009 .613 .5402.066E-02 .010 .029 2.072 .038

.103 .008 .198 13.420 .000

.189 .008 .361 24.576 .000

( )常數跨聯盟DNVSP1日期編號WEEKEND

模式1


Beta 分配

標準化係數

t 顯著性

\依變數：人數比a.

R2=0.194


Conclusion

the best starting pitchers of home and visiting teams would attract more fans to MLB games


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


Results -- CPBL係數a

-2460.125 1823.821 -1.349 .192

4924.357 4123.117 .313 1.194 .246

4559.963 2248.073 .367 2.028 .056

196.570 96.916 .330 2.028 .056

-263.497 510.469 -.109 -.516 .611

-204.792 456.547 -.085 -.449 .659

62.313 551.221 .020 .113 .911

-747.215 589.908 -.243 -1.267 .220

-537.542 610.475 -.175 -.881 .389

578.815 589.153 .188 .982 .338

684.710 615.531 .222 1.112 .279

416.821 827.621 .115 .504 .620

( )常數今年勝率去年勝率明星賽先發今年季後賽去年季後賽今年冠軍year1

year2

year3

year4

year5

模式1


Beta 分配

標準化係數

t 顯著性

依變數：主場平均觀眾數a.

R2=0.501


Results – model 4 dependent variable: total attendance of all games in the

season係數a

-1755.485 1615.154 -1.087 .290

3541.935 3651.384 .241 .970 .344

4563.628 1990.867 .392 2.292 .033

182.915 85.828 .329 2.131 .046

-264.702 452.065 -.117 -.586 .565

-287.768 404.313 -.127 -.712 .485

343.187 488.155 .119 .703 .490

-722.207 522.415 -.251 -1.382 .182

-488.281 540.629 -.170 -.903 .377

591.413 521.747 .205 1.134 .270

705.893 545.107 .245 1.295 .210

485.459 732.931 .143 .662 .515

( )常數今年勝率去年勝率明星賽先發今年季後賽去年季後賽今年冠軍year1

year2

year3

year4

year5

模式1


Beta 分配

標準化係數

t 顯著性

依變數：平均觀眾數a.

R2=0.552


Conclusion: CPBL

More starters in all-star games would attract more overall attendance


Data sources: Retrosheet


Data sources: Lahman database

Yearly stats of each player/team http://seanlahman.com/


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


Data sources: pitch FX Pitch-by-pitch

Speed, location, movement, results http://www.brooksbaseball.net/


pitch FX

the impact of star power on gate revenues in nba and mlb

Documents

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promotion of team performance

impact of star power

probability of home

star power of hill

revenue sharing

capacity dcap