The Demand for Baseball Tickets 2005

1. The Demand for Baseball Tickets 2005 Frank Francis Brendan Kach Joseph Winthrop

2. Overview • Objectives • Hypothesis/Variables Examined • Software • Approach • Model • Variable • Statistics • Results • Policy Implications

3. Objectives • To develop an econometric model that explains what factors drove the demand for baseball tickets in 2005 • To provide forecasters with a working model that can be used to make predictions about the future demand for baseball tickets • To provide baseball management with a solid foundation upon which to make policy decisions based on objective reasoning

4. Hypotheses • Ho: The demand for baseball tickets is explained by average ticket price • H1: The demand for baseball tickets is explained by the cost of parking • H2: The demand for baseball tickets is explained by stadium seating capacity • H3: The demand for baseball tickets is explained by winning percentage

5. Variables Examined Demand Variable: Demand for baseball tickets Price Variables Average ticket price Parking price Beer price FCI: Fan Cost Index Performance Variables 2005 WINS 2005 Losses 2005 Win / Loss Percentage 2005 Runs Scored 2005 Runs Allowed 2005 Homeruns Other Variables Home Game Average Attendance Road Game Average Attendance Home Game Occupancy Percentage Stadium Seating Capacity Population Census Data Team Economic Variables Total Revenue Operating Income Total Payroll Expense Current Worth

6. Software • WinORS was used to formulate the model • Ease of Use • Ability to handle large data sets • Ability to change model and recalculate results in a timely fashion

7. Approach • Baseball team cross sectional data set from 2005 • Developed an industry demand model • Stepwise regression was used to determine the most significant variables • Ordinary Least Squares regression was used to test variables for Multicollinearity, homoscedasticity, serial correlation, and normality

8. Variable Identification and Definition VariableTYPEHypothesized Sign Home Game Average Attendance END Dependent Average Ticket Price END Negative Home Game Occupancy Percentage END Positive Home Game Seating Capacity EXG Positive Team Payroll EXG Positive Operating Income END Positive

9. Cross Sectional Linear Additive Demand Model QX = -27408.907 - 149.537(PX ) + 446.93(hX) + 0.638(sX) + 0.00003(tX) + 0.00002(oX) QX =Demand for Baseball Tickets PX =Average Ticket Price hX =Home Game Occupancy Percentage sX =Stadium Seating Capacity tX =Total Team Payroll oX =Operating Income

10. Predictive Ability of Model

11. Overall Significance • The P-value (0.00001) is well below 0.05 • This shows that the model is statistically significant at better than the 99% confidence level. F-Value 825.032 P-Value 0.00001

12. Coefficient of Determination • Demonstrates that a high degree of variability in ticket sales that can be explained by variation in the independent variables Association Test Root MSE 731.018 SSQ(Res) 12825310.950 Dep.Mean 30590.833 Coef of Var (CV) 2.390 R-Squared 99.422% Adj R-Squared 99.301%

13. Multicollinearity • The first of four regression assumptions is the absence of collinearity or that independent variables must be independent from other independent variables. • The test for multicollinearity is determined by the value for variance inflation factor (VIF) with a value below 10 indicating an absence of collinearity. AVERAGE VIF= 1.998

14. Parameter VIFs VariableVariance Inflation Factor Average Ticket Price 2.291 Home Game Occupancy Percentage 2.546 Home Game Seating Capacity 1.450 Team Payroll 2.532 Operating Income 1.172

15. Constant Variance • The second of four regression assumptions is the expectation of constant variance across the residual terms. • The White’s test is used to test the null hypothesis and determine if the residual error terms are homoskedastic. White's Test for Homoscedasticity ====> 25.800 P-Value for White's ====> 0.17254

16. Constant Variance

17. Auto Correlation • The third of four regression assumptions is the absence of serial (auto) correlation • The Durbin-Watson statistic is used to test for the existence of positive and negative serial correlation with time series data. • Constant Variance plot provides an indication of positive or negative serial correlation.

18. Normality of Error Terms

19. Elasticities • Elasticity represents a percentage change in the dependent variable given a percentage change in the independent variable. VariableAverageElasticities Average Ticket Price - 0.10778 Home Game Occupancy Percentage 1.02480 Home Game Seating Capacity 0.99879 Team Payroll 0.06168 Operating Income 0.00523

20. Price Elasticity Implications • Existing demand for baseball tickets is price inelastic • A 10% increase in the average price of tickets will on lead to a 1% decrease in demand • Baseball teams can raise prices and it will lead to an overall increase in revenue.

21. Conclusion • We accept Ho, that states the demand for baseball tickets is explained by average ticket price, because it is significant at the 99% confidence level. • We reject H1, because price of parking is not significant at the 90% confidence level.

22. Conclusion • We accept H2 because stadium seating capacity is significant at the 99% confidence level • We reject H3 because the winning percentage of the team is not significant at the 90% confidence level