1. 11/11/20151 The Demand for Baseball Tickets 2005 Frank Francis Brendan Kach Joseph Winthrop

2. 11/11/20152 Overview Objectives Hypothesis/Variables Examined Software Approach Model Variable Statistics Results Policy Implications

3. 11/11/20153 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. 11/11/20154 Hypotheses H o : The demand for baseball tickets is explained by average ticket price H 1 : The demand for baseball tickets is explained by the cost of parking H 2 : The demand for baseball tickets is explained by stadium seating capacity H 3 : The demand for baseball tickets is explained by winning percentage

5. 11/11/20155 Variables Examined Price Variables Average ticket price Parking price Beer price FCI: Fan Cost Index Demand Variable: Demand for baseball tickets 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. 11/11/20156 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 Software

7. 11/11/20157 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. 11/11/20158 Variable Identification and Definition VariableTYPEHypothesized Sign Home Game Average AttendanceENDDependent Average Ticket PriceENDNegative Home Game Occupancy PercentageENDPositive Home Game Seating CapacityEXGPositive Team PayrollEXGPositive Operating IncomeENDPositive

9. 11/11/20159 Cross Sectional Linear Additive Demand Model Q X = -27408.907 - 149.537(P X ) + 446.93(h X ) + 0.638(s X ) + 0.00003( t X ) + 0.00002( o X ) Q X =Demand for Baseball Tickets P X = Average Ticket Price h X = Home Game Occupancy Percentage s X = Stadium Seating Capacity t X = Total Team Payroll o X = Operating Income

10. 11/11/201510 Predictive Ability of Model

11. 11/11/201511 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. 11/11/201512 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 MSE731.018 SSQ(Res)12825310.950 Dep.Mean30590.833 Coef of Var (CV)2.390 R-Squared 99.422% Adj R-Squared 99.301%

13. 11/11/201513 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. 11/11/201514 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. 11/11/201515 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. 11/11/201516 Constant Variance

17. 11/11/201517 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. 11/11/201518 Normality of Error Terms

19. 11/11/201519 Elasticities Variable Average Elasticities 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 Elasticity represents a percentage change in the dependent variable given a percentage change in the independent variable.

20. 11/11/201520 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. 11/11/201521 Conclusion We accept H o, that states the demand for baseball tickets is explained by average ticket price, because it is significant at the 99% confidence level. We reject H 1, because price of parking is not significant at the 90% confidence level.

22. 11/11/201522 Conclusion We accept H 2 because stadium seating capacity is significant at the 99% confidence level We reject H 3 because the winning percentage of the team is not significant at the 90% confidence level