Question No. 03

The variables that has taken includes tournament, days and winning percentage. The simulation model would help in determining the revenues for the whole 16 games played in the single session. The management would be ease in forecasting the revenues and to predict the multiple replications in easier manner.

The winning percentage is selected as the foremost variable for the simulation model. In the model, the unit cost, fixed cost has also incorporated. Not only this, the average of the winning ratio is taken, and the average of the single game ticket for prior year is also calculated i.e. 1,060.

The simulation model is applied by taking average of winning percentage for 4 seasons. The price of ticket is $12. The random numbers are taken for 16 game season and per game revenue is calculated by taking price of ticket and number of ticket sold. Hence, the revenue generated from the 16 game season is $204729.312.

Question No. 04

It is obvious to use the 50 percent wining ratio for calculating revenues over 100 simulation seasons. It can be seen through analyzing revenues on the basis of the 100 seasons that the revenue of the team are depending upon the performance of the team.

Team Win average 0.5
Eight conference games 947.125
Eight non-conference games 1173.625

Table 1: Simulation model for 100 seasons

It can be anticipated that the team would performexceptionally well throughout season, due to which the demand of the tickets would be increasing, hence generating revenue for the athletics department or university by increasing the price of ticket.

The previous data 16 game season data is split into two categories i.e. conference and non-conference. The simulation formula is used to calculate the number of tickets sold for the conference and non-conferenceand then it is multiplied with the price of ticket to get the revenue. The average revenue of the 100simulated seasons is $204340.8.

The data on the simulation for 100 season can be seen in excel file.

Question No. 05

The revenue generation process has been determined by using simulation model for different seasons. In 2006-2007, the winning percentages of the team was 0.619, for 2007-2008 it was 0.4, for 2008-2009 it was 0.643 and between 2009 and 2010, the winning percentage was 0.323.

In addition to this, the variables have also used in simulation model to identify that in case of increasing the winning percentage, there is a likelihood that the revenue would also be increasing. Also, in case of decreasing winning percentage, the total revenue would be adversely affected. Likely to previous analysis, the revenue is calculated by applying simulation model, the revenue for eight conference and non –conference games are calculated by multiplying price $12 with the 16 games season and number of ticket sold, the revenue is $100893.343.

 

Exhibit A

Ticket sales

ANOVA          
  df SS MS F Significance F
Regression 1 17835966.64 17835966.64 47.66678167 2.56141E-09
Residual 65 24321714.01 374180.2156    
Total 66 42157680.66      

 

  Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 405.3868933 164.807988 2.459752699 0.016572126 76.24261291 734.5311737 76.24261291 734.5311737
X Variable 1 0.268612382 0.038906137 6.904113387 2.56141E-09 0.190911461 0.346313303 0.190911461 0.346313303

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