The University Of Wyomying Men’s Basketball Team Case Study Solution
The men’s basketball team at the University of Wyoming was a major source of revenue for the athletic department at the university as significant revenues were earned through ticket and concession sales. The University’s men’s basketball team competed in Mountain West Conference (MWC) twice a year and the revenue was generated by hosting tournaments and additional games against teams. Bill Spark, the senior director of the UW’s athletic department was responsible for forecasting the revenues as well as establishing the ticket prices.
The director believed that the forecasted ticket sales would depend on various factors including, the day of the week of the game, rival i.e. conference or non-conference team and performance of that team in that particular year. Therefore, the case report involves forecasting revenue for the upcoming basketball season using the multiple linear regression model and simulation model in order to assist the director in the revenue forecast. (Sorochuk, 2011)
Bill Spark, the director of the athletic department experienced difficulties in forecasting future Revenues for the upcoming basketball season due to uncertainty in the number of home games, revenue from single-game tickets and concession sales. Moreover, it was challenging for the director of the athletic department to project the revenue, due to co-relation of revenue with other dependent factors, such as: day of the week of the game, type of opponent and team’s performance.
The ticket and concession sales are dependent on various factors, which are specified below:
Day of the week of the Game (Weekday or Weekend):
The projected revenue is dependent on the day of the week of the game as people prefer to watch sports or their favorite matcheson the weekend as compared to weekdays due to the busy schedules and work commitments. This factor plays an integral role in the demand and sale of the tickets.Thereforeforecasting revenue will be difficult due to the mentioned factor.
The basketball team plays games and matches in Mountain West Conferences and other games can also be hosted by the Men’s basketball team against non-conference teams. Moreover, past trend i.e. the information provided in Exhibit 2 of the case study suggests that the demand for tickets in matches against conference team is higher than the demand for tickets in matches againstthe non-conference team. Therefore, the type of opponent team appears to be a vital factor in estimating the demandfor the tickets and concession sales.
Team’s Performance/ Winning ratio:
The revenue projection and demand of the tickets depend on the team performance as better performance of the team will motivate its followers or fans to purchase tickets for future matches as well in order to support the team.Therefore, it results in an increased demand for the tickets.
Multiple Linear Regression
In order to predict the ticket sales and concession sales, the multiple linear regression model has been used. The linear regression model describes the relationship between the dependent (response) variable and the independent (explanatory) variable. (KENTON, 2019)
Predicting Ticket Sales:
The multiple linear regression model is used to predict ticket sales and identify the relationship between the team’s performances i.e. winning percentage, day of the game and opponent. The coefficient of determination of the ticket sales is 0.26, which indicates that the value 26% fitted to the regression line. (See Exhibit 1)
Moreover, the multiple R-value is 0.52, which suggests that there is a medium relationship between the ticket revenue and the three variables. The win percentage variable has a higher coefficient which suggests that a 1% change in winning percentage will increase the ticket sales revenue by $21823. The intercept of the ticket sales indicates that the ticket sales revenue will be $2332 in case other variables i.e. the winning percentage, the opponent and the day of the game is zero.Moreover, the significance level is lower than the p-value of 0.55,indicating that there are strong statistical significant evidences to reject the null hypothesis.(Annexure, Excel)…………………………
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