ADMN 580 Case: Forecasting Ticket Revenue for Orlando Magic Original Case from Operations Management: Sustainability and Supply Chain Management by Heizer, Render, and Munson 2020 Pearson Education, Inc. Additional Content by Russell A. Miles, Peter Zaimes, Alison Chen, and Alice Sheehan (UNH Paul College of Business) NOTE: Please be sure to watch the associated case video embedded in the Pearson e-text (Chapter 4). TEXTBOOK CASE Note: Use this case description, not the one in the e-text. Some of the data (specifically, Table 4.3) is different here from what is in the e-text. For its first two decades of existence, the NBAs Orlando Magic basketball team set seat prices for its 41game home schedule the same for each game. If a lower-deck seat sold for $150, that was the price charged, regardless of the opponent, day of the week, or time of the season. If an upper-deck seat sold for $10 in the first game of the year, it likewise sold for $10 for every game. But when Anthony Perez, director of business strategy, finished his MBA at the University of Florida, he developed a valuable database of ticket sales. Analysis of the data led him to build a forecasting model he hoped would increase ticket revenue. Perez hypothesized that selling a ticket for similar seats should differ based on demand. Studying individual sales of Magic tickets on the open Stub Hub marketplace during the prior season, Perez determined the additional potential sales revenue the Magic could have made had they charged prices the fans had proven they were willing to pay on Stub Hub. This became his dependent variable, y, in a multiple regression model. He also found that three variables would help him build the true market seat price for every game. With his model, it was possible that the same seat in the arena would have as many as seven different prices created at season onsetsometimes higher than expected on average and sometimes lower. The major factors he found to be statistically significant in determining how high the demand for a game ticket, and hence, its price, would be were: The day of the week (x1) A rating of how popular the opponent was (x2) The time of the year (x3) For the day of the week, Perez found that Mondays were the least-favored game days (and he assigned them a value of 1). The rest of the weekdays increased in popularity, up to a Saturday game, which he rated a 6. Sundays and Fridays received 5 ratings and holidays a 3 (refer to the footnote in Table 4.3). NOTE: For this case analysis use the data provided in the following Updated Table 4.3 rather than the information provided in textbook Table 4.3.

ADMN 580 Case: Forecasting Ticket Revenue for Orlando Magic Original Case from Operations Management: Sustainability and Supply Chain Management by Heizer, Render, and Munson 2020 Pearson Education, Inc. Additional Content by Russell A. Miles, Peter Zaimes, Alison Chen, and Alice Sheehan (UNH Paul College of Business) Updated Table 4.3 Data for Last Years Magic Ticket Sales Pricing Model * Day of week rated as 1 = Monday, 2 = Tuesday, 3 = Wednesday, 4= Thursday, 5 = Friday, 6 = Saturday, 5 = Sunday His ratings of opponents, done just before the start of the season, were subjective and range from a low of 0 to a high of 8. A very high-rated team in that season may have had one or more superstars on its roster, or have won the NBA finals the prior season, making it a popular fan draw. Finally, Perez believed that the NBA season could be divided into four periods in popularity: Early games (which he assigned 0 scores) Games during the Christmas season (assigned a 3) Games until the All-Star break (given a 2) Games leading into the playoffs (scored with a 3) The first year Perez built his multiple regression model, the dependent variable y, which was a potential premium revenue score, yielded an R2=.86 with this equation: Table 4.3 illustrates, for brevity in this case study, a sample of 12 games that year (out of the total 41 home game regular season), including the potential extra revenue per game (Y) to be expected using the variable pricing model. A leader in NBA variable pricing, the Orlando Magic have learned that regression analysis is indeed a profitable forecasting tool. ADDITIONAL INFO (FROM YOUR INSTRUCTOR) Your team is a consulting group that specializes in forecasting methods for large capacity events to maximize revenue and create pricing strategies to support the long-run goals of the organization. You are competing with another consulting firm to win the Orlando Magics business. As part of the selection process, Anthony Perez has asked you to assess the current forecasting model Orlando Magic is using to estimate additional sales potential per game and to recommend associated ticket pricing strategies.

ADMN 580 Case: Forecasting Ticket Revenue for Orlando Magic Original Case from Operations Management: Sustainability and Supply Chain Management by Heizer, Render, and Munson 2020 Pearson Education, Inc. Additional Content by Russell A. Miles, Peter Zaimes, Alison Chen, and Alice Sheehan (UNH Paul College of Business) In addition to the game data provided in the Updated Table 4.3, the data analytics team at your consulting firm provided you with estimates for additional characteristics of the opposing teams summarized in the following Supplemental Table: Supplemental Table: Characteristics of Opponents PLAYOFF PROJECTS are scored from 0 = almost certain to not make playoffs to 2 = almost certain to make playoffs Your client contact (Perez) has asked you to present both your assessment of Orlando Magics current forecasting model and challenged you to develop and present a more robust model using your supplemental research. Your presentation will give you the opportunity to demonstrate your teams understanding of the clients issues, your ability to conduct a logical quantitative analysis, and your strategic approach to problem solving. Perez has asked your team to include the following in your presentation (at a minimum). 1. Clearly and concisely explain what is being forecasted and why it is important to the organization. Remember, not all of the managers in the room will be directly involved in the forecasting process. 2. Use multiple variable regression to develop a forecasting model based on the data associated with the twelve games listed in Updated Table 4.3. Call this the Baseline Model. Use it to forecast the additional sales potential for each game. Discuss the performance of this baseline model by finding the coefficient of determination (R2) and calculating the Mean Absolute Percent Error (MAPE). 3. Analyze the data provided in the Supplemental Table. Create four individual scatter plots and evaluate each of the individual supplemental variables ability to forecast additional sales potential. Which two supplemental variables have the best R2? 4. Choose the two best supplemental variables and create a Revised Model (with five total independent variables). Compare the R2 and MAPE of your revised model to Perezs baseline model. Highlight the differences between the models. 5. With the forecasting analysis done your team can now get creative. Show your client how to use the forecasting models to optimize revenue from ticket sales. Develop and demonstrate a quantitative approach to adjusting ticket prices based on the forecasted potential revenue of a given game. Please provide a specific solution such as a flexible pricing model (not just general advice). TEAMDATE SUPER STAR PLAYERS DISTANCE BETWEEN TEAMS PLAYOFF PROJECTIONS WIN/LOSS PREVIOUS GAME AGAINST OPPONENT Boston Celtics22-Oct71,2842W Golden State Warriors3-Nov82,8061L Minnesota Timberwolves16-Nov31,5680L Atlanta Hawks14-Dec24381W Los Angeles Lakers27-Dec52,5050W Memphis Grizzlies5-Jan68222L New York Knicks7-Feb41,0722W Portland Trail Blazers5-Mar23,0361L Utah Jazz9-Mar42,5401W Brooklyn Nets26-Mar79422W Detroit Pistons2-Apr11,1600L Cleveland Cavaliers4-Apr41,0372WADMN 580 Case: Forecasting Ticket Revenue for Orlando Magic Original Case from Operations Management: Sustainability and Supply Chain Management by Heizer, Render, and Munson 2020 Pearson Education, Inc. Additional Content by Russell A. Miles, Peter Zaimes, Alison Chen, and Alice Sheehan (UNH Paul College of Business) 6. Finally, get even more creative and recommend three additional initiatives (beyond optimizing ticket prices) that your client can implement to further maximize game day revenue. These strategic initiatives can include actions related to sales, marketing, promotions, merchandising, concessions, or any other functional area within the organization. Help the client make more money per game!