ADMN 580 - CASE 01

Forecasting Ticket Revenue for Orlando Magic

NOTE: Please be sure to watch the associated case video embedded in the Pearson e-text.

TEXTBOOK CASE

For its first two decades of existence, the NBA’s Orlando Magic basketball team set seat prices
for its 41-game 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 onset—sometimes 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).

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, and Alice Sheehan (UNH Peter T Paul College of Business and Economics)
 ADMN 580 - CASE 01
Forecasting Ticket Revenue for Orlando Magic

Table 4.3 Data for Last Year’s Magic Ticket Sales Pricing Model

TEAM DATE* DAY OF TIME OF RATING OF ADDITIONAL

WEEK* YEAR OPPONENT SALES POTENTIAL
Phoenix Suns 4-Nov Wednesday 0 0 $12,331
Detroit Pistons 6-Nov Friday 0 1 $29,004
Cleveland Cavaliers 11-Nov Wednesday 0 6 $109,412
Miami Heat 25-Nov Wednesday 0 3 $75,783
Houston Rockets 23-Dec Wednesday 3 2 $42,557
Boston Celtics 28-Jan Thursday 1 4 $120,212
New Orleans Pelicans 3-Feb Monday 1 1 $20,459
L. A. Lakers 7-Mar Sunday 2 8 $231,020
San Antonio Spurs 17-Mar Wednesday 2 1 $28,455
Denver Nuggets 23-Mar Sunday 2 1 $110,561
NY Knicks 9-Apr Friday 3 0 $44,971
Philadelphia 76ers 14-Apr Wednesday 3 1 $30,257

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 particular 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.

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, and Alice Sheehan (UNH Peter T Paul College of Business and Economics)
 ADMN 580 - CASE 01
Forecasting Ticket Revenue for Orlando Magic

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 Magic’s
business. As part of the selection process, Anthony Perez has asked you to assess the pricing
scheme the Orlando Magic developed using their current multiple regression model, as well as
what pricing recommendations are suggested by the model.

In addition to the game data in Table 4.3, your data analytics team provided estimates for
additional characteristics of the opposing teams. For instance, rather than just using Perez’s
ratings of opponent, which considers super star players, your analysts have made this data
available directly.

Supplemental Table: Characteristics of Opponents

WIN/LOSS
SUPER DISTANCE PREVIOUS
CITY
TEAM DATE* STAR BETWEEN GAME
POPULATION
PLAYERS TEAMS AGAINST
OPPONENT
Phoenix Suns 4-Nov 0 2,138 1,626,000 W
Detroit Pistons 6-Nov 0 1,161 673,000 W
Cleveland Cavaliers 11-Nov 2 1,038 386,000 L
Miami Heat 25-Nov 1 236 463,000 W
Houston Rockets 23-Dec 0 964 2,313,000 L
Boston Celtics 28-Jan 1 1,295 685,000 L
New Orleans Pelicans 3-Feb 0 670 393,000 L
L. A. Lakers 7-Mar 2 2,510 4,000,000 L
San Antonio Spurs 17-Mar 0 1,159 1,493,000 L
Denver Nuggets 23-Mar 1 1,842 620,000 W
NY Knicks 9-Apr 1 1,081 8,623,000 W
Philadelphia 76ers 14-Apr 0 994 1,581,000 L

Use the information in Table 4.3 PLUS the supplemental team characteristic data above to
develop a more robust pricing model. Overpricing can be costly and result in lower than
expected sales or steeply discounted prices as the event date approaches. Conversely, if they
continue to underprice their tickets, the Orlando Magic will miss out on revenue that is currently
being collected by the third-party ticket distributer StubHub.

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, and Alice Sheehan (UNH Peter T Paul College of Business and Economics)
 ADMN 580 - CASE 01
Forecasting Ticket Revenue for Orlando Magic
As part of the selection process, Perez has asked you to assess and present your findings using
their current pricing model as well as another more robust model. This presentation will give
you the opportunity to demonstrate your team’s understanding of the client’s 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. Use multiple regression analysis to forecast the additional sales potential using Perez’s
current method and comment on the performance of the model. Specifically, what
actionable pricing recommendations are suggested by the model? Do you feel these
recommendations are appropriate? For instance, Perez found it was possible that the
same seat in the arena would have as many as seven different prices created at season
onset. Could you put the games into four “buckets” determining which ones should be
priced lower than average and which should be priced higher than average? Do your
buckets agree with the “buckets” that would be suggested by the previous year’s
StubHub prices?
Hint: Analyze games from lowest potential revenue to highest and put into four groups
(three games in each). Do the groups determined by Perez’s pricing model and the
actual StubHub data align?
2. Assess the viability of the four new “supplemental” variables to assist in determining the
“true market” seat price. Which variables do you think will be helpful? Which will not?
Hint: Use scatter plots and simple linear regression to evaluate each of the individual
variable’s ability to forecast ticket sales.

3. After you have assessed the viability of the new “supplemental”, choose the two most
relevant variables and create a revised pricing model. Is your “revised” model truly
more robust than the initial pricing model developed by Perez and his team? What
actionable pricing recommendations are suggested by the new model?
Hint: Again, use four pricing “buckets.” How do these “buckets” compare to those
identified by StubHub?
4. Finally, which pricing model would you suggest the Orlando Magic use to forecast ticket
sales, the PEREZ MODEL or your REVISED MODEL? Back up your recommendations with
quantitative logic. What are your team’s strategic pricing recommendations based on
your analysis?

Please feel free to check in with the CEO of your consulting firm (aka your instructor) if you have
any questions about how to engage with this client, conduct the required analysis, or prepare
your presentation. And, please be sure to read the appendix of this document for more
information on how to prepare for your presentation.

GOOD LUCK!

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, and Alice Sheehan (UNH Peter T Paul College of Business and Economics)
 ADMN 580 - CASE 01
Forecasting Ticket Revenue for Orlando Magic

APPENDIX
TEAM CASE COMPETITION:

Students will be placed on one of eight teams (of three to five members). Each team will be
assigned a case/company from the textbook to present and will act as consultants advising the
subject company on the issues presented in the case. We will cover four cases in this
course. For each case, two of the teams will compete by presenting their recommendations in
an attempt to win the client's business. Your instructor will score the teams' performance and
select the winning team.

NOTE: Your team should NOT simply answer the questions posed in the textbook – but present
a cohesive analysis of the issue at hand along with an operational strategy for the subject
company. The presentation should:

• Provide background on the business situation to baseline understanding with the client
• Summarize the current situation (issues, challenges, opportunities)
• Highlight associated client questions if applicable
• Highlight applicable industry best practices (do a bit of light supporting research for this)
• Summarize your analysis approach and results (briefly and effectively)
• Cover strategic initiatives to help the client with their operational situation
• Note risks to implementation (if any) and associated mitigation actions
• Note any key action items to drive successful implementation of the strategic initiatives
• Close strong with a clear call to action to your client (the final sales pitch)

After each consultant team presents, there will be a brief Q&A session with the company
management team (aka the rest of the class and the instructor) to discuss approaches, findings
and recommendations. The consultant case presentations should take approximately 15
minutes followed by ~5 to 10 minutes of Q&A.

Each team shall post the final version of their presentation file to Canvas PRIOR TO presenting in
class.

TIPS FOR SUCCESS:

Each team is required to meet at least once with their instructor (outside of class) to discuss
their approach to the case. Teams are responsible for proactively scheduling this meeting at
least two weeks prior to their scheduled presentation date.

Leading up to your presentation make sure you have practiced presenting as a team to make
sure your story is clear, and your hand-offs are smooth. On the day of the presentation arrive to
class early and make sure your slides work.

Take this presentation seriously. Be professional in the way you prepare and present. Use this as
an opportunity to practice presentation skills for the real-world. Make sure you introduce
yourselves clearly, be confident in what you are presenting, and be sure to end strong with a

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, and Alice Sheehan (UNH Peter T Paul College of Business and Economics)
 ADMN 580 - CASE 01
Forecasting Ticket Revenue for Orlando Magic
clear conclusion and call to action for your client. Dress code for teams presenting is business
casual.

SCORING RUBRIC:

Following is the rubric that will be used to score all the case presentations this term.

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, and Alice Sheehan (UNH Peter T Paul College of Business and Economics)