For its first 2 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.

* Day of week rated as 1=Monday, 2=Tuesday, 3=Wednesday, 4=Thursday,5=Friday, 6 =Saturday,5=Sunday,3=holiday.
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:
y = 14,996 + 10, 801x1 + 23,397x2 + 10,784x3
Table 4.2 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.