Dynamically priced baseball tickets

May 18, 2009 in Economics,Sports

Seven years ago, the Mets were among the first teams in MLB to adopt a tiered pricing system in which it costs more to see a game against a good opponent than a bad one. At the time, it was the most sophisticated such plan in baseball. Others included simple methods like charging more for weekend games or prime summer games.

The act was a major step toward recognizing that while the supply of tickets was more or less constant for every game, demand could vary wildly. Nonetheless, ticket prices were still set for every game before the season started, and were locked in no matter how attrative (or otherwise) the game ultimately appeared. Rain, cold, chances to see historial achievements, unexpected opponent records (good or bad) - all of these affect demand in ways which can not be anticipated in March.

Now, the Giants have announced a new initiative which will adjust ticket prices up until the first pitch. A software program will estimate demand based on dynamic variables and adjust ticket prices according. The factors include both teams' records, the stats of individual players (including the starting pitchers), the weather, the number of seats left to sell, proximity to gametime, promotional nights, and other similar metrics. Fans showing up on a rainy weekday night just before the game might be able to snag tickets for $5 that were $30 the week before.

It is likely that the model is not nearly as complex as one might think - in fact I imagine this is a perfect example of a time when a simple model can account for a surprising amount of variance. Not that this approach is revolutionary outside MLB - airlines and hotels have been doing something similar for years - but I think its an exciting and approrpriate use of data and would not be surprised if next season the metric is rolled out in a more expansive manner.

To think it was just a decade ago that the concept of statisticians on the field was inconceivable (but taken for granted today) - and now we have teams employing econometric methods to estimate demand. Quelle journee!

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