For many years, the NFL has tried to bolster ticket sales to undersold games by blacking them out on television. But as that option may soon be off the table, many NFL teams have been considering a new ticket pricing model. The Detroit Lions became the first NFL team to adopt variable ticket pricing for the 2014 season. Analytics and Football may be moving closer together.
In the current approach to pricing single game tickets taken by most teams, the same seat has the same price for each game of the season, including the preseason, regardless of the opponent. With variable ticket pricing, the same seat may be priced differently for different games, depending on the forecasted demand for that game. Contests that are expected to sell out would command a premium, while preseason games and games against a lackluster opponent would be priced less to account for the lower interest in the game.
Detroit announced that each game will now be assigned a level of demand from 1 to 3. Level 1 will consist of lower-demand and preseason games. Level 3 will include the traditional Thanksgiving Day game and other high-demand primetime games like Monday Night Football.
This new approach to pricing can help teams boost ticket sales, but may be too reliant on the day and date of a game to determine fans’ interest. How can teams accurately forecast demand to make sure they don’t leave anything on the table?
Push it over the goal line with predictive analytics
To answer these questions, Beyond the Arc recently evaluated which factors might influence ticket prices for an NFL team. We approached the problem as we would any business research question, applying our data science methodology to design an approach to the problem. The steps of the process include:
- Business understanding
- Data understanding
- Data preparation
During the business understanding phase, we explored key theories about which factors might influence the willingness of fans to pay more for specific games. Some of these variables included:
- Temperature and weather conditions on game day
- Win-Loss record of the visiting team
- Returning home team players on visiting team (e.g., Peyton returning to Indianapolis)
- Playoff contention
- Week number in the season
- Day of the week
- Rivalries between teams
- Excitement factor of the teams and players
- Social media commentary and sentiment
The data draft
To test our hypotheses, Beyond the Arc obtained publicly available attendance and sales data for a particular NFL stadium and augmented that data with information from other public sources. Regional rivalries, simple win-loss standings, and weather and temperature at game time were easy to gather from Wikipedia. However, we needed a way to quantify “excitement,” so we looked at:
- The number of ProBowl players on each team
- The ESPN weekly team rankings
- The Las Vegas betting lines
- Information from various Fantasy Football research sites
The ProBowl players are a matter of historical record and easy to find on Pro-Football_reference.com. However, if the NFL sticks with its new method of selecting ProBowl players who are chosen draft-style by two former NFL players (this year by Deion Sanders and Jerry Rice), then this factor will not play a role in determining ticket prices as there is no reliable way to predict which players will be chosen prior to draft day.
The other information on team excitement is more subjective, so we sought different kinds of crowd-sourced data in order to get a balanced perspective.
The ESPN rankings are developed by sports experts. The Vegas Insider data claims to be the best handicapper of sports events in the world. Whether or not that’s true, we can leverage their opinion for this research. Finally, we found team ranking and player information from Fantasy Football sites like NFL.com and fftoolbox.com. Fantasy Football data is both peer-reviewed and self-correcting, two attributes we look for in a reliable dataset.
Defense wins championships, winning sells tickets!
So as not to give away the recipe for a variable ticket pricing system, we won’t reveal our exact findings in this article. However, we did find that weather was not a key factor as we had anticipated. Rather, competitiveness throughout the season stacked up as a key determiner of continued fan interest and willingness to buy tickets.
So, if struggling teams want to pack the stadium for late season games, they may want to consider incentives or discounts that lower the effective cost of each seat. A “frequent fan” program that offers discounts and rewards based on the number of games attended could drive loyalty and keep seats warm on cold December Sundays. Data science insights could help to identify the right tailored offer to make to each individual member of the loyalty program.