Building off of Part 1 and Part 2 on the relationship between winning and revenues, I’ll share highlights of my Revenue Opportunity Index—an analysis I began at the end of last season to get a better sense of the which teams could expect the greatest financial gain by improving on-field performance for the 2012 season. I initially used this to gauge who might be the most active players in the free agent market, but modified it to give an indication of the opportunity for revenue upside for this season. The index combines “raw” revenue opportunity with the expected competitiveness of the team. Raw revenue opportunity—an analysis of a team’s unrealized and potential revenues—includes a team’s ticket revenue upside, based on the current (previous season’s) attendance, ballpark capacity and average ticket prices. If a team’s average ticket price is far below the MLB average, they are deemed to have more ticket pricing upside than teams like say, the Yankees, Cubs, and Red Sox, whose prices are already far above the league average. The combination of empty seats and ticket pricing upside determine the potential for attendance revenue growth. I combine this with overall market size and an estimate of “brand strength”, which is intended to get at a club’s likelihood of realizing the attendance upside. Next, I give points for the attraction of a new stadium (Marlins). It’s been shown that the synergy of a competitive team and a new ballpark can accelerate revenues. Finally, I factor in a team’s recent postseason history—both their success in the postseason and the time since their last appearance. A recent deep run through the playoffs suggests a portion of the revenue opportunity has already been realized.
Onto this raw revenue opportunity I overlay a team’s anticipated competitiveness. This speaks directly to the point I’ve made in the past (in Diamond Dollars and elsewhere) that certain ranges of wins have a greater financial value. If a team was expected to be hovering in the 85 to 93 win category in the NL, they were assigned a higher Competitiveness index than say the Astros, a team that was not expected to be competitive and therefore not in a position to truly capitalize on their raw revenue opportunity. A competitive team provides motivation to fans to reach into their wallets to generate revenue for the ballclub. The Revenue Opportunity Index combines the raw revenue upside with a team’s expected competitiveness. Entering the 2012 season, my Index ranked the following teams in the top quartile: the Dodgers, Angels, Braves, Blue Jays, Rangers, Marlins and White Sox. The Dodgers topped the list, having been beaten down in the latter stages of the McCourt years via declining attendance. They represented a large market, lots of unused seating capacity and reasonable ticket prices, not to mention a great brand. Combine these raw revenue factors with a team that was expected to contend in the NL West and the stars were aligned for the Dodgers to drive revenue growth for 2012, under a new revitalized ownership group. A team that is conspicuously absent from my list that was generated prior to the season is the Washington Nationals. I expected the Nats to be at least the 3rd best team in the NL East, but underestimated their division leading pace. After escalating the Nats performance expectations, I ran an alternate scenario through my model and the Nats easily came out in the top quartile of revenue growth for 2012. On the other hand, Toronto is a team I mistakenly expected to contend for a wild card spot in the always tough AL East. Given their half-full Rogers Centre, there is tremendous revenue upside, once the Jays truly challenge for the postseason.
The Rangers have tantalized their fans with near-championships in back-to-back seasons and are realizing the value of their playoff success, with nearly a 20% increase in attendance over last year, on top of an 18% attendance increase in 2011. On the other hand, there are instances of attendance declines in the face of improvements in team performance. Sometimes the layout of the schedule can have a measurable impact on a team’s attendance. One might think the way the schedule falls over the summer and which interleague teams are coming to town has a minor impact, but I’ve seen schedules that have generated a 5% or 10% swing in attendance, independent of team performance. For example, if a team in a cold weather, northern climate (without a dome) has the Yankees and Red Sox come to town in April, rather than weekends in July and August, it can mean six or seven games with 20,000 less fans in the stands. The number of home games in various months can also have an impact. Northern cities dread April and early May games, when fewer fans are willing to brave the elements to attend. On the other hand, the Arizona Diamondbacks thrive on April games, while snowbirds are still in town, but dread July and August games when the local population base shrinks.
Two other relevant points to add about the impact of winning on revenues: First, the fans response to winning is not instantaneous, as I discussed in an earlier post. In the past, when I’ve modeled annual attendance as a function of on-field performance, I found that a combination of the most recent season and the previous season had the greatest explanatory power. Second, excess capacity is not always a positive factor for realizing revenue growth. After talking with hundreds of fans in focus groups and surveying thousands of others in quantitative surveys, it’s clear that an abundance of empty seats reduces the urgency of fans to make an advance purchase, or to buy season tickets. Their perception is “good seats will always be available.” Anything that allows fans to wait to purchase tickets, usually has a negative effect on ultimate attendance, as some fans who had good intentions to buy tickets on game day find that often “life gets in the way”, or an unfavorable weather forecast causes a change in plans. Also, another penalty of a near-empty ballpark is that it detracts from the atmosphere and excitement of attending a ballgame, for both the attendee and the television viewer, discouraging future ticket sales.
Statistical modeling can provide insights to the ebbs and flows of team attendance, but the analysis is most effective when it includes a framework that incorporates consumer behavior. Just like the best explanatory models or forecasts of player performance must include factors that are not on the stat sheet, the same is true for attendance and revenues, or for that matter, any behavior models.