Quantifying The Success Of The Pro Leagues

A reader sent along a final paper for a college course that suggests some ways to systematically compare the Triple Crown, the American Ultimate Disc League, and Major League Ultimate.

Josh Hamilton, a student at Montana State University, wrote a final paper for an economics course creating a framework for analyzing the pro leagues. Here are some thought provoking excerpts from the paper:

…The Triple Crown Tour (TCT) is the descendant of the current USAU National Championships, it will likely maintain the best athletes, and have established teams, but it is just starting to promote its teams outside the ultimate community.  There is almost no central control of teams in this model, teams only have a few tournaments that they have to go to, and there is no revenue sharing or requirements of the teams to promote themselves or drive attendance…

… The AUDL has more central coordination than the TCT, but teams still have a significant amount of freedom to set ticket prices, and find their own video streaming…

…The MLU is the most centrally controlled league, creating league-wide promotions, setting prices, and generally controlling many of the operations of the teams. Unlike in the AUDL where owners actually bought franchises in the MLU investors only bought a share of the league.  The MLU will have by far the least differentiation between business strategies of teams, including having all of their games streamed by the same company.

This is almost a natural experiment into what the best method for regulating a network industry is.  These three league have all taken different approaches to the management of network industries, from almost no coordination in the TCT to a vast amount of control in the MLU.  This allows for a fantastic opportunity to see which of these models is the most successful.  Since the literature has been unable to provide a definitive answer to how to regulate network industries the relative success and failure of these leagues may be able to show what model would be most successful…

…If the leagues hold up and there is data to be analyzed then I will have two primary variables of interest for each league model: how it will grow the visibility of the sport and how the individual teams profit from innovation and success. I chose these because growing the industry as a whole and knowing what the effect on individuals firms are two of the primary goals of policy changes.

Visibility of the sport will be measured by viewings, either on TV or online, as well as spectator attendance at games.  Visibility of the sport is a proxy for the size of the entire industry, as the entire industry grows and more people pay attention to Ultimate it gets easier to talk about it with your friends. Each league or team can do things that will promote the sport as a whole, and those things will likely have a benefit for the other leagues. Spectator attendance will measured both by absolute numbers and by attendance revenue, as attendance numbers will be greatly affected by ticket prices. Revenue will will give an approximation for ‘willingness to pay,’ assuming that teams set their ticket prices to maximize revenue…

It is important to differentiate between TV or online viewings and attendance.  Fans who go to games are very likely to be fans of the home team, and want to see their team win.  Fans who are just watching are much more likely to be fans of other teams, or of the sport in general. It should be tested to see if there is a difference between these two groups to determine the validity of the uncertainty principle, that the outcome of the game needs to be in doubt for fans to attend.  There have been studies that have both supported (Depken, 2006) and decried (Buraimo, 2008) the effect of the uncertainty of outcome. I would like to test to see if this data set will allow me to detangle the effect of fans wanting to see their team win, from the effect of fans wanting to watch a close game.

The other key measure will be the profitability of the individual firms.  This will be the actual profit of each of the teams if that can be obtained, otherwise total revenue or some similar number would serve as a reasonable proxy. The success and failure of each of the teams mirrors individual firms. How they react to incentives to innovate will give us insight into how firms will react in other network industries.  It is possible that one league will have a less talented product on the field still be more profitable because of its better business practices.  This would be a prime example of one model succeeding over the other, so controlling for the quality of frisbee on the field will be a major obstacle.  The profitability of the firms is also very important for to tease out the relative strengths of different incentives for individual firms.  Figuring out the strengths of incentives will make it much easier to design policy to regulate network industries.

This is potentially interesting research. There are three very distinct models for each of the leagues — something we’ve written about a lot in the past. We do plan to monitor the attendance and game-day revenue from the leagues as much as possible. Of course, the TCT is player-funded, not spectator-funded, making an apples-to-apples comparison basically impossible.

Regardless, much as we have a natural referee experiment going on (self-officiated, observed, officiated), we have a natural business model experiment as well. It will be a fascinating case study to follow over the coming years.

  1. Charlie Eisenhood
    Charlie Eisenhood

    Charlie Eisenhood is the editor-in-chief of Ultiworld. You can reach him by email (charlie@ultiworld.com) or on Twitter (@ceisenhood).

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