Several years ago I attended a SpORts (OR in Sports) session at the annual INFORMS meeting (I think it was New Orleans –> San Francisco 2005) and the room was packed. All the chairs were taken and latecomers resorted to standing against the back wall. Not only was this one of the most attended sessions of the conference (at least compared to the other sessions I attended), it also had an unusually high level of presenter-audience interaction.
In my experience it is fairly normal for a presenter to receive few, or even zero, comments and questions after a presentation. But in this SpORts session, several members of the audience interacted with the presenters; probing the data and methods, questioning the results and providing their own ideas for improvement. It made me wonder: What is it about OR applied to sports that made the audience so much more engaged than with other applications?
I think the answer lies in the accessibility of the data and results of a sports application of OR. Often only a handful of people know enough about an OR problem to be able to fully understand the problem’s data and judge the quality of potential solutions. For instance, in an airline’s crew scheduling problem, few people may be able to look at a sequence of flights and immediately realize the sequence won’t work because it exceeds the crew’s available duty hours or the plane’s fuel capacity. The group of people who do have this expertise are probably heavily involved in the airline industry. It’s unlikely that an outsider could come in and immediately understand the intricacies of the problem and its solution.
But many people, of all ages and occupations, are sports fans. They are familiar with the rules of various sports, the teams that comprise a professional league, and the major players or superstars. This working knowledge of sports makes it easier to understand the data that would go into an optimization model as well as analyze the solutions it produces.
If I remember correctly, one of the presentations during that INFORMS SpORts session was about rating/ranking NFL quarterbacks. Professional football is one of the most popular sports in the United States and even those who aren’t a fan can probably name a NFL quarterback. And those that are fans probably have their own opinions about how good each quarterback is and how the various quarterbacks compare to each other. People not connected with the project are familiar with the basic data components and can even generate their own potential solutions and judge the solutions generated by others.
This fluency in the problem makes the methods and results more interesting to the audience because they can understand aspects of the algorithm and determine for themselves its efficacy. The audience doesn’t have to trust the authors that 5.6 is the optimal solution. And the results can even validate their own personal opinions (“I knew Peyton Manning was a better quarterback than Tom Brady!”). I am sure there are other reasons why sports is such a popular application of OR but regardless it is nice to see it generating enthusiasm and interest for OR.
This blog post is a contribution to INFORMS’ monthly blog challenge. INFORMS will summarize the participating blogs at the end of the month.