You might think, wait, didn’t you just had a post that you are not going to bug us any more with your stuff? Yes, that’s true but as I said I will still be posting my (subjective) opinions (or tutorial-style posts). So I had a discussion with various colleagues lately that I bounced around some ideas on how sports analytics (and tech) research can be finally transform from a “cult” in academic circles to a full-blown research area. First of all, people that think sports analytics are all about predicting winners in sports competitions and creating metrics for coaches’ and managers’ consumption they are far from reality, and most probably have not spent time thinking about the deeper questions that can be answered with this. As a crown jewel example, for those that have missed it, Richard Thaler received the Nobel Prize last year and one of his famous studies was related to NFL draft, where he and Cade Massey used the drafting process to study overconfidence and other behavioral trends in decision making. David Romerstudied the fourth-down decisions and placed it into the context of a firm trying to maximize its profit. Others have studied sports labor market and market composition as a proxy to answer more general questions in labor markets. I could keep going on with studies that have used sports data to understand the process behind human decision making, cognitive bias, financial markets etc. but this is not the point here. The point is that sports data and analytics are not only about helping teams win (which by itself obviously offers a tremendous thrill and excitement) but this setting provides a clean, kind of in-the-tube, environment for studying interactions between agents and groups of people. The availability of coarse grain data today (i.e., player tracking) allows us to study decision making at a different level; e.g., how do players/teams perceive spatial risk (unfortunately I cannot talk about this openly, and this is something that can change with what I will talk in the following)? Sports tech that is developed for monitoring athlete’s practising, performance and rehabilitation, can also have applications beyond sports, to every day people that want to stay fit and healthy. Optical tracking technology that tracks basketball players playing basketball can have applications in tracking the total flow of pedestrians (note: not individual pedestrians) in an urban area, which will then allow us to understand how to improve the design of our streets. I also assume I do not have to get into details about medical research. Sports research is not only for sports, it does not end in sports! This is something that people in academia have to understand – and I have kind of put this as my next goal!
Where am I going with this? Well unfortunately in academia people tend to choose their research topics based on where there is funding, since they are constantly pressured by their supervisors (that is, deans, provost, chancellors etc.) to bring in research funds. This is understandable but sometimes we need to make short-term sacrifices (in this case do some unfunded research) to see the long-term potential (i.e., cultivate the land for research funding in the area of interest). I think this time has come for sports analytics. With more academics being hired by league offices and analytics departments of teams I expect them to be more open to these possibilities and initiatives. Tech giants like Google, Facebook and Amazon offer university grants for topics that are tangentially related to their focus. I think that this is a great opportunity for league offices and teams to get answers from academia to questions they might have been trying to answer. After all they are trying to do so through hackathons (which I do not think is the best way to do it – in my opinion). On the other hand, academia can get access to data and funds that will allow them answer deeper questions (similar to the ones people like Thaller, Massey, Romer and others have tried to answer) that advances science as well. This kind of industry grants are not large (typically between 50K-150K) but they are enough to fund one or two years of research and have a long-lasting impact in the organization. In fact, Pitt made some initial effort this year to promote research on sport tech. And as the call mentions: “While the immediate goal is to improve athletic performance, the solutions developed are expected to have broad applications and the opportunity to positively impact people of various ages and physical conditions“. I hope we will see more of that and not only from Pitt and other universities but from professional sports entities too. Actually Texas Rangers took a (very minor) step towards this offering a small scholarship to undergrads that came up with an “innovative analytics approach”. Of course the ratio of the reward to the potential benefits for the Rangers is a bit skewed but still a start…
I can see this being a win-win situation but there are two things that need to be done:
- Make sure academics understand that sports analytics is not a “cult” but a field that offers data that can help answer deeper scientific questions in a variety of fields and problems
- Make sure league offices and teams understand what academics can bring to the table and it is not just for winning the next game (which can very well be of course) but it is mainly for better understanding processes and improving practises for the long-term
Let’s see how things unfold in the years to come…!