Since moving to Boston in 2016, one of my bucket list items was to attend the MIT Sloan Sports Analytics Conference, otherwise coined as Dork-A-Palooza by Bill Simmons (who was in attendance). The event encompasses my two passions and brings out the top figures in sports management and media. This year was no exception, with a speaker list that included Michael Lewis, Malcom Gladwell, Adam Silver, Stephen Dubner and even Paul Pierce. With 3-4 talks going on at any one time, I had to go into the conference with a strategy of what I wanted to get out of the event. Being a basketball fan, I could had easily spent the entire conference attending sessions just about that one sport. To ensure I got the most of my experience, I made an effort to concentrate on attending sessions that would help me relate business problems seen in the sports industry to my day to day.
Over the two-day event, I was fortunate to see nine talks that covered topics such as how development of athletes has changed in this generation to debunking traditional soccer strategy. Even though these speeches covered different sports, the problems that teams and leagues are facing, are comparable to that of bank or a retailer, how to handle an abundance of data. This caught me off guard because two years ago when I was watching speeches from this conference on my phone, the message was that teams had limited data and if they had data, they were struggling to get it to a usable format. Now just two years later, the industry has gone a complete 180. This can be contributed to the popularity of RFID chips on jerseys to track player movement or wearables to track heart rate and energy, but this shift in this short of time is significant. This acquisition of a plethora of data now leaves teams baffled of what to do with data and where to apply it.
There were two talks, “Basketball Analytics: Hunting For Unicorns” and “Managing Big League Levers”, that talked extensively about the struggle to comprehend and leverage the amount of data that teams are getting. In “Managing Big League Levers”, a representative talked about providing tracking data to teams that they had collected from RFID chips on their jerseys. Initially, teams were intrigued by this data but soon realized they were not sure what to do with it. Teams had a hypothesis of how this information could help their organization, but the data is so intensive that they do not have the time to analyze and digest it. This is a common issue that I have run into with clients. They get new data and want to start plugging it into their everyday business. However, once we start reviewing and validating the data, we tend to see that the data is not ready to be plugged in and start printing money. That leads to taking a step back, developing a use case for the data and spend time cleansing, profiling and then evaluating. Then if all goes well, the data is in a workable format and paints a clear story for us to gain value from it. This goes to show that not all data is good data (or at least in its original state).
The second talk, “Basketball Analytics: Hunting For Unicorns”, was unique because it was one of the few panels that integrated athletes with management and media. One of the questions raised to the athlete, Paul Pierce, was “Is there a tipping point where you would get too much data”. Pierce highlighted that data could come in a variety of ways to him, whether it was by video breakdowns, shots charts, or even tables of statistics. With all this data, it is clear to see that being inundated with this information could lead to decision paralysis. Therefore, it should be clear that Pierce said YES, but he does find that data in doses can be useful. He broke down that the best ways for him to ingest information to prepare for the next game was for the analysis to focus on a few key points, specifically examples of how to exploit those points and an action plan for success. Hearing Pierce say this probably made me feel as close to a NBA player as I will ever be. On a daily basis, I am given large tables of data and asked to make decisions based off of this information without much context. This is often frustrating to be given but once you develop a process to digest the information, whether it is pivot tables or data visualization, the picture becomes much clearer. This is an important concept for companies to remember as they move to a data driven culture, which is even though you have “meaningful” data, if your team can not parse through it, there will not be value added insights for the organization.
Though my opportunities to work with sports teams may be rare, I could relate to the business cases that were presented at the conference. The sessions that I sat through gave me a variety of applicable use cases through a different lens than I am accustomed to, which helped the checkbox of my objective of attending the conference. Overall, my biggest takeaway from the conference was a confirmation of what I tell clients and students frequently. No matter what industry you are in and how much data your company has, if you do not have the right people in place to interpret the results, your organization will struggle to adapt to be a data driven organization.