I love this conference (for my take on last year’s SSAC, click here). I have zero professional connection to the world of sports business or analytics, and I rarely discover anything investable here, but I still find this two-day affair one of the most thought provoking and fascinating on my calendar. While it continues to get bigger every year (2,700 attendees in 2013, up 25% from last year), it still has that feel of the Game Developers’ Conferences of the early 90’s, when information flowed and there was a collegiality of common purpose (and common nerd/outsider status). The students, data geeks, ESPN celebs and professional sports insiders mix freely. SSAC is certainly getting more corporate, but it still has a start-up feel.
The presentations were extremely diverse, as usual. But there were a couple of dominant themes in the sessions I attended (and there’s a bit of selection bias in the sessions I attended given my own interests):
1. The rise of spatial data. I flagged this as a trend last year, but this year it was clear that spatial data is going to provide extremely important explanatory power for analysts in basketball, baseball, and probably football and soccer, too. It may be the key to unlocking the tricky problem of quantifying defense, which has always been a challenge for conventional statistics.
2. The importance of communication. Probably in response to the continued resistance the data geeks are encountering inside clubs, there was a very strong “story-telling” theme at the conference. Visualization was a hot topic. Making complex data understandable to non-geeks inside clubs, and to fans, was a repeated concern. I was particularly impressed by the “data cartographers” like Kirk Goldsberry (CourtVision) and Joe Ward (New York Times). These guys are true artists.
3. The acknowledgement that data/analytics have significant limitations. This was an interesting theme — the notion that luck and randomness can obfuscate data, that humans have strong results-bias, and strong loss-avoidance bias. It showed up in several talks, as did the idea that intuition and judgment play important roles in decision-making, and the human vagaries and biases that come with that intuition and judgment. I thought Michael Mauboussin did a great job providing a framework for these ideas in his brief talk on luck. The great Nate Silver (whom Bill Simmons referred to as “Dork Jesus” during the show) also provided some interesting insight here.
4. Open-source vs. proprietary data for competitive advantage. When Bill James and the SABR-metricians were making their contributions to baseball in the late 70’s and 80’s, they were a fringe group working with public data. They worked for decades without the taint of team sponsorship, before Billy Beane and Moneyball dragged them into public view. Now that sports analytics has gone mainstream, a lot of important work is being done by in-house number-crunchers, working in secret, in order to provide clubs with competitive advantage. The lack of community and lack of broad data access may be retarding analytics innovation in sports like basketball (as this Slate piece discusses) and almost certainly in soccer. I’ll discuss the soccer problem in another post, but it was remarkable how little progress there has been developing “game models” for soccer, from the public perspective.
For me, the unexpected insight was that this conference has a lot of applicability to venture investing. A lot of the problems the sports world is struggling with — talent assessment, understanding the contributions of skill and luck to success, the use of data to derive insight into “black boxes,” valuations and the upside potential of players at various stages of development, etc. — have analogs in the venture business. Paying Joe Flacco $120MM over 6 years at this stage of his career is very similar to venture investors betting big dollars and high valuations on previously-successful 40 year old repeat entrepreneurs. Maybe not such a good idea.