This weekend I had the privilege of speaking at the New England Symposium on Statistics in Sports. It is a much more technical conference than the Sloan Sports Analytics Conference so I felt a bit like a duck out of water given my background in computer science and not hardcore statistical methods (and these guys were hardcore!). Originally I had planned to do a write up, similar to the one I did for SSAC, but there was too much going on for me to take adequate notes. I really enjoyed chatting with a lot of people who are similarly passionate about their respective sports and take the time to sit down and produce cool stuff. The panel discussion was also fascinating. Some of the themes that were discussed during SSAC carried over such as:
Read more
Archive for Sloan Sports Analytics Conference
NESSIS Wrap-up and Slides
Data Visualization and Sport: Passing Wheel
One of the main themes of the Sloan Sports Analytics Conference was the need to present your ideas to the rest of the organization in a manner that is easily understandable. Rambling on to a club’s manager about a stat varying from the mean by several standard deviations won’t go over so well. It got me thinking about the field of data visualization and it’s potential application to sport. Used properly, it can easily tell the story behind the numbers.
Why Averages Aren’t Meaningless
During the Basketball Analytics Panel at the Sloan Sports Analytics Conference, Mike Zarren and Mark Cuban both mentioned their dislike of averages, preferring instead to look at recent performances. Their complaint was that a season average doesn’t tell you very much because after a while it becomes hard to move (i.e. a string of poor performances would be hard to detect). Fair point, there isn’t a whole lot of information that can be gleamed from just looking at an average, however, there is information to be gained by looking at the distribution of values.
Let’s say you’ve identified a set of metrics that measure specific aspects of your team’s performance. Since soccer is such a low scoring sport, the better team doesn’t always win because of randomness — a deflection, a missed offsides call, a red card for not hearing the whistle. How can you compare your team’s performance against it’s past performance, especially against differing opposition? Assuming the metric you’re looking at is normally distributed, you can use the mean and variance to see if the team is over or under performing for a specific match compared to their previous performances. Read more
Sloan Sports Analytics Conference Recap
This was my second year at the Sloan Sports Analytics Conference and it was interesting to see the changes that they have implemented from one year to the next. The biggest differences were more attendees and extending the conference to a second day. There were a few people I had wanted to meet up with early on the first day, but given the increased attendance, meeting up with them was like finding a needle in a haystack. Fortunately, the conference attracts a lot of really great people so I was able to meet some wonderful folks through random encounters. A lot of the value of the conference is in the hallway conversations and unfortunately that isn’t something you get when watching the taped sessions online.
The conference overloads you with a wealth of information that is difficult to take in all at once and it is impossible to attend everything you’d like to due to scheduling conflicts. My key takeways were:
- Analysts need to build trust with those within the organization
- The way the results are presented is crucial
- The human element can never be taken out of the game (ie: shooting freethrows in key moments is incredibly difficult)
- Teams are interested in how they can win more games, not player rating systems
- Teams can measure things that outsiders cannot (ie: missed blocking assignments)
- Season long averages are often meaningless, variability and recency are more important
- Impact is more important than averages (averages can be inflated by blowouts, teams want to know who is contributing to wins at key moments)
- Swings of chance are large in football so analytics might not always predict the outcome (this doesn’t mean they aren’t useful)
- The psychological makeup of a player is incredibly useful information and hard to quantify. A coach can get a good read of a player’s ability from watching him but it is almost impossible to know ahead of time if the guy loves the game or when his love for the game will leave him.
- Analytics aren’t a replacement for decision making. At the end of the day, someone needs to look at all of the information (beyond analytics) and make a decision. Analytics helps, but it isn’t a complete solution.
Details Set for Sloan Sports Analytics Conference Meetup
The final details are set for anyone interested in meeting up after Day 1 of the Sloan Sports Analytics Conference. We’ll be meeting around 7:30pm at MJ O’Connors which is located in the Westin Waterfront. Anyone is welcome to join us and talk shop. So far we have a pretty good group shaping up. Here are some links to work done by possible attendees:
If you’re interested in connecting with more SSAC attendees, there is a Lanyrd site set up to help people connect.
Sloan Sports Analytics Conference Meetup
For those attending the Sloan Sports Analytics Conference, a group of us will be getting together Friday night for dinner/drinks and shop talk. Details are still being ironed out, but if you are interested in coming, send me an email at srudd@onfooty.com. Feel free to include a link to your blog or other work and I’ll include it with the final details.
Who’s Going to the Sloan Sports Analytics Conference?
The Sloan Sports Analytics Conference is a little over two weeks away, taking place March 4-5 in Boston. I attended last year and had a great time, and I’m really looking forward to attending again this year. They’ve made a lot of changes, such as extending it to two days, that I hope will improve the experience.
