Beyond the Box Score: Essential Lessons from the Sloan Sports Analytics Conference
My Recap of the 2023 MIT Sloan Sport Analytics Conference (SSAC)
The Next 2 Big Things?
It is easy to dismiss the future of sport of Pickleball as a fad. As a recreational tennis player, my first impression was how would this be any different from any other racquet ball sport? However, at some point in time, every game began as a niche activity.
Watching pickleball at the SSAC did change my view. The conference had not only had an open pickleball court where participants can play; they also had professional matches. The amazing part of this is that it was in a hotel! The fact that these matches can be played indoor and outdoor in a variety of different settings gives it extended reach.
Pickleball has many things going in its favor: it can be played by all age groups, it’s affordable and relatively easy to learn. Add the fact that people, especially seniors, were looking for a socially distant way to stay active during the pandemic; pickleball had the perfect conditions to grow to 36 million participants in the U.S.
This growth has caught the attention of the sport, entertainment, and business world. This includes investors such as Lebron James, Tom Ricketts, Tom Brady, Heidi Klum, Kevin Durant, Rich Kleiman, and Patrick Mahomes.
I’m still not sure whether the game will continue to grow at the same pace, but it is clear that the sport has all the opportunities to be the next big sport.
Speaking of new sports, there was another one that I saw for the first time at the Sloan Sports Analytics Conference….
While chess as we know it has been around since the end of the 15th century, hand and brain chess became popularized just a few years ago when it started to be streamed on Twitch.
Compared to traditional chess with 2 players, hand and brain chess is played with 4 players split into two teams. On a team, one player is the “hand,” and the other is the “brain. The only communication that is allowed is that the “brain” can tell the hand which piece to move. Then, the brain decides how to move the chess piece based on the traditional rules of the game. Time controls can be in place that only allows the hand and the brain a set amount of time to make their decision.
This version of chess introduces an old game to a new audience/generation, it allows novices to play with experts, and it makes a great team-building exercise similar to what Pickleball did for the more well-known game of Tennis.
What do you value: Process or Results
My favorite session of the conference was Data Centered Decision Making: How to Implement a Process that maximizes the Value of Your Data. This session was led by Benjamin Alamar, who once worked for the Cleveland Cavaliers as a Senior Analytics consultant for the Cleveland Cavaliers.
He started off his presentation with taking us back to the 2013 NBA Draft where the Cavaliers had the 1st pick of the NBA Draft. The Cavaliers had settled on a final list of 5 players to choose from. Each one of those players were assigned ratings on whether they were projected to be a star or a bust.
Player 1: 6'8 Power Forward for Mid-Major University
Star Potential: 75% GM, 40% Assistant GM, 3% Analytics
Bust Potential: 10% GM, 10% Assistant GM, 55% Analytics
Player 2: 6'5 Explosive Guard from Major University
Star Potential: 50% GM, 35% GM, 3% Analytics
Bust Potential: 5% GM, 5% GM, 25% Analytics
Player 3: 6'8 Small Forward from Major University
Star Potential: 20% GM, 10% GM, 9% GM Analytics
Bust Potential: 15% GM, 20% Assistant GM, 15% Analytics
Player 4: 6'11 Center from Major University
Star Potential: 40% GM, 20% Assistant GM, 3% Analytics
Bust Potential: 10% GM, 30% Assistant GM, 15% Analytics
Player 5: 6'5 Shooting Guard from Major University
Star Potential: 35% GM, 40% Assistant GM, 30% Analytics
Bust Potential: 15% GM, 35% Assistant GM, 60% Analytics
Ultimately, he had the audience select based on this information and a scouting report who they would select in the draft. 43% of participants selected player 5, 35% of participants selected player 3 and no one selected player 1.
Then, he revealed the players….
Player 1: Anthony Bennett
Player 2: Victor Oladipo
Player 3: Otto Porter
Player 4: Nerlens Noel
Player 5: Ben McLemore
Of course, this draft was known as the one where the Cavs selected Anthony Bennett with the 1st pick of the draft. One of the major themes of the presentation was how data had a seat at the decision making table instead of data being at the center of the table where each individual had to use data to support their ideas.
I believe this to be true but I think there is a separate issue at play here. Just because the decision ended up being bad; did it mean that process was completely flawed.
What happens if the Cavaliers GM felt this…..
A few years ago, lost the best player in franchise history who was grew up a hour away from where we play. In a considered to be weak draft where we cannot trade down. We need to go after a player who has a very high ceiling to pair with our current star point guard (Kyrie Irving); if the pick doesn’t work out then we will be drafting high again soon versus a solid pick that puts in the pit of mediocrity.
Then do we have to look at this differently? If you sum the percentages, the organization figured that Anthony Bennett had a chance to be a star but also a high chance of not working out. The risk was one the organization was willing to take. Just like the presentation mention the role of biases in humans making flawed judgments (hence the need of data-centered decisions), there is also a prominent role that chance/luck/favor/divine intervention plays in many choices. What we can control is that we stay true to what our objectives are.
The Cavaliers struggled that year, went back to the lottery, received the #1 pick again! and drafted Andrew Wiggins to trade for Kevin Love which, combined with the return of Lebron James led the franchise to its first NBA Championship.
One does have to wonder, if this was true, why did the GM think Anthony Bennett had that high of a ceiling?
The House Wins (Normally!) : I don’t think Sports betting is any different than other Casino Games.
I wonder if my generation would be the last to remember a world where sports betting was not widespread. There were at least 6 sessions that dealt with some aspect of betting. What I learned is there are really smart people that take a lot of time on either a) making sports betting a successful venture or b) making money of sports betting.
I really enjoyed playing fantasy football. It’s a way to have vested interest in teams you are not rooting for and you can win your league based off of your knowledge and shrewd strategies but once you talk about big money (i.e. daily fantasy), you are battling more than your friend or co-worker.
I joked with one of the participants that it’s crazy how many people play daily fantasy, but so few people win, and those people win with computer models, and he said, I’m one of the people with the models.
Here’s your fair warning for most sports fans. If you choose to play, play responsibility (i.e., for enjoyment).
What makes a good trade?
The Sloan Conference gave the award to the best transaction to the Utah Jazz for trading Rudy Gobert for Walker Kessler, Jarred Vanderbilt, Malik Beasley and four 1st round draft picks.
I feel like I have a bigger issue with the criteria for the award more than the selection itself. Even though the award was given to Jazz, it could have easily been worst transaction of the year being given to the Timberwolves. Some of this may have been my initial apprehension that a Rudy Gobert- Karl Anthony Towns frontcourt pairing would have some defensive liabilities, but I feel like the general sport consensus was the the Timberwolves got taken advantage of.
Ultimately I think a more interesting transaction of the year was another Jazz transaction later in the year.
Jazz Received: 2027 1st Round Draft Pick, Russell Westbrook, Juan Toscano-Anderson, Damian Jones
Lakers Received: D’Angelo Russell, Malik Beasley, Jarred Vanderbilt
Timberwolves Received: Mike Conley, Nickeil Alexander-Walker, 3 2nd Round Picks (2024, 2025, 2026)
In this trade, the Jazz get draft picks and cap relief for their young rebuilding roster, the Lakers get rotation-ready players to help their veteran win-now roster, and the Timberwolves get a veteran point guard to help put their new frontcourt pairing in the right position to succeed.
This transaction would be viewed by most as a win-win-win.
I feel like the analytics community needs to be careful about feeding into the narrative that nerds are just looking at spreadsheets and future assets and are not sportspeople. Transactions from an analytics standpoint could be one-sided, but they also can be win-win, and a win-win transaction means that more teams trust the data.
5. The Elephant in the Room: How to Increase Diversity
5 years ago, a prominent minority member in the sport community asked a question highlighting the lack of diversity at the conference and in sport analytics in general. In the conference’s defense, one of its primary focus was to get people together who worked in analytic roles for sport teams. That was a very small part of team’s departments since there was initial pushback on using data versus maintaining the status quo, which was led by intuition and experience. So it can be argued that the group was started as an inside group.
The industry as a whole still has a diversity issue which I believe is driven by the number of unpaid internships that restricts access to maintain students and young professionals who cannot afford to not be paid, but the conference has come a long way in that regard. This year, the conference had mentorship groups, multiple networking opportunities, resume reviews, and career conversations, and should be commended on their approach.
All in all, it is an event that I think all analytically driven sports fans should experience once to see the past, present, and future on how information is being used to enhance the sport experience from player selection, fan engagement, and injury prevention.
Thanks for reading!
