Over the last few years, the main qualifications for a general manager of a sports team have changed dramatically. Gut feel and experience have been replaced by analytical insight and predictive modeling. If Billy Beane started the trend with his Moneyball approach to team building, GMs like Daryl Morey (Houston Rockets) and Theo Epstein (Chicago Cubs) have taken analytics from cool to essential.
And it’s not just GMs that are embracing analytics:
- Boston Celtics Head Coach Brad Stevens is leading the charge in the NBA.
- Major League Soccer is using data trackers embedded in uniforms to track players in real time.
- Statistician Nate Silver made a name for himself in baseball analytics before becoming a force in the political world.
Like it or not, the Sports Analytics Era is here.
Later this month, front-office executives, sports journalists and analytics gurus will descend on Boston for the MIT Sloan Sports Analytics Conference. It’s more than just two days of keynote speeches and panel discussions around advanced analytics. It will also give conference goers a chance to network with the ground breakers and world changers that are paving the way for how sports teams and leagues can provide better in-stadium experiences for fans, maximize digital revenue and field a competitive team year in and year out.
Player analytics
As an avid sports fan, I’m always interested to see where the top college players get picked in the annual league drafts. With the NFL Combine coming up on February 22-25, teams will be looking to gather a plethora of data points to assist them as they decide which players to select in the NFL Draft in June – and in which round to draft them. Advanced analytics help teams sift through Wonderlic results, physical attributes, mechanics and football knowledge, just to name a few.
GMs also use analytics to help them decide which free agents to sign and which players to trade – both for performance and for salary cap implications. I love the story of Memphis Grizzlies VP of Basketball Operations John Hollinger. One of his first acts was to trade star guard Rudy Gay to Toronto. Many were shocked by this move, and some viewed the move as extremely unpopular. But using Hollinger’s own analytical formula, Player Efficiency Rating (PER), the Grizzlies traded for a trio of players that were more efficient and saved millions of dollars in salary in the process.
Analytics in the front office
Part of the front office's responsibility is to make sure that fans are engaged – in person and online. Even if the players are winning, teams with supportive fans will enjoy increased ticket, in-stadium and merchandise revenue. From fan experience to digital marketing, having a connection to your fans is critical.
Teams (like the Orlando Magic) and leagues (like MLS) are using analytics to make decisions that improve the fan experience – resulting in more revenue and increased loyalty. Knowing which messages are resonating with fans or which apparel is selling – both in stores and online – allows teams and leagues to make decisions that bolster their brands.
Digital marketing for sports organizations is changing constantly. Fans get their information and share opinions on a variety of social media outlets and they are more than willing to express their opinions. Loudly. This means that teams, leagues and advertisers can use analytics to manage how digital content is consumed as they listen and decipher fan sentiment.
The final buzzer
It’s not uncommon to see a story on the news about analytics and sports. But it’s not just ESPN airing these stories – it’s also the mainstream news outlets. Today’s advanced analytics touch every aspect of sports. Organizations that use analytical output to help them make personnel, marketing, sales and salary decisions will be better positioned to win on the court/ice/field/pitch, all while increasing fan loyalty.
SAS is proud to be a sponsor of the MIT Sloan Sports Analytics Conference. We can’t wait to hear all the exciting case studies and innovative ideas that come from the event. The agenda hasn’t been published at press time of this blog post, but one session we are particularly excited to see features author and researcher Tom Davenport. Tom has recently completed a research study into how sports teams and leagues around the world are using analytics to improve operations and find success. He’ll be available at the SAS booth following his session to field questions. Make sure you stop by!
Follow the action on Twitter at #SSAC14. Hope to see you there!
2 Comments
You mention Brad Stevens as a great example of a coach who embraces analytics. I've also highlighted Drew Cannon in the past -- he's the guy on Brad's staff who collects the data and crunches the numbers. When Brad left Butler to join the Celtics, he took Drew with him. That's a testament to his faith in the applied data science and its role in sports performance.
Chris, you're absolutely right. The teams and leagues that embrace an analytical culture have folks throughout the organization that "get it." I'm sure you could look at other professional teams that I didn't mention in this post, and find a ton of analytical talent there. For example, you can't tell me that Bill Belichick only relies on his gut on 4th-and-2.