Luke Stanke

Data Science – Analytics – Psychometrics – Applied Statistics

Sports Analytics Mirrors Real Life: My Big Takeaways from SSAC17

Last weekend I finished up my first Sloan Sports Analytics Conference. For many in the analytics world this attending means running into big-name sports analytics figures: Bill James, Jessica Gelman, Nate Silver, Bill Barnwell, and Daryl Morey, to name a few. And while the conference boasts big names that packed very large auditoriums, there were some excellent smaller sessions that anyone in attendance could have taken a lot from. Here are a few of my bigger takeaways:


Perception doesn’t always meet reality.

In any given sport there are 2-3 organizations who on the leading edge of innovation. These organizations are consistent performers and are constantly seeking competitive advantages in-game and in-business. Teams are now tracking in great detail the physical excursion of players in practice to minimize injuries, keeping players healthy, and keeping the in-game product at a high-level for their fans. Even more impressive are some of the fan experience analytics. Some teams are tracking many sources of data – beyond box score, weather, and attendance data to understand fans habits.

The conference highlights big wins in sports analytics. But in hallway conversations with other organizations – those not presenting – they were quick to confide they felt way behind. And that’s not really a surprise – while sports analytics has been a hot topic for nearly a decade the business only began to boom in the past 5 years. A team on the leading edge five years ago can easily maintain their competitive advantage. This leaves teams late to the game playing major catch-up.

"Today, you will need to understand this, whether you are the first or last on the team" – Luis Scola on analytics #SSAC17 #futureofbball

— Sloan Sports Conf. (@SloanSportsConf) March 4, 2017

There’s an overused quote: “sports are a microcosm of life”. But this phrase also applies to analytics. In any industry it’s easy to believe that all organizations – especially your competitors – have a strong analytics culture and are building an ROI analytics behemoth. It’s just not the case. Reality is a handful of organizations – paraphrasing, but I don’t think it’s out of line – have “figured analytics out”.


A culture of analytics goes beyond the team (on the field/court).

Let me re-iterate from my earlier points: it’s important to use analytics to improve player performance and the in-game product, but the culture begins with leaders who drive business decisions with data. There are team executives across multiple sports that drive success using both their expertise of the game and advanced analytics. But there are also leaders who are not convinced of analytics and believe intuition and the eye-test are still the true way to gain competitive advantage. This is just isn’t true: research has largely shown leaders leveraging data and their expertise produce the best outcomes in any industry.

Building an analytics culture also requires capable employees who are domain experts, are have capacity with advancing technologies, can code, and can distill all the data into clear stories for the organization to use. People with these skills are nearly impossible to find across any industry – that’s why most executives call them unicorns.

Be able to program

— Daryl Morey (@dmorey) March 3, 2017

That’s the thing, finding the right people for a team is hard. Sometimes the right move is promoting an analytics savvy individual from within the organization; sometimes it’s hiring a fresh-out-of-school MBA graduate.

Both can backfire. That person internally promoted might be a strong communicator with the business but not have the expertise to place appropriate probabilities to leads, leading to wasted effort by ticket reps. And that recent MBA grad might know how to build the best models to place probabilities to leads but might not know how to talk to the business, leading to the exact same problem.

I’m not saying I have a solution for this. But it’s clear that every organization faces the same problem. And that’s not just in the sports world, but also in every industry, everywhere. It’s clear that sports teams are trending towards the integrated use of analytics across all lines of business but because talent is hard to find there have been some growing pains in some markets.


Make sure a story can be told from the data.

In every session I heard the same thing – directly and indirectly: analytics professionals need the capacity for data-based storytelling. And those stories need to be actionable. In a session with basketball professionals, coach Vinny Del Negro stated he receives a 40-page analytics binder before every game. That’s 40-pages of information 82 times a season. Meaning coach Del Negro has to absorb 3200 pages to develop in-game strategies for a dozen players. It’s too much information to digest, so he often just scans it for a handful of key takeaways he can bring to his team.

"There are two types of coaches: those that embrace analytics and those that are unemployed" @adirshiffman #SSAC17 #sustaininggreatness

— Sloan Sports Conf. (@SloanSportsConf) March 4, 2017

When it comes to the players, women’s professional Sue Bird tries to put aside analytics on herself to focus on her teammates strengths. That message carried from session to session: most pros already know their tendencies. But most were less familiar with their teammates and competition. And that’s where analytics is most useful on-court. With these digestible bites of data she makes in-game decisions on which open teammate should get a pass based on situational statistics of each player.

Regardless of background – in pro sports or in another industry – it’s clear that the analytics professionals have to be able to take complicated ideas and bake them into a 3-5 point story that has clear meaning and easily executable. Without this skillset analytics is practically fruitless. But with it teams can develop improved efficiencies in-game or in-business.