Why Sports Still Leads the Analytics Revolution

We all know that professional sports is big business. In 2018, the NBA took in more than $8 billion in revenue and the NFL some $16 billion. Leaders in other industries might rightly look to professional sports for lessons in advertising, marketing, and customer engagement.

Today’s guest says that the sports industry has another gift to bestow on the larger business world: an understanding of how to get real value from data.

MIT Sloan senior lecturer Ben Shields argues that the use of data and analytics in sports is years ahead compared with many other industries. “It comes down to one word: competition,” Shields says in this week’s episode of the Three Big Points podcast. “For as long as sports have been played, teams and athletes are looking for a competitive edge. And it just so happens that today, and well into the future, data and analytics are going to be a source of a competitive edge for teams.”

For you skeptics out there who may be thinking, “Yeah, but my business isn’t about winning or losing in that concrete sports sense — the goals aren’t as clear cut, and neither are the problems and challenges,” Shields has fielded this objection before.

“I try to challenge business leaders to think about [how they can] create similar sets of conditions for competition,” he says. “We know that every business is in a competition in one way, shape, or form. How can you create proxy-type problems that are similar to what a sports organization might deal with?”

Shields gives us a full field-level view of how to adopt the most potent of sports’ lessons on data and analytics in this week’s episode.

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Transcript

Ben Shields: I think we’re getting into this culture now where, whether it’s athletes or even employees at your businesses, [they] are more receptive to using data to improve their own performance.

There’s the gut of the coach, there’s the intuition of the player — and, of course, there is data and analytics. And so what we’re seeing with sports is they have a very clear sense of the problems they’re trying to solve, and they are using data analytics as another input to solve those problems to ultimately win more games on the court, the ice, the field.

We know that every business is in a competition in one way, shape, or form. How can you create proxy-type problems that are similar to what a sports organization might deal with?

Paul Michelman: I’m Paul Michelman, and this is MIT Sloan Management Review’s Three Big Points. Each week, we take on one topic that leaders need to be on top of right now and leave you with three key takeaways for you and your organization.

We all know that professional sports is big business. In 2018, the NBA took in more than $8 billion in revenue and the NFL some $16 billion. Leaders in other industries might rightly look to professional sports for lessons in advertising, marketing, and customer engagement. Today’s guest says that the sports industry has yet another gift to bestow on the larger business world: how to get real value from data. He argues that the use of data and analytics in sports is years ahead of many other industries.

Ben Shields: It comes down to one word: competition. For as long as sports have been played, teams and athletes are looking for a competitive edge. And it just so happens that today, and well into the future, data and analytics are going to be a source of a competitive edge for teams.

Paul Michelman: Sports organizations have been at the cutting edge of data collection for years. Numbers can provide an edge. And franchises have an intense desire to win because winning directly impacts their bottom line. Of course, it isn’t just about having the data — it’s about what you do with it. Here is where sports further distances itself from other industries.

Ben Shields: What’s different about sports is, yes, the data and analytics are available; the difference is how teams and athletes apply the data to win games. It’s not that data is just sitting on a spreadsheet somewhere. This data is being communicated from the front office to the coaching staff and then, ultimately, to the players, who apply it to win games.

Paul Michelman: Shields says that there are three main areas in which sports analytics are succeeding and that have clear transferability to other businesses. The first is data strategy.

Ben Shields: We all know that organizations struggle with this notion of, “There’s so much data, we don’t know what to do with it.” And sports certainly have that issue, but to a lesser extent. And what’s very clear about sports is decision makers start with the business problem, not necessarily the data or the methodology or the technology. In other words, sports decision makers are very clear on the problems that they’re trying to solve and, if they solve those problems, how that’s going to relate back to the organizational goal.

Paul Michelman: In sports — more so than in some other industries — the goals are clear, discrete, and easy to identify.

Ben Shields: It’s not a surprise to anyone that an organizational goal for a sports team, generally, is to win a game. And there are lots of different problems that sports teams need to solve in order to win that game. Which lineup should we start? Which offensive and defensive strategy should we employ? Who should come off the bench? There are a whole host of problems that sports teams have to solve on a daily basis. The question is, what information are you going to bring to bear to solve that problem? Well, there’s lots of sources of information. There’s the gut of the coach, there’s the intuition of the player — and, of course, there is data and analytics. And so what we’re seeing with sports is they have a very clear sense of the problems they’re trying to solve, and they are using data analytics as another input to solve those problems to ultimately win more games on the court, the ice, the field.

Paul Michelman: For you skeptics out there: You’re probably thinking, “Yeah, but my business isn’t about winning or losing in that concrete sports sense. The goals aren’t as clear cut, and neither are the problems and challenges.”

Ben Shields: What I try to challenge business leaders to think about is, how can you create similar sets of conditions for competition within your own organization? We know that every business is in a competition in one way, shape, or form. How can you create proxy-type problems that are similar to what a sports organization might deal with? In other words, it’s on you as the leader to create those conditions for competition. And out of that process, hopefully, you can identify clear problems that people can try to solve with a variety of different approaches, data analytics being one of them.

Paul Michelman: Beyond using data strategically, businesses should also be aware of and investing in technical improvements for data capture. If you don’t have the information you need, argues Shields, you need to find new ways to create and capture it. He cites the introduction of player tracking data across all major sports as being instrumental in capturing better information. And one particular data-capture technology has already revolutionized the sport of basketball.

Ben Shields: The sport view cameras that were introduced in the NBA in the late 2000s gave teams much more insight into the most efficient basketball plays — the plays that would gain the most points for an offense anytime down the court. And, of course, what seems like commonplace now wasn’t always the case.

Paul Michelman: Shields says that the 3-point revolution was a direct result of improved data collection and analysis.

Ben Shields: So what the new player tracking technology in the NBA allowed teams to discover is which shots were the most efficient for a team to take. And there was some conventional thinking in the NBA for years that the midrange jump shot — for those of you that are basketball fans, this is sort of the area in between the 3-point line and then right close to the basket — what the analysis of the data revealed as a result of the player tracking was that more-efficient shots were, for instance, the corner 3-pointer. Where it may seem like such an obvious point today, it wasn’t always the case that a 3-point shot was mathematically better than a 2-point shot in like the midrange, even if you made the 3-point shot less frequently than you did the 2-point shot. And while there may have been some basketball minds that intuitively knew that they needed to shoot more 3-pointers, it wasn’t until this comprehensive data set that teams understood the true value of the 3-pointer. And now we can look at data across the NBA, and the number of 3-pointers that have been taken is continuing to rise as a result of this data-driven revolution.

Paul Michelman: Improving technologies are helping to create more useful data in other areas as well.

Ben Shields: Teams are now employing some pretty interesting approaches to sports science to making sure that “hey, we’re investing all of this money into players. Let’s make sure to protect and keep healthy our most important assets.” So for instance, you may have heard of this new term called load management. Load management is this principle of making sure that your players are mentally and physically in their best shape to perform when it matters most. Sticking with the NBA, because we’ve been talking about it, Kawhi Leonard is a great example of load management. He took a number of games off in the 2018-19 season to make sure that when he was in the playoffs, he was healthy enough to perform. The way that they captured data on Kawhi not only ranged from the introduction of wearable technologies but also cutting-edge medical practices to make sure that teams had the best amount of data available to determine when and where Kawhi should play.

Paul Michelman: So you have strategy and you have technical improvements, but there are also implications from a managerial perspective. After all, data means next to nothing when it’s just figures on a spreadsheet or it’s only meaningful to those actually crunching the numbers. It needs to be communicated — and applied.

Ben Shields: One of the unique aspects of sports is that the analytics insights are making their way from the analytics team to the coach and then ultimately to the player, who is the end user. This is important, because if you’re really trying to drive change at your organization, trying to make your organization more data driven, we know that the end users have to be believers in the information that analytics provides. And we’re starting to see that more and more in sports. Now, certainly, there was a time when there was a significant culture clash — Charles Barkley is the poster child for this. Analytics don’t work at all. And there are still some naysayers in the sports world. But by and large, players are starting to see that this information can help them. Now, to be clear, players don’t necessarily need to have a Ph.D. in statistics to make use of the data. And, in fact, sometimes the best coach and player conversations about analytics don’t even use technical terms. It may be that, “Hey, why don’t you try this particular play?” Or it may be, “Why don’t we change your form to this particular form?” Players are understanding the value of data [and] are using it in their own way to make their performance better.

Paul Michelman: The use of data in sports isn’t just coming from the top down but is becoming integrated at every level. And Shields says you can see all kinds of parallels in the business world to this as well.

Ben Shields: Even the everyday runner can pull up Strava and take a look at how their metrics are improving or decreasing over time. And I think we’re getting into this culture now where, whether it’s athletes or even employees at your businesses, [they] are more receptive to using data to improve their own performance. That has been a key driver of the sports analytics revolution and users becoming advocates of the data to improve their performance.

Paul Michelman: That’s MIT Sloan’s Ben Shields. And now, three big points on what sports teaches us about how to use data and analytics:

Number one.

Ben Shields: Start with the business problem, not the data method or technology. This is a human thought process that requires little, if any, technical knowledge.

Paul Michelman: Number two.

Ben Shields: If you don’t have the information you need, you need to create and capture it.

Paul Michelman: And number three.

Ben Shields: Data-driven decision-making is science and art.

Paul Michelman: That’s this week’s Three Big Points. You can find us on Spotify, Apple Podcasts, Google Podcasts, Stitcher, and wherever fine podcasts are streamed. If you’d like to support our show, please post a rating or a review on whatever podcast platform you prefer.

Three Big Points is produced by Mary Dooe. Music by Matt Reed. Marketing and audience development by Desiree Barry. Our coordinating producer is Mackenzie Wise.