Analytics and Its Impact on the Sports Industry

Analytics and Its Impact on the Sports Industry

Ever wondered why your favorite basketball team loses season after season? Or why other teams perform well?

Height plays a huge role in the NBA — the average NBA player is 6 feet 6 inches tall, and the average male in the United States is about 5 feet 10 inches. With each inch that you grow, your chances of making it to the NBA double.

Team management and player talent also play a large role in the success of a team — but the statistical analysis that happens behind the scenes to find the best solutions to produce a winning team is an integral part of a successful season.

Daniel McIntosh is an associate teaching professor at the W. P. Carey School of Business and the faculty director of the sports business undergraduate program offered on campus at Arizona State University and through ASU Online.

With expertise in basketball and football analytics, as well as sports revenue generation and economic impacts of events, McIntosh has assisted with economic impact studies for the Super Bowl, the NCAA Final Four, the College Football Playoff National Championship, the Fiesta Bowl, the Cactus Bowl and the Waste Management Phoenix Open.

And while most people might be listening to the radio, looking at a website or watching a sportscast for reference, they have a limited picture of sports analytics. McIntosh is looking at sports data on a large scale.

“Sports analytics has changed the NBA in a lot of different ways,” he said. “We’ve got access to things like player-tracking datasets. We can apply that data to interesting problems like optimal team construction — which players should we draft? Which free agents should we sign? Can we limit injuries and maximize the players we currently have?”

For individual player projections, he can see how a player performed in past years, and then try to forecast how they’re going to perform in the future.

“We could think about the types of shots we’re taking and the strategies that we’re using at the end of the game,” McIntosh said.

More prominently, the data accessible to McIntosh can help him analyze the way the game itself is played, from the shots they should be taking to the strategies they use at the end of a game.

“All of these things factor into how we optimize or maximize our chance at winning,” he said. “That, in its essence, is an application of sports analytics.”

In the middle of NBA playoff season, we spoke to McIntosh about how sports analytics has shaped the game of basketball.

Note: Answers have been edited for length and clarity.

Question: How has sports analytics changed the way that we construct teams? 

Answer: If we start to think about the foundations of sports analytics and how we construct teams, it starts with looking for efficiencies.

Specifically, small-market teams were looking at how they could compete with larger-market teams. It was how much money can we spend on the best players. Well, if you were a small-market team you’re at a resource disadvantage, so they had to find ways to identify talent more effectively, given their different budget structures. That was the premise of “Moneyball.”

They started to look at what variables are most predictive of future success. They could use statistical analysis and data analytics to create these competitive advantages when they looked for players that they wanted to sign. What they were doing in baseball has now taken hold in every major sport, including the NBA.

Q: How have recruitment strategies changed over time? 

A: If we want to think about how recruitment strategies have evolved over time, specifically we could think about things like player fit — how well do they fit into the strategies we’re going to implement?

For example, you could think about the NBA and if your team likes to take corner threes, you might look to identify players who are demonstrating that skill set but haven’t been easily recognized, so we look at individual players: How would they fit into the structure of that team? Is there an underlying metric that would help us determine if they’re capable of performing at a higher level than they currently are?

That’s where you can find some of those inefficiencies. Everybody can see how many points you scored in an individual game, but the really important question is how many points you’re going to score in the next game and future games.

Q: So, is there a “special sauce” to creating a team?

A: Each team has its own kind of bent on this and what they look at. It might be how well they’re performing on a statistical test, say an IQ test, or an information-processing test like the recently popularized S2 cognition test, but they’re also doing physical skill tests. They’re looking at heights, weights and vertical jumps.

One of my favorites has to do with hand size and the predictive power that hand size has for securing a rebound. The point being, they’re looking at every part of the athlete to see what little piece of information will help them more effectively project how that player will perform in the future.

Q: Can we look into those things a little deeper because of new technologies?

A: If we look at how technology has helped us take a deeper dive, we can start to think about player tracking: How many of the shots were catch-and-shoot? How many of the shots were contested? How many of the shots were closely contested? How far and how fast did a player run? What was his load and intensity of movement prior to the shot?

We can put a difficulty score on each and every shot that the player is taking. This is a pretty substantial improvement from just a count of how many did they take and how many did they make, the traditional field-goal-percentage metric. We can estimate and adjust for taking very difficult shots and the rate at which the players are successfully hitting those shots.

Part of my role was looking at what metrics can help us more effectively predict how a player is going to perform. I presented a paper on what we called the universal shooting percentage, and this is where we wanted to estimate who were truly the best shooters.

As statisticians and analysts, we have to find what metrics best correlate to the abilities that we want. That’s where new statistics — effective field goal percentage, true shooting percentage or universal shooting percentage — attempt to better estimate the underlying skill set of the best shooter.

Q: Can analytics result in higher-scoring games?

A: Using analytics to result in higher-scoring games comes down to maximizing points per possession. If we come through and we say, “Hey, everybody’s going to get 100 possessions,” the question becomes, what are you going to do with them? How are you going to maximize your opportunity to score?

We have seen that through increased layups and increased attempts to draw fouls — so that you can attempt shots when the clock’s not moving — and then, obviously, through the addition of three-pointers, trying to maximize how many points you generate per each shot. But, we’re also seeing changes in terms of how quickly those shots are happening. We have seen an increase not only in the spacing of the floor but also an increase of the pace at which the game’s being played, understanding that if you can attack a basketball team before the defense is set up properly, you have a much higher probability of converting and scoring. We’re seeing lots of changes, both on offense and defense, that are a direct result of data analytics.

Q: How has the game of basketball changed because of this?

A: We can see a lot about the evolution of basketball due to sports analytics. If you look at just the most popular shots that were taken 30 years ago, 20 years ago, 10 years ago and today, the shots that are taken are radically different, and most fans would identify that the mid-range game is completely gone. It’s been replaced by corner threes, it’s been replaced by free throws and it’s been replaced by layups. All of that is directly related to statistical analysis. It’s about finding what is the most efficient shot.

If you think about it, why would you take an 18-, 19- or 20-foot shot and only get two points, when if you step back an extra foot, you get a full point? You get a 50% bonus, and when we look at the efficiency of that shot, it skyrockets. Those were the types of early statistical analyses that we were doing to see which shots were most valuable, and it directly translated to what you’re now seeing on the court and how the game is being played.