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How Data Science and Analytics are Changing Competitive Sports

How Data Science and Analytics are Changing Competitive Sports

The biggest change in competitive professional sports this century has not taken place on the field, but in the front office. Specifically in the use of data science and analytics to win games.

Once, general managers focused primarily on hiring talented players and bringing in coaches who excelled at player management and on-field strategy. And, guess what? That’s all still important. However, the methods used to evaluate those players and coaches have changed. The rise of data science and analytics, along with the ability to measure and utilize patterns found in data research has altered how teams evaluate players and develop on-field strategies.

Data Science and Analytics in Sports

It’s also opened the door to hundreds of jobs for those with a degree in data science and analytics. Sports team owners now realize the same talented people they hire for their businesses are the same people they want in the front offices of their sports teams.

Billy Beane, executive vice president of baseball operations for the Oakland Athletics, wrote in the Wall Street Journal that advanced technology stills needs the right people who know what data to collect and how to interpret it.

This will “dramatically change” the composition and demographics of front offices, which historically have drawn on former players.

“Competing to hire those best equipped to glean insights from the new data regardless of their backgrounds will be a welcome trend in an industry that has actively sought ways to improve its diversity,” says Beane.

Beane and the Oakland A’s

Beane served as general manager for the A’s baseball team in 2002. He already had learned how to apply analytics to evaluating players, but in 2002 he went a step further by hiring statistician Paul Depodesta, a Harvard graduate.

Like many businesses who turn to data science and analytics, the A’s were looking to cut costs while finding ways to maintain a quality product. Beane and the A’s have had their ups and downs, but no one will argue that they haven’t done well overall. Even better, in some cases, than teams that have spent far more money.

The Boston Red Sox and Chicago Cubs have won championships by using many of the same approaches. The primary difference in both cases being that those clubs had more money at their disposal. But both used data-driven strategies that paid off.

Many teams are now following suit. The low-budget Tampa Bay Rays were an early adopter of analytics. The Houston Astros, after hiring an analytics-driven general manager and a NASA engineer, have had a complete rebuild of the team that resulted in four losing seasons. But now the team is a contender. The Chicago White Sox, San Diego Padres, Milwaukee Brewers and Cincinnati Reds have all followed suit, with varying degrees of analytics buy-in.

The Rise of Data Science and Analytics

Data analytics even continues to spread to other sports. The National Football League’s Cleveland Browns hired DePodesta to run the team with an analytics approach. This team is currently in a rebuild phase.

And while no one really knows what the New England Patriots do, owner Robert Kraft is a big proponent of data science and analytics. They’ve had economics expert and former bond trader Ernie Adams on their staff from the beginning of the Bill Belichick era.

What all this means is that someone earning a master’s degree in data analytics might have just as much chance working for a sports franchise than working in finance, healthcare or marketing.

The Use of Data

While data can be used for a variety of purposes, the two main areas are: Finding the true value of a player and making better on-field decisions.

Using baseball as an example, data analytics have led to you seeing very different things when you watch a game. They include:

  • Batters taking more pitches, looking to get pitch-counts up and also waiting for the best possible ball to hit. As explained in the Michael Lewis book “Moneyball,” each at bat is like a hand of black jack, with the odds changing on each pitch.
  • Defenses shifting their positions in the field – sometimes radically – to place themselves where statistics show the batter is most likely to hit the ball
  • Less bunting and stealing, which statistics show help a team a lot less than people might think
  • There are some 20 different data points for every pitch thrown, and this is affecting arm angle, release point and also strategy for where to place pitches, depending on the hitter

Player evaluation is even more complicated. Once measurements such as 60-yard dash speed and batting average were important. Now, it’s numbers like on-base percentage plus slugging percentage and win share (calculating a player’s individual contribution to a win) that are factors.

Beane and others expect data to reach a point where general managers can determine what combination of players work best, rather than just focusing on individual statistics.

All of this requires experts in data science and analytics. Increasingly, those doing the hiring want employees to have advanced degrees, giving them the latest knowledge and skills. And as the NFL and NBA move more into this area, look for the job market to grow even larger.

2017-08-08T14:15:33+00:00
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