Like other professional sports NBA analytics are taking shape on the professional basketball court.
Major League Baseball’s last two champions, the Chicago Cubs and the Houston Astros, both are led by front offices with a strong emphasis on using data analytics.
The National Football League has the New England Patriots, who use analytics but play things so close to the vest that no one is exactly sure what they are doing.
The NBA also has gone into analytics in a big way. Almost every team now has an NBA analytics department in the front office. Data is collected using cameras that record every movement of both the ball and all 10 players 25 times per second.
Here are some of the ways teams are leveraging data analytics to become more competitive.
Using NBA Analytics to Rest Players
Teams now pay a great deal more attention to fatigued players in the NBA for a variety of reasons.
One is fairly obvious. Resting a player during a relatively meaningless game late in a season makes more sense than having him take the court in the playoffs without the energy he needs to be at the top of his game.
The other is injuries. Players tend to get hurt more often when they play fatigued, according to the data from thousands of injuries.
At an analytics conference this year, NBA commissioner Adam Silver said teams now have players wear monitor not only during games but during practice to measure, in part, performance and fatigue. They even have saliva sampled as it contains indicators of fatigue. Teams track and quantify a player’s diet.
All of this works toward having a better, healthier player and a better basketball team.
Using NBA Analytics to Pick Players
In the same speech, Silver contrasted what a sports team faces in picking players against what a Fortune 500 company faces in making an important hire for a critical job.
For the company, things work out or they don’t in a fairly accelerated time frame. It doesn’t take years for executives to realize whether a person is right for the job or not. If things don’t work out, the employee is simply let go and another person is hired to take the position.
While NBA teams have some flexibility in filling key roles with free agents, much of a team is built through the draft. A wrong choice in the first round can set a team back years. Teams look for whatever advantage they can when evaluating picks, and analytics play a key role.
Data from college and high school play is analyzed. Performance in matchups against certain types of players is quantified. “The number of analytics fields they’re looking at now, for example when they’re doing college scouting or drafting internationally, is incredible.” Silver said.
NBA Analytics and Scoring
Nothing shows fans what analytics have brought to basketball more than the explosion in attempts for three-point shots. In 2012, teams averaged about 18 three-point attempts per game. In 2017, that number reached 27.
Why? It’s really just common sense and math, backed up by data. Essentially, data showed that the reward of taking a three-point shot outweighed the risk. On average, teams that take more three-point shots ultimately score more points over the course of a game.
The Golden State Warriors, the current champions of the league, are a perfect example of this philosophy.
NBA Analytics and Matchups
Teams also crunch large data sets on defenders for other teams. They determine where they had the most and least amount of success against various offensive attacks, such as long range shots, midrange jumpers and driving the lane to the basket.
Teams then take that information to isolate a player who is good in one area against a defender who isn’t. The flipside is true, as well. Teams attempt to get their defenders on a specific player, particularly in critical situations, if that defender has a statistically better chance of preventing a score.
These are just some of the area where data analytics is changing the NBA in fundamental ways. The revolution is still in its earliest stages, but it has opened up an entire new career path for those who wish to go into analytics.
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