Analytics key to modern NBA success

Several weeks ago, basketball legend Charles Barkley decided to make it his mission to attack the growing emphasis on sports analytics in the modern NBA. At one point during his rant on live television, Barkley called out the personal qualities of the individuals who work with sports statistics saying: “Analytics don’t work at all. It’s just some crap that people who were really smart made up to try to get in the game because they had no talent. Because they had no talent to be able to play, so smart guys wanted to fit in, so they made up a term called analytics. Analytics don’t work” (

While sports analytics sounds like a fancy term, it is really just the work done by modern sports statisticians when they analyze game data beyond the box score. For decades, coaches, fans, players, and the media have looked at the data from box scores when evaluating players and teams.

In the case of basketball, a good player might average over twenty points, five rebounds, and four assists, while shooting above eighty percent from the free throw line, thirty-eight percent from three-point territory, and forty-four percent overall. When determining which players he should try to acquire, a team’s general manager might look at that stat line and decide that this is a player he should try to add to his team.

The problem with this simple analysis, is that it fails to account for other variables that might affect these numbers. While there are many different types of advanced statistical measures, perhaps the most widely recognized is player efficiency rating or PER. The process of calculating PER is quite complicated, and I will not attempt to explain it since I do not even understand it myself.

It suffices to say that PER is a measure of per-minute production for NBA players which is standardized so that league average is always fifteen. The actual numbers involved are somewhat arbitrary, but the resulting data can be used to describe how efficient a player is.

There are players who put up good box score numbers, yet are below the league average for PER. Various factors might account for this scenario including the percentage of offensive possessions that end with that player taking a shot, or the amount of points that player allows his opponent to score.

A perfect example of why sports analytics are useful is Dion Waiters of the Oklahoma City Thunder. By taking a quick glance at Waiters’s stats this season, you might decide that he is quite a talented player since he averaged about twelve points, two assists and two and half rebounds per game. Yet Waiters’s PER is only 10.93, which is over four points below the league average.

So while Waiters may put up decent box score numbers, he is an inefficient part of the Thunder. On the other hand, a player such as Shabazz Muhammad on the Minnesota Timberwolves averaged thirteen and half points, about one assist and four rebounds per game this past season. While his numbers are similar to those of Dion Waiters, Muhammad has a PER of 19.99, which is about five points above the league average. So a team that is looking to maximize its efficiency, would rather have Shabazz Muhammad, than the relatively inefficient Dion Waiters.

When Charles Barkley criticized the use of advanced stats in basketball he is being ridiculous. All NBA teams utilize analytics in some way, and the most successful teams tend to be the ones with the best analytics departments as well. During his on-air rant, Barkley tried to suggest that some of the best teams in the NBA do not rely on analytics when analyzing player talent: “What analytics did the Miami Heat have? What analytics did the Chicago Bulls have? What analytics do the Spurs have? They have the best players, coaching staffs who make players better.

Like I say, the Rockets sucked for a long time. So, they went out and paid James Harden a lot of money. Then they went out and got Dwight Howard, they got better. They had Chandler Parsons, this year they got Ariza. The NBA is about talent” (

Yes Mr. Barkley, the NBA is all about talent, yet the franchises who are best at evaluating talent are also the ones with the strongest analytics departments. The Miami Heat, San Antonio Spurs, and Chicago Bulls all use advanced stats to make personnel related decisions.

Almost all of the Spurs’ starting lineup have PERs above the league average; 16.5 for Danny Green, 22.6 for Tim Duncan, 22.0 for Kawhi Leonard, and 16.2 for Manu Ginobili. Consistently successful teams such as the Spurs rely on various advanced statistical measures when determining which players to acquire. The Spurs obtain many of the players from overseas leagues where there is less information available. Yet despite what Charles Barkley says, the Spurs are so good at finding these players because they are expert at evaluating them.

Perhaps the most ridiculous point in Barkley’s criticism is when he suggests that analytics did not factor in to Houston’s decision to acquire James Harden and Dwight Howard. The Rocket’s general manager Daryl Morey is famous for his use of analytics. Data over the last decade has suggested that the most efficient NBA teams utilize a pace-and-space style offense that relies on three-point shooting, and playing inside to players can get fouled and take free-throws.

James Harden is the model of player efficiency with an incredible PER of 26.76. Teams value Harden because he is a great shooter, and he gets sent to the foul line at an absurd rate. Harden is highly efficient according to advanced stats, and that is why Houston was able to acquire him from Oklahoma City back in 2012 since the Thunder undervalued Harden’s value. Charles Barkley is simply wrong, all teams now rely on analytics, and they will continue to do so into the future.

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