Understanding NBA Advanced Stats
Part 1: True Shooting %
The art of advanced statistics in sports was revealed, at least to the general public, following the publication of Michael Lewis’s Moneyball: The Art of Winning an Unfair Game in 2003, and then subsequently revealed to an even larger audience when Brad Pitt and Jonah Hill starred in the movie version of the book in 2011. Moneyball told the story of how the Oakland A’s managed to win every year with one of the lowest payrolls in the MLB, despite the league being seemingly skewed in favor of teams with the ability to spend the most money, like the New York Yankees. Billy Beane, Oakland’s GM, and his staff, mainly graduates of prestigious universities with degrees in advanced mathematics and statistics, instead of the traditional staff composed of baseball veterans, discovered that statistics like On Base Percentage and Slugging Percentage were better indicators of offensive success than the traditionally tracked stats such as Batting Average and Home Run Count. Their innovative strategy allowed the 2002 A’s, who had a payroll of $44 million, to win 103 games, the same amount as the New York Yankees, a team with a payroll of approximately $125 million.
Since then, many other statisticians, like John Hollinger, have gained prominence for their analysis of baseball and basketball. In 2009, Michael Lewis profiled Shane Battier and the Houston Rockets in a piece titled “The No-Stats All-Star,” in it he described the highly analytical culture governing the Rockets’ strategy (which included a nice cameo by Sam Hinkie, then an executive in the Rockets’ front office). Lewis detailed the complex analysis that Houston conducted before each game and how they used it to generate wins each night. The piece piqued my interest in how advanced stats are currently shaping the NBA and inspired me to write this piece.
Like many others, I have watched and played basketball my whole life but I have never taken the time to learn and understand the advanced metrics that are shaping the way today’s game is played. This “column” will serve as both my own attempt to understand these concepts and explain what they mean and how they are used to the general public. The first statistic I will study is “True Shooting Percentage.” (Thanks to Bleacher Report and Basketball-Reference for 95% of this information)
What is True Shooting Percentage?
The key to understanding TS%, and in some ways the modern NBA, is understanding that some shots are better than others. Seems simple right? Statistically speaking, the most efficient shots in basketball are layups, three-pointers and free throws, but regular FG% statistics weight all shots equally while ignoring free throws entirely. The formula for TS% is:
TS%= 100 x (PTS/2(FGA+.44*FTA))
- PTS= Total Points Scored
- FGA= Field Goals Attempted
- FTA= Free Throws Attempted
- True Shooting Attempts (TSA)= (FGA + .44*FTA)
TS% is not merely a calculation of how many shots a player makes as compared to how many shots he takes, it measures the efficiency of the player’s scoring via how many shots it takes them to reach a certain amount of points. How? Well, let’s compare two players, one a wing and one big, from last season with similar point totals and see how this plays out, Klay Thompson (1771 PTS) and DeMarcus Cousins (1748 PTS). Last season Klay took 1386 shots (shooting 47% from the floor) and 221 FTs (shooting 87.3 percent), giving him 1483.4 TSA. Boogie, on the other hand, took 1332 shots ( shooting 45.1%) and 663 FTs (which he hit and a 71.8% clip), which adds up to 1623.72 TSA. Do the math out and you’ll find that Klay’s TS% was 59.7% while Boogie’s was 53.8%.
By looking only at FG%, you’d conclude that either Klay was a superhuman shooter last year or Boogie was just below average, or maybe both. But examining TS% gives you a clearer image of what’s going on. Klay took more shots than Boogie did, but Boogie’s 442 extra free throws (and the 283 points that he got from them) made up for that discrepancy.
Why is it useful? Traditional FG% favors big men because they tend to only take high percentage shots near the rim so they will obviously make more of those shots than a wing player who shoots from all over the court, it also fails to account for players who score a high percentage of their points from the free throw line. If I’m trying to find the players that will outscore my opponents on the least amount of shots then I want the players with the highest TS%. This is kind of how the Rockets run their team. They’ve assembled the players that will score the most points on the least amount of shot attempts and turned James Harden into an efficient monster. So, in effect, TS% is much more helpful when I’m comparing players that play different positions because it attempts to control for the different types of shots that players take and the positions they play.
Still, someone judging only by TS% will usually end up discriminating against players who carry a major individual load for their team, a player like Russell Westbrook. As a player’s Usage Rate (another cool stat that we’ll examine next, one that measures how many offensive plays a player is involved in while he’s in the game) rises This season, Westbrook’s TS% is 54.1%, an incredible feat considering the fact that he’s nearly single-handedly keeping the Thunder afloat in the West. On the other hand, James Harden, the other player in the MVP conversation, has a TS% of 61.6% this year, probably due to the fact that he has more help on offense than Westbrook and thus isn’t forced to take as many shots that aren’t very efficient.
Overall, TS% is a great start to understanding basketball’s new advanced stats movement. Soon we’ll examine some other new ways of breaking down the game that we’ve all come to know and love.
