Steals Don’t Mean Squat

If there’s one thing we know about defensive statistics in the NBA, it’s that steals don’t mean squat. James Harden was second in the league in steals per game last year, and Andre Drummond was eighth. This was not merely an illusion created by the two playing lots of minutes, as both ranked within the top 20 in the league in steals per 36 minutes. In 2016-17, Manu Ginobili and T.J. McConnell were first and second in the league, respectively, in steals per 36 minutes. Steph Curry and Nerlens Noel were both in the top seven in the league in 2015-16, while the same two players along with Pablo Prigioni all ranked in the top eight in the league the previous season. Andray Blatche was eighth in the league in steals per 36 in 2012-13. The illusion extends back as far as steals go in the statistical record.

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The Real Reasons Why Steals Don’t Mean Squat

          These examples are merely anecdotal evidence, though; what really makes steals unreliable indicators of defensive performance are the many different chains of events which can lead to a player being credited with a steal in the box score. Many of those sequences involve plays made by teammates of the player who gets credit for the steal. Any observer can recognize these plays when they happen: tipped passes, saves on balls headed out of bounds, instances in which an on-ball defender pokes the ball away from the dribbler and another defender grabs the loose ball, traps, double teams, errant passes caused by pressure on the ballhandler, etc. Sometimes a steal is the result of a phenomenal play by one defender, but oftentimes a steal is the result of one player disrupting the offense and another player recovering the ball. Steals create an immediate problem of attribution; the player who gets credit for the steal is not always the player who truly created the turnover.

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Matchup-Based Defense

Defense is the unsolvable puzzle in NBA analytics. No matter how advanced the advanced stats get, defensive metrics continue to crash against the same conundrums. Better data often leads to better models, and recent years have seen a dramatic improvement in the quality of defensive data available for analysis. Tracking data, opponent shooting data, play-by-play data, and more have all played a hand in modern defensive analysis. In spite of the improvements, or perhaps in part because of the improvements, it is clear that defensive analysis is still not highly accurate.

Most defensive metrics which are currently extant are based on one of two schools of thought. In order to take stock of why defensive analysis is still frequently inaccurate, it will help to investigate the underlying assumptions behind most current models.

The Plus/Minus School of Thought

The most popular method by far is The Plus/Minus School, which counts BPM, RPM, RAPM, PIPM, and more among its adherents. The distinguishing precept of the Plus/Minus School is the belief that we can ascertain a player’s defensive value by evaluating the team’s performance with him on the court, if only we properly adjust for strength of opponent, the team’s talent level, the team’s performance with the player off the court, and the player’s performance level in seasons past. The adjustments made to raw plus/minus are attempts to extract reliable data by excising confounding variables.

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