Is there an accepted stat that analysts use to measure how consistent a basketball player is?

Take points per game (or points per 48 min): two players with the same average scoring rates could have very different game distributions. Of course standard deviation comes right to mind (after you standardize it to account for high vs low scorers), and it makes sense when I try it.


  • Is another measure even more informative? I've looked at some data and am intrigued by other possible stats, but with only small datasets (and somewhat limited personal impressions of specific player [in]consistency in the NBA this year), the patterns I'm seeing could just be red herrings. :)
  • Even if you use standard deviation, would any adjustments be needed? I'm not sure for example if low scorers would need to be handled differently, or if that all comes out in the wash once you standardize the data. And do you weight right skewed data differently, so it hurts your consistency rating less when you score more (vs less) than your average?
  • What scale is most intuitive to use for the "consistency" measure? If you use standardized SD, for example, would it be %, where 100% is the player who always scores 12 every night? (what a machine!) Is there a scaling that would make comparisons between two realistic players' consistencies more meaningful than 25% vs 35%?

So I wonder if consistency is a "thing of interest" in sports analytics, and if so how pro analysts handle questions such as those.

Thanks in advance for any insights!

  • Per 36 Minutes normalizes production. Chris "Birdman" Andersen typically had respectable Per 36 numbers, but never averaged 36 minutes per game (ostensibly due to lack of stamina). This stat, however, shows things like "12 fouls Per 36" because the player administered two fouls in six minutes of play.
    – user16112
    Jan 28, 2019 at 13:55
  • @user16112 Cool thank you! Makes sense that per 36 would be a key step to the consistency calculation. I take it per 36 is preferred because per 48 is less of a realistic game, though either way it's normalized to enable better comparisons. Jan 29, 2019 at 14:09

1 Answer 1


I initially didn't post an answer because Per 36 isn't widely accepted as a useful stat.

In short, "Per 36 Minutes" normalizes production. Chris "Birdman" Andersen typically had respectable Per 36 numbers (averages a double-double), but never averaged 36 minutes per game (ostensibly due to lack of stamina or being best used sparingly). Beware that this stat also shows things like "12 fouls Per 36" because the player administered two fouls in six minutes of play.

However, Per 36 can be used to convey consistency. Tim Duncan is the poster child for consistency, especially based on Per 36. Here is a graph from 2013 to demonstrate season percent differential from career Per 36 (credit to C-Itz of Flickr):

Season Percent Differential from Career Per 36

As you can see, Tim Duncan is far and away the most consistent player when considering his first ~15 seasons. Compared to Andersen's 17.7 MPG, I am much more comfortable with Duncan's 34 MPG as a more accurate measure of Per 36 consistency.

  • This is a great example of what I’m looking for. It seems from the graph and the Duncan article it came from, they really are calculating a kind of standard deviation. Whether it’s across a season or a career, it makes sense. One question: why is per 36 not widely accepted? Thanks very much!! Mar 28, 2019 at 1:38
  • First, Per 36 doesn't adjust for pace as some teams play faster than others. Next, and the more glaring reason, Per 36 can inflate a player's stats to be comparable to an all-star's stats. There are a myriad of reasons why a player is playing limited minutes or is not mentioned in the same breath as the league's elite.
    – user16112
    Mar 28, 2019 at 12:20
  • Here's a reasonable Per 36 comparison that still maintains the spirit of Per 36. Context is important.
    – user16112
    Mar 28, 2019 at 12:20
  • Awesome examples looking at differences among higher-usage players instead of the Chris Anderson type low-usage cases. Also I had not considered the role of team pace. Thanks again for the insights, @user16112! Mar 29, 2019 at 18:31

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