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!