NBA Players in their prime

It has been suggested that I look at players’ statistics from only the primes of their careers. This is a good idea, given that both very inexperienced and very old players will “regress to the mean” in terms of their performance and possibly, playing style. As such, I generated a sum of each player’s boxscore statistics during the modern area across only their best seasons. My definition of “best” was simple: not their worst. For each player, I found their mean seasonal winshr, as well as their winshr standard deviations. Any seasons for which a player’s winshr was greater than the mean less one standard deviation was included in this analysis. This way, I excluded seasons in which a player was injured or relatively underused because of age or because of a minor role on their team. Chris Webber’s current and previous seasons, for example, would not be included. In this way, I hope to get at the “pure” essence of each player for an even better comparison. You will probably not be surprised to see that the diagram looks very similar to the non-peak-performance versions:

nbaprimethumb.png  NBA players at peak performance [pdf]

 A few interesting things to note, however: at their peak, Michael Jordan and Larry Bird are now among each others’ closest matches. Also, taking a macro view of the whole network, it is now easy to identify several different nodes: In bluish purple at top left, we can see defensive-minded, “dirty work” bigs, while at the bottom in blue are more scoring bigs. To their right is a reddish group of primarily scorers, while going north from there in green we see “pure point guards” and then more scoring point guards. Etc, etc. Let me know if you notice any other interesting connections or clusters in the comments.


          One response to “NBA Players in their prime

          1. I wandered over from APBR to thank you again for the interesting analyses and graphs. If your not sick of us asking you questions about your stuff, I had a couple more ideas that may help over in APBR.

            I know you’re torn over the ratios vs. per minute stuff, so I think I may have a solutions that would not compromise:

            -taking into account usage in per minute stats that ratios can’t address, and

            -indirectly addressing the pace that was incorporated in the ratios to some extent by looking at a stat like pts/min and adjusting to pts/min relative to league avg pts/min

            while also taking into account league differences on a year-to-year basis.

            Each iteration of this you’ve put together is better and better. Thanks again for all of the good work!

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