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NCAA Four-Factor Impact

Lots of credit here goes to David Hess (aka @AudacityOfHoops) for his work on a simple estimation of how turnover effect efficiency. Check out his pretty blog!

Given the limitations of that formula, I decided to take it a step further: how much does EACH four factor affect a team's offensive performance? Because every time I check out Ken Pomeroy's team four factors I want to better-quantify those green-or-red bits of data.

I've come up with a way to quantify how deviation of the league-mean by each team's four-factors affects their overall offensive efficiency.

The same can easily be done for defense, but for right now, I'm just going to focus on offense:
I took a regression (which myself and David have done before) of the four factors on offensive efficiency. For each team, I took their four factors, save for the one in question, and multiplied them by the regression estimates. I replaced the one in question with the league average. Finally, I took their raw offense and subtracted this number from it. This gives us an estimate of how a team's deviation from the mean affects their overall offense, in terms of the Four Factors.

Here's the great news:
1) I made an Excel spreadsheet so you can easily plug this in for any team without having to scour for them (just enter the team under "Team")
2) I used the same color scheme as Ken Pomeroy's numbers :)
2) I also made a PDF for those who don't want to use Excel.

Editable Excel File

PDF File

1 comment:

  1. This is a great idea, but upon further review:

    A) I don't think you should be subtracting raw efficiency from each of your 4 if-this-factor-were-average estimates. For the best example of why not, look at Tulane. They are almost exactly average in all four factors, but each factor appears to give a lift of ~3 points. That's because the regression equation overestimates their efficiency by 3 points when you just plug in their actual four factor values, and that 3 points is being tacked on to every factor's individual impact.

    B) Given A, I think a better calculation might be to subtract what you label as O2 in the spreadsheet from their estimated efficiency (found by applying the regressions parameters to all four factors). But then, that reduces to simply (using eFG as an example):

    (eFG% - average eFG%) * 1.4



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