By Jon Nichols
Today I’m going to take a different approach to evaluating coaches. One suggested theory of evaluating head coaches is to look at how their teams outperform their expected (Pythagorean) wins. The thinking goes that great coaches consistently excel in late-game situations and often win more than random chance would allow. You could also make the argument that great coaches optimize the way in which they use their players, another reason they outperform their expected win totals.
What does the data say? I rounded up each team’s actual and expected wins over the last seven years and calculated the averages for each coach. You can find the data here:
http://spreadsheets.google.com/ccc?key=rI4SSKbrDqddJZcy0IBJ50w
As you can see, those theories may be wrong. The results appear to be random, at least when you factor in common beliefs about who’s a good coach and who’s not.
Stay tuned, as there is much more research on coaches to come.
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How Do NCAA Statistics Translate to the NBA?
By Jon Nichols
As March Madness begins and the NBA Draft approaches, I often wonder how close the college game is to the professional one. It’s clear who the stars in the college game are. But are they just “built” for that style of play, or are they true stars who excel at any level (including the NBA)?
I have attempted to solve this problem by seeing how college stats correlate to NBA stats. To do this, I first took a large sample size of current NBA players’ career statistics and compared it with those sample players’ college stats. Everything was calculated on a per-minute basis. Once I had the stats, I ran a series of simple regressions to see how well the NBA numbers correlated with the college ones.
Below I have posted the R^2 values of the different correlations. R^2 basically says how well future outcomes are likely to be predicted by the model and can be thought of as a percentage. For example, if the R^2 of the correlation between college points per game and NBA points per game is 0.3405, then we can say that about 34.05% of NBA players’ PPG can be explained by their college PPG. The higher the R^2, the better.
Below are the R^2’s for the different correlations:
Points per minute: 0.3405
Field goal attempts per minute: 0.3522
Field goal percentage: 0.3436
Three-point attempts per minute: 0.6391
Three-point percentage: 0.7941
Free throw attempts per minute: 0.286
Free throw percentage: 0.7615
Rebounds per minute: 0.8312
Assists per minute: 0.8823
Steals per minute: 0.5981
Blocks per minute: 0.9327
Turnovers per minute: 0.4535
Personal fouls per minute: 0.4447
Those numbers are all higher than I expected before I began the study. Specifically, we can predict with pretty good certainty an NBA player’s blocks, assists, rebounds, three-point percentage, and free throw percentage based on their equivalent college statistics. Free throw attempts, points per game, field goal percentage, and field goal attempts are the weakest.
This all comes with one big caveat. The sample only includes guys that have made it in the NBA. The college stars that fizzled out at the pro level or the guys who NBA teams knew had no chance at the highest level before the draft were not included in this study. In other words, just because a guy is great in college doesn’t mean he will be great in the NBA. However, if he does make the NBA, we can somewhat predict how he’ll end up doing based on his college stats.
This is just the beginning of my study, though. I have developed a model for predicting a player’s NBA stats using multiple variables at a time. As it turns out, even things like NBA assists can be predicted using more than just college assist numbers.
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