Archive for the 'Statgeekery' Category

In case you're a hoops fan that's been living under a rock these past 6 years, 82games is the web destination for NBA game-tracking data, including plus/minus, counterpart stats, and a variety of other incredible numbers that you can't find anywhere else. Today Roland Beech released the 2009-10 stats pages, so head over and just bask in the raw data. Bask in it!!

Hoopnumbers.com has debuted a method of calculating adjusted +/- with the lowest standard errors yet. If you want the gory mathematical details, go here and here first, but if you just want the results, check out the 3-year version and the single-year 2008-09 results... A (very) brief synopsis: Kevin Garnett and Lamar Odom? They're really good. Josh Powell and Antonio Daniels? Er, not so much.

Quick question: What's the probability that the league's "best" team will win the championship this season? Okay, then what are the odds that a team outside the top 5 in talent will win it all? Outside the top 10? The top 15?

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Last November, we used Win Shares to look back at the players who took the floor alongside the most talented offensive players over the course of their careers. Unfortunately, in that article I only looked at offensive teammate quality, and we only had Win Shares going back to the 1973-74 season. Today, though, Justin's efforts have given us WS extending back to every season for which the NBA tracked minutes (through 1951-52, in case you were wondering). So I thought now would be as good a time as any to revisit the topic of teammate quality using offensive, defensive, and total Win Shares since 1952.

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The equivalent of Christmas morning amongst hoops stat-wonks, it's the annual unveiling of John Hollinger's NBA previews over at ESPN. And I'm sure the player comments are not far behind, either... Unfortunately, people who aren't ESPN "Insiders" don't seem to be able to access JH's stuff, which is sad -- but I guess the man has to make a living, just like the rest of us. And for those lucky enough to have Insider, enjoy.

I know we haven't talked about Dean Oliver's Four (Eight?) Factors here in a while, but that hasn't been deliberate. I actually like the 4 factor methodology for evaluating teams' strengths and weaknesses, although there's a quite a gordian knot to deal with when you start trying to link team factors to their respective metrics for individual players. Hmm... maybe that's the reason why I haven't invested so heavily in them recently, because we've been all about trying to establish expectations for teams in 2010 based on their current rosters, and last year's 4 factor data wouldn't help you get very far in that direction. But it occurs to me that another way to look at team trends is to see which stats are historically sustainable from year to year, and which aren't. So, with that in mind, here are year-to-year correlations for each of the 4 factors, on offense and defense, since 1973-74 (the first year the NBA kept turnovers, offensive rebounds, etc. at the team level):

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Last week, we took a very preliminary look at what our Statistical Plus/Minus projection system saw in the cards for the 2009-10 NBA Season. To project minutes played, we used a very simplistic regression equation that took a weighted average of a player's minutes over the past three seasons and regressed it heavily to the mean. Of course, this is a very rough way to estimate what a player's minutes will be next season; in fact, the standard error of the playing time regression (done on all players from 1978-2009) was 674.8, meaning that the prediction was likely to be off by a significant amount in either direction, too high or too low. As you might guess, this could severely impact the accuracy of the projected standings.

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Since everyone seems to be jumping on the projection bandwagon right now, here are the preliminary projected standings for 2010 (I'll explain the process at the bottom):

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Last Friday, I took an initial look at the most "experienced" teams of all-time, settling on the method of averaging the number of career NBA games & minutes played by members of a team's roster as a measure of team experience. There were several problems with this method, however -- first of all, ABA games did not count at all toward a team's experience quotient, so we ended up with a host of late-70s merger refugees appearing as the most inexperienced teams; second, we did not weight by actual minutes played during the season in question, so having an ancient player like, say, Robert Parish on the end of your bench made you look far more "experienced" even if Parish rarely saw the floor.

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Over the past year, I've dabbled a bit in the realm of what I like to call "translating" stats -- that is to say, the process of taking a player's numbers out of one context and plopping them down in another context. Now, this doesn't necessarily mean the usual "what would Player W from Year X have averaged had he switched places with Player Y in Year Z?" strain of time-travel fantasizing, but more like, "given that Player A's averages were worth B wins in Year C, what would have had to average in Year D to create the exact same number of wins?" The difference is a nuance, a shade of meaning, but still very important, because typically we're in the business of making value judgments in the latter sense, and we leave the former to the alternate-history crowd.

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