Archive for the 'Statgeekery' Category

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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In response to Tuesday's post about Win Shares and aging, reader Jason J thought about whether or not today's improved equipment (including much better shoes), training methods, and dietary regiments made it easier for older players to stick around in the NBA for longer periods of time. It's a great question, because the success of recent players like John Stockton, Karl Malone, Michael Jordan, Reggie Miller, Dikembe Mutombo, etc. well into their forties makes you wonder if modern basketball technology is helping older players extend their careers longer than ever.

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...AKA part one of what I'm sure will be a very long series before we've said all we want to on the subject.

Now I'm preparing you guys ahead of time, this is mostly a data/graph dump, and there's a lot of selection bias going on here (then again, I challenge you to find an aging study where there isn't selection bias). But using a sample of all player-seasons since 1978 with >2000 MP, here are the numbers on how a player's age affects his rate of Win Shares per 3000 minutes. The first focus will be every player in the sample, and the average change in their WS3K by age:

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I just realized I've been derelict in my linking duties recently, because I haven't thrown any love to the Basketball Geek, Mr. Ryan Parker, for some of his posts this week on multilevel modeling. Basically, MLM is a type of regression technique that you'd use in real-world situations where contextual effects occur on several levels (hence the name) and make it difficult to assume that the errors for each coefficient are uncorrelated. And basketball, as we know too well, is a game where performance is often heavily context-driven, so MLM is certainly a method that deserves more investigation as APBRmetrics becomes more and more sophisticated. This past week, Ryan used this type of random-effects model to predict 3-point shooting ability and offensive rebounding ability based on age and past performance. Essentially it's a really fancy way of regressing to the mean, but this method also has the potential to do a lot more than that because you can theoretically control for some of those pesky contextual effects that we analysts often run into when trying to unravel a game as complex as basketball.

The 2008-09 NBA season is finally getting started, and just about every fan and media member is concerned with what we can expect from each team. So here's a question: from a strictly statistical perspective, which teams in NBA history most exceeded their preseason expectations? By the same token, which teams were the most disappointing in light of their expectations?

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