Archive for the 'Statgeekery' Category

In the absence of a runaway choice, there's an ever-growing push among traditional media members in favor of Derrick Rose's MVP candidacy -- and to be totally honest, the advanced boxscore-based stats don't see it. Rose is having a tremendous season, without a doubt, but he's currently 9th in Win Shares, 17th in WS per 48 minutes, 14th in Player Efficiency Rating, and 14th in Statistical +/-... Not exactly the most impressive MVP resume from the stathead's perspective.

However, there is one advanced metric that does validate the love for Rose: Adjusted Plus/Minus (via BasketballValue.com). Sure, the standard errors are huge, and Mike Dunleavy Jr. shows up as the 2nd-best player behind Rose (yikes!). But at least there is some numerical evidence that Rose is making Chicago better in ways that aren't being detected in his box score numbers.

This morning, Zach Lowe of SI.com's must-read Point Forward blog emailed me wondering how Utah's collapse in defensive rebounding % ranks among all-time declines. That got me wondering about the biggest drop-offs in all of the Four Factors, so I ran Z-scores on each team's numbers and looked at the biggest negative changes from one year to the next:

Offensive Effective FG%

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2010-11 NBA power rankings through the games played on January 20, 2011:

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I finally got around to calculating the standard errors for our team Simple Ratings today:

Then I set up a little Monte Carlo sim to estimate what is the probability of each team being the NBA's best (aka the team with the greatest "true" SRS skill). After 10,000 simulations using the estimates and standard errors above, here were the results:

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What was the best run ever for your favorite team? What was the worst stretch of seasons? Let's take a look at the raw numbers in terms of NBA winning percentage over an x-year span (including our regressed 2011 WPcts):

Best & Worst 2 Years

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BBR reader Prashant wrote in with a good question yesterday:

"I just read John Hollinger’s article about the sustained success of the Spurs and Mavs and was wondering if there was any way to calculate the average deviation of a given team’s record over time? Basically, which teams are the most consistently good/bad/average over a set timeframe, say a decade? I would imagine the Spurs/Mavs/Clippers are atop that list, while the Celtics and Heat probably have a pretty wild deviation (from lottery team to title contender)."

Sure, the easiest way to look at this is to calculate the standard deviation of each franchise's year-to-year winning percentages over the given timeframe.

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2010-11 NBA power rankings through the games played on January 13, 2011:

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As a follow-up to their post about minute-weighted team age, Hoopism took the advice of our commenters and re-ran team ages, this time weighted by Win Shares:

Mapping Average Age to Success in the NBA

Comparing side-by-side with the raw roster averages, this has the effect of allowing you to see which teams' most productive players skew especially young (Miami, Orlando, LA Clippers) or old (Phoenix, Boston, Houston).

2010-11 NBA power rankings through the games played on January 6, 2011:

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I was reading Brian Burke's excellent Advanced NFL Stats site when I came across this post about predicting future team rushing efficiency (expected points per rushing play). Because a handful of big, somewhat unpredictable rushing plays can have such an outsized impact on overall efficiency, Burke found that past success rate -- simply the percent of plays that had positive expected point values, regardless of their magnitude -- was actually a better predictor of out-of-sample rushing efficiency than past efficiency was.

In basketball, we have two similar (though not totally analogous) metrics: Offensive Rating (average points scored per possession) and Floor% (the probability of scoring at least one point on a given possession). Offensive Rating gets all the publicity, and as well it should -- the entire goal of an offense is to maximize points per possession. However, ORtg can also be heavily impacted by 3-point shooting, so boom-and-bust offenses that over-rely on threes might be like those teams whose running backs bust off a handful of long runs but otherwise get stuffed at the line too often. Their overall efficiency might be good, but their success rate isn't, and in the end success rate is what you can count on going forward.

With that thought in mind, I'm going to replicate Burke's study, hoops-style. The NBA's rapidly-increasing obsession with 3-point shooting finally leveled off from 2008-10, so my sample will include every game from those seasons. For those games, I calculated each team's offensive/defensive rating and floor%; I then broke their seasons up into even- and odd-numbered halves based on the order of games in the year, as well as 1st & 2nd halves of the schedule. Finally, I ran the correlation between ORtg/DRtg or offensive/defensive Floor% in a given half and ORtg/DRtg in the opposite half. Here were the results:

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