Archive for the 'Statgeekery' Category

2010-11 NBA power rankings through the games played on December 9, 2010:

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I was messing around with the database this morning, and I decided to check out the 2010-11 leaders in the percentage of team points produced by a player while he's on the court:

No doubt that this has been a good season so far for Kobe, but if he's producing 34% of L.A.'s points at age 32 on a team with Pau Gasol, Lamar Odom, Ron Artest, & co., what must his % have been around 2006 or so, when he was lining up with guys like Smush Parker and Brian Cook?

Curious, I dialed up the post-1977 single-season leaders in this stat:

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Facing his former team in Cleveland last night, LeBron James played like his vintage Cavs-era self, pouring in 38 points on 15-25 shooting with 8 assists in 30 minutes. That had me wondering, where did James' performance rank among historical instances of a player facing his ex-'mates the season after he left town?

So I ran a database query on all games since 1987 where a player was facing a team he had played for the previous season. And for the performance metric of choice, I want to introduce an adjusted plus/minus-based game score-style metric called "APMVAL". I basically ran an intercept-free regression between APM value over replacement (minutes * (APM + 6)) and raw box score totals, coming up with this formula:

APMVAL = 45*pts - 35*tsa + 18*reb + 30*ast + 72*stl + 41*blk - 75*tov - 39*pf

The best APMVAL game of the overall 1987-2010 period was Michael Jordan's 69-point outburst vs. Cleveland in 1990 (Kobe's 81-point game was 2nd). Here were the best games against a player's former team in the first season after his departure:

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2010-11 NBA power rankings through the games played on December 2, 2010:

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This post is a follow-up to this morning's piece about LeBron James & Dwyane Wade's current slumps, so should probably read that one first, if you haven't yet.

In response to the hand-wringing about Wade & James' sub-standard production thus far, some have suggested it's merely a pair of slumps that just happened to coincide with the duo joining forces in South Beach. How legitimate is this theory? Well, thanks to the magic of Monte Carlo simulation, I can test exactly how likely that explanation actually is.

Specifically, I'm going to simulate 10,000 18-game samples based on the career distribution of James & Wade's Hollinger Game Scores. (Yes, there are countless other, better metrics, but hey, this is a quick-n-dirty study.)

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Because the much-hyped 2011 Miami Heat have had such a bizarre season so far (they apparently have a knack for whipping bad teams but losing close games to good opponents), we've had to think harder about the nature of blowouts. For instance, are they really very predictive if they come against weak opponents?

Adapting an old Football Outsiders study to the NBA, I found that the answer is actually 'yes'. I also found that a team ranking system which gives a lot of weight to blowouts is more predictive than one which places less emphasis on lopsided games.

Now BBR reader "Anon x 2" asks another question:

"What if being blown out means a lot more than blowing someone out?"

Let's take a look using roughly the same methodology as the "Guts and Stomps" article.

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2010-11 NBA power rankings through the games played on November 25, 2010:

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Here's a follow up to a post I made two years ago regarding the best Thanksgiving Day performances since 1987. I only looked at raw totals then, but this time around I think we should nerd it up with an overflowing cornucopia of advanced stats. Yum!

Hollinger Game Score

The most basic metric you might consider "advanced" is John Hollinger's Game Score. It's a simple linear weights formula based on his infinitely more complex Player Efficiency Rating, but the weights are a step up from something like TENDEX. Here are the best Thanksgiving games by game score since 1987:

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This layup comes courtesy of Josh Levin and the live edition of the Hang Up & Listen Podcast...

While he was at MIT in 2005, Dr. Walter Sun (who -- random fact -- did his undergrad at my alma mater) published a paper about optimal fouling strategy in college basketball. He found that intentionally fouling the opponent's worst foul shooter for the 7th, 8th, and 9th fouls of a half (when the other team is in the 1-and-1 bonus) could increase your PPG margin by as much as 2 points. And this isn't just when fouling in desperation situations, either; the paper calls for you to intentionally hack the worst FT shooter no matter when you get to six fouls.

Although, as Josh points out, Dr. Sun wasn't the first person to think of this strategy. In the early 1980s, NC State coach Jim Valvano also used the intentional foul to his advantage. In fact, the NCAA instituted the 2-shot rule specifically because Jimmy V. was essentially using Dr. Sun's strategy on every foul he could (since there wasn't a double bonus in those days).

Two weeks ago, I looked at the players who were under- and over-achieving the most so far this season. Today, I want to take the same concept and apply it to teams -- given the projected SPM performance levels of their players and the distribution of minutes to those players, which teams are currently playing better or worse than we would have expected?

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