I was curious about which seasons saw the most Hall of Famers in action, so I set up a query to count how many HOFers (inducted as players) were active in a given season, both in the NBA and the NBA/ABA/BAA combined:

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

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This week in the New York Times: Blake Griffin, Super Rookie.

Keeping Score: Blake Griffin’s Big Impact

Look for it in tomorrow's print edition as well.

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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Inspired by yesterday's quiz about the 1990s All-NBA Teams, here are the Win Shares leaders for each decade (made possible by the handy BBR Player Season Finder):

The Fifties (1952-59):

All-Decade WS team: G Bill Sharman (67.6) - G Bob Cousy (60.5) - F Dolph Schayes (95.2) - F Paul Arizin (69.8) - C Neil Johnston (92.0)

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From Sporcle, here's a quiz for everyone who fondly remembers the NBA of the nineties:

Can you name the players who made the All-NBA team in the 1990's?

Among the players in their most common lineup, which teams divvy up possession usage most efficiently?

To answer that question, let's use a method I introduced here. Just like that old post, this one is going to lean heavily on the concept of "skill curves", which says that a player's offensive efficiency drops as he shoulders more and more of a team's possessions. I realize this isn't always the case for all players -- but as a very general rule it holds, so let's pretend for a moment that this simple model does in fact explain the fundamental usage-efficiency tradeoff in basketball. Under those rules, a player using 18% (or fewer) of team possessions while on the court would see his efficiency change by 1.65 points of offensive rating for every 1% change in usage, a player using 18-23% would see a change of 1.24 pts of ORtg for every 1% of usage change, and a player using 23% or more would see a change of 0.82 pts per 1% change in usage.

To find every team's most common lineup, I gathered data from 82games.com, and scaled the sum of those players' season-long possession usages to equal 100%. I found their predicted lineup efficiency based on actual ORtgs and usage patterns, and also found the optimal distribution of possessions that would maximize offensive efficiency according to the rules above. The teams with the smallest difference between their actual usage pattern and the optimal pattern can be considered to be efficiently allocating their possessions.

Here are the teams, sorted by the squared difference between their actual and optimal usage patterns:

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Here's what you get when you run the BBR Rankings (and our old friend Maximum Likelihood) on every game, regular-season & playoff, that took place in the calendar year of 2010:

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2010-11 NBA power rankings through the games played on December 30, 2010:

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Just saw this tweet from Darren Hoyt:

"the website for the movie Space Jam hasn't been updated since 1996 - http://j.mp/hMPQqG"

I'm so loving the 90s-era design of that page. Especially the pressbox page:

"No Spacejam news at the moment!"

As Michael Kerney tweeted, "If there's no Space Jam news now in the pressbox link, I'm pretty sure there's not gonna be."