Archive for the 'Statgeekery' Category

Back in June, right before the Finals tipped off, I developed a method to estimate possessions for teams going back to 1951 using the following regression equation:

Possessions ~ -4.05*Wins - 3.96*Losses + 0.97*FG + 0.75*FGA + 0.70*FTA - 1.37*OReb + 0.53*TotReb + 0.31*Fouls - 0.50*Points +0.19*Opp. Pts

For most teams, this method can estimate a team's actual possessions total within roughly one possession per game, so it's surprisingly accurate given the basic nature of the inputs.

At any rate, I went on to use this method in finding the most similar NBA Finals matchups to the Lakers/Celtics clash, as well as in determining the Finalists that improved the most during the playoffs, and ranking playoff defensive performances. Today, though, I want to use estimated offensive ratings as a way to rank the players who have played for the best offenses during their careers.

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Last October, Jason Lisk published two great studies at PFR about which passing stats are the most situation-independent, looking at the year-to-year correlations in rate stats for both QBs that changed teams & teams that changed QBs. (Chase Stuart followed this up with additional interception rate research in March.) His conclusions? Sacks per dropback and completion % were the most consistent, which implies that those are more under the control of the individual QB rather than the situation he's in. At the other end of the spectrum, interception % is actually the least consistent rate stat, indicating that interceptions (or lack thereof) are more due to luck and situation than actual player skill (a finding Chase reinforces in this Footballguys article). This also means that when evaluating QBs, we should regress their interception rates more to the mean than their sack or completion rates.

What does all of this have to do with basketball? Well, I decided to do the exact same study for NBA players, except instead of looking at players who changed teams, I looked for players whose offensive roles changed (as measured by possession usage %). I can certainly look at players who changed teams as well, but for basketball my hypothesis is that the player's role is as important as anything in determining certain rate stats. This goes back to the concept of "skill curves", or the idea that a player's efficiency is fundamentally a function of not only his own skill, but also his usage rate.

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There are a lot of attributes that I've looked into as the hallmarks of great teams, including dominant wins, ideal usage allocations, and superior playoff point differentials. But here's another characteristic to throw onto that heap -- season-long performance vs. playoff teams.

Since the playoffs only feature the league's best teams -- a.k.a. those through which the path to a championship runs -- you could argue that we should judge a good team's ability by its performance vs. fellow postseason participants. Or at least that's the premise here: for every season since 2000, I whittled down the NBA schedule (regular-season and playoffs) to just include games between 2 playoff teams. Then I ran the Simple Rating System formula on those games, adjusting for a home-court advantage of 3.3 PPG and setting the results relative to the overall league average of 0.0 (to keep things on the familiar SRS scale).

The results are the teams that performed the best vs. playoff teams during the year in question:

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If you missed Monday's post, I encourage you to go back and check it out -- I looked at player performance in 2009-10 (regular-season + playoffs) against above-average and below-average defenses to see if certain players thrived vs. weak defenses and/or wilted against strong ones. Today, I'm going to break it down even further by looking at performances against top-/bottom-10 and top-/bottom-5 defensive teams.

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See also: Part II

Last March, I wrote a piece that compared LeBron James, Kobe Bryant, and Dwyane Wade's 2008-09 advanced stats against four groups of defenses: all 30 NBA teams, the top 15 in Defensive Efficiency, the top 10 in D.E., and the top 5 in D.E., to see if certain players thrived vs. weak defenses and/or wilted against strong ones. The results? James was the league's overall best against all teams, but his efficiency took a hit as the D got tougher. At the same time, Bryant was more immune to tougher defenses than James, and Wade was actually better than either Kobe or LeBron vs. the cream of the defensive crop.

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In preparation for the updated "Who Rules the Top Defenses?" post I'm planning to write next week, I had to run the advanced stats for every player-game of the 2010 season (all 26,488 of them, including the playoffs), in addition to SRS scores for defenses only. Since I now have that data completed, today I thought I might as well make a post out of it and list the best opponent-adjusted offensive games of the 2010 campaign (according to offensive SPM, at least).

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Here's an extremely interesting study from Jamie Merchant's blog Numeranda, regarding the "chemistry" of a given 5-man unit. Merchant used BasketballValue's 2010 adjusted plus/minus data for both lineups and individuals, and calculated which combinations were literally greater than the sum of their parts:

"Here was my thinking: we have at our fingertips (thanks to Aaron) two measures of APM, an individual measure and a five-man unit measure; is there some way to connect the one with the other? My first instinct would be to simply add the individual APMs together and see how they compare to the five-man unit APM. If player impact works in a simple, additive fashion, the two measures should be roughly equal. Is that the case?"

The usual APM caveats about samples sizes and standard errors apply, but the results are fascinating. For instance, we would expect Houston's Aaron Brooks/Shane Battier/Trevor Ariza/Carl Landry/Luis Scola lineup to be highly negative (-7), but they ended up being average (+0). And at the other end of the spectrum, we would expect Boston's Rajon Rondo/Ray Allen/Paul Pierce/Kevin Garnett/Rasheed Wallace combo to be very good (+8), but instead they were downright bad (-6).

Sometimes the pieces just fit... and sometimes they don't (we've already commented on how Wallace negatively impacted the C's when on the floor last season). This has been a basketball aphorism forever, but now we actually have some data to quantify the phenomenon.

One common media refrain when criticizing LeBron James' decision to "take his talents to South Beach" has been the idea that he left behind unfinished business in Cleveland. He and the Cavs posted consecutive 60+ win seasons in 2009 & 2010, each time securing the #1 record (and top playoff seed) in the Eastern Conference, but in both years Cleveland flamed out early. Many have used this as supposed "proof" of some character flaw on the part of James and his teammates, but what was the probability that this could have simply happened due to random chance alone?

To answer this question, I set up a very basic Monte Carlo simulation using the regular-season winning percentages of all playoff teams since the Cavs' first playoff appearance of the James era (2006). 10,000 times, I simulated the playoffs for each season, taking into account the postseason bracket & home-court advantage effects, and I recorded the team that won the Finals in each simulation. Here's how it broke down for each season:

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Many commenters have asked for this, especially in light of the fact that 1/4 of the Redeem Team are "taking their talents to South Beach" next season, so I thought some stats from the 2008 Olympics were in order. All I have is the USA page right now, though hopefully I'll be able to add other teams from the Beijing games at some point in the near future.

2008 United States Men’s Basketball Statistics

Olympic Tournament Summary

If the Redeem Team numbers are informative about the LeBron James/Dwyane Wade dynamic, Wade will be the bigger possession user (he led James 27.7% to 23.6% in terms of possessions used on the floor in the Olympics), while James will have the ball in his hands and be a facilitator (he led Wade in touches/minute, 1.33 to 1.21, and his pass/shot breakdown on touches was 59%/28% vs. Wade's 43%/34%). This aligns with the commentators who predict James will become the 21st-century version of Magic Johnson alongside his new Heat teammates.

Of course, the Redeem Team used Wade off the bench while James started, so a portion of those stats were accumulated with only one of the two in the game. Still, the general trend could hold, since they did play at least a third of their minutes together.

"Miami Thrice," they're calling it, and it would be perhaps the most impressive collection of individual superstars ever assembled on a single team. What seemed incredibly unlikely at the start of the free agent period is actually looking more than possible now, as reports claim LeBron James is "leaning towards" joining Dwyane Wade and the newly-signed Chris Bosh in South Beach to create a megateam of historic proportions.

But here's the question: if this trio gets together, what kind of damage can we expect this wrecking crew to inflict on the rest of the NBA? ESPN's John Hollinger weighed in with a PER-based analysis a week ago (he said Wade + Bosh + James + 10 replacement level ballers = 61 wins), but his system also dramatically underrated what the 2008 Celtics would do (he said 51 wins -- and I said 48, btw, so he didn't have a monopoly on being wrong), and that's the most recent example of a similar 3-star amalgamation.

In fact, the only method that correctly ballparked the C's greatness? Adjusted and/or Statistical Plus-Minus. So let's see what those systems see in the cards for a team with James, Wade, Bosh, and a bunch of nobodies.

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