Archive for the 'Analysis' Category

This is sort of a random data dump/trivia post, but this old Doug Drinen post about NFL teams' records vs. fellow playoff participants inspired me to run a similar query for NBA top-4 seeds' records against other top-4 seeds (in either conference). Since the NBA expanded to 16 playoff teams in 1984, here are the top-4 seeds with the best records vs. their fellow top-4 seeds:

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Tyrus Thomas is in some pretty heady company this season:

(For single seasons; played in the NBA/BAA; in the regular season; in 2010-11; requiring Offensive Rating >= 110 and Usage Pct >= 26 and Defensive Rating <= 101 and Minutes Played >= 450; sorted by descending Win Shares Per 48 Minutes.)

Now, the defensive rating might overstate Thomas' case (although the Bobcats are 4 pts/100 poss. better on D when he's in the game), but even if you remove the defensive rating requirement, he's still in a good group:

I'll also grant that Thomas is the only non-starter in the group, meaning the lineups he's faced have not been as strong as the others on that list.

Still, that's impressive production thus far... Thomas flies under the radar because he always seems to come off the bench for mediocre teams, but he's quietly having a strong season. With BBR Blog favorite Gerald Wallace slumping and suffering an ankle injury this week, Thomas now has a chance to show everyone what he can do in expanded minutes.

Although we're only a month and a half into the season, the 2010-11 campaign has already seen its share of impressive winning streaks. The Spurs rattled off 12 straight across almost the entire month of November (a streak matched by the Mavericks last week), the Lakers & Hornets each won 8 consecutive to start the year, and the Celtics, Heat, & Knicks are currently in the midst of 8+ game win streaks themselves. How probable have these runs of victories been?

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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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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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For those with ESPN.com "insider" subscriptions, here's John Hollinger on the Miami Heat's struggles (written before the Washington game, but the points still stand).

For those without, the basic gist is that Miami's supporting cast hasn't really played below expectations; if anything, they've exceeded them -- guys like James Jones & Zydrunas Ilgauskas are playing better than you would have expected from their 2010 stats. Instead, the biggest reason for the gulf between the Heat's preseason hype and actual on-court results (which, given their +6.82 SRS, haven't been quite as bad as the media suggests) is the obvious one: the simultaneous decline of Dwyane Wade, LeBron James, and to a lesser degree, Chris Bosh.

(ESPN's Tom Haberstroh has more on the psychology of Wade's struggles here.)

Just take a look at the trio's advanced stats together this year, vs. last season when they were apart:

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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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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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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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