Tag Archives: NBA

Basketball Stacks part 2: Rebounding

Yesterday, I posted a new idea for visualizing box scores: Game Stacks. While the first version did a good job of showing shooting percentages and turnover rates, it didn’t do a good job on rebounds. As my pops pointed out, Indiana had a big rebounding advantage over Michigan by the basic numbers (36-22), so it seemed wrong to rely only on the height of the stacks to determine who rebounded better. The reality: Michigan got more chances not because they rebounded better, but because they had more misses — and you have to miss to get a second chance. The height of the stacks just showed that Michigan got more offensive rebounds, even though their rebounding rate was terrible.

So, round two. Here’s the Michigan-Indiana Game Stack redesigned to capture rebounding:

Michigan at Indiana 2-2-2013

Without play by play data, I had to keep the rebounding simple — I figured out the offensive rebound rate for each team:

Off reb rate = your off rebs/(their def rebs + your off rebs).

Then, I multiplied this rate by the relevant number of shots to generate the “Missed (O Reb)” category for each type of shot (the dashed regions). Each dashed/empty combo now visualizes the offensive rebound rate for the relevant team.

Now the picture is clearer:

Visualization: Basketball Game Stacks

Note: On my dad’s advice, I posted another version of the Game Stacks that depicts rebounding rates, rather than just total offensive rebounds. The discussion in this post is a little naive on that point — the new version yields a better analysis of rebounding.

I have a general hang up when looking at the box score for basketball (or listening to announcers list off statistics). I see some rebounding numbers, but I can’t tell who rebounded better without offensive and defensive breakdowns, plus the number of shots missed by each team. And I see shooting percentages and shot attempts, but it’s hard to put it all together into how a team got its points.

I realized that what I really want to see is not complicated. Here’s the list:

  • What each team did with their scoring chances:
    • Two point attempts
    • Three point attempts
    • Free throw trips (2 attempts)
    • Turnovers
  • Efficiency on each type of shot
  • Rebounding advantage in terms of extra scoring chances
  • And, of course, total score

All these stats exist, but there should be an easy way to see all of it at once and get a sense for how the game was won. Here’s my first try, the Game Stack:

Michigan at Indiana 2-2-2013

The picture shows total “plays,” or chances to score, for each team, and total points, broken down by type. In a quick glance, you can see that Indiana was out-rebounded (Michigan got three more chances to score) and turned the ball over a ton. However, on just over 60 non-turnover plays, the Hoosiers Continue reading

Is Joe Flacco Elite? Barnwell strikes again!

Bill Barnwell is up to his usual tricks at Grantland. This time, he’s tired of hearing that Flacco is an elite quarterback and wants a new measure of quarterback value. Flacco gets credit for piling up wins, which Barnwell thinks is unfair:

For whatever good or bad Flacco provides, he has spent his entire career as the starting quarterback of the Baltimore Ravens, who perennially possess one of the league’s best defenses. He also has Ray Rice and a solid running game to go alongside him on offense. It’s safe to say that a win by, say, Cam Newton usually requires more work from the quarterback than one by Flacco.

I agree with this wholeheartedly. In response, Barnwell tries to capture quarterback value by creating an “expected wins” measure based on points allowed by the defense and comparing this to actual wins. He argues that a quarterback with more actual wins than expected wins is doing well because he is scoring more points than average.

An example helps explain the concept. First, Barnwell notes that teams have won 86.5% of games recently when allowing between 8 and 12 points. Imagine a team that allows between 8 and 12 points in all 16 games. They are expected to win 86.5% of those games, or 13.8 games. If the team won 14-16 games, Barnwell would argue that the quarterback is doing better than average, while if the team won fewer, Barnwell would argue that the quarterback is doing worse.

As hoped, Flacco is unimpressive by this measure (while the usual suspects — Tom Brady and Peyton Manning — are top dogs). He has 44 wins in 64 regular season games, but because the Ravens D is so good, an average QB would have managed about 42.

Before going farther, I’ll warn you: these numbers are pretty meaningless. I’ll start by explaining Continue reading

My Imaginary Baseball Team: The Portland Peskies

During the NBA season this year, I wrote up some parameters for an alternative way to build an NBA winner: The Seattle Scientists. The idea behind the Scientists is the same old Moneyball methodology for small market teams — find the undervalued assets and spend your money there. In the NBA, my buddy Tony and I think effort, defense, and intelligence are the assets to focus on. In the the MLB, there are some related options: bunting, speed, and defense again. We settled on the Portland Peskies for this thought experiment (an over-educated city that would appreciate a non-traditional team), though the Indianapolis Institute and the Las Vegas Vig (“You can never beat the house!”) were also in the running.

It’s no coincidence that I’m writing this while my Tigers play their old nemesis the Twins. The Tigers (outside of Quintin Berry this year) never have any hitters that would fit the Pesky mold. But Twins outfielder Ben Revere (currently snagging a tailing line drive off his shoe tops) would be on the Peskies’ radar for sure, as would Alexi Casilla and Denard Span. Revere has 6 bunt singles this year on 13 tries and 16 steals Continue reading

Part 2: The Return of Adrian the Canadian

Yesterday, Adrian reasserted himself on the blog with a clear proposal to reduce diving in soccer.  Today, he shows off his versatility with a response to my recent thoughts on fairness in U.S. and European professional sports leagues (written in relation to my brother Conor’s defense of talent concentration in European soccer). For a taste of the historical, economic, legal, and political, set aside 10 minutes and read on:

How long has it been? Too long, I think.  But Tyler’s recent post has compelled me to withdraw from my self-imposed hibernation and away from the stultifying process of studying for the Ontario bar exam. In short, I disagree with the capitalist/socialist, American Sports/European Sports dichotomy or, rather, I think it abstracts away from the real issue – that cartels make a heck of a lot more money than entities that exist in competition with one another. In short, the NFL and MLB are not staunch defenders of equality values; Dan Snyder and Hank Steinbrenner are not driving the train to the Finland Station.

The standard argument goes something like this: isn’t it ironic that America, land of unbridled capitalism, home of animal spirits on free and open fields, has “socialist” sports leagues that redistribute resources from winners to losers while red, socialist, pinko Europe has a free and open market for sports talent? It’s a cute argument and one that elicits a nice “hmmm…” from readers and there are certainly large elements of truth to it. American sports are, at least nominally, more redistributive, and there is a larger perception that American sports are organized more “fairly” than European sports from a competitive standpoint. Still, it’s far from clear that European sports are more aristocratic than American sports if we look at the highest levels and, more importantly, I think this distracts us from a deeper, more thorough comparison of why European sports and American sports are organized so differently.

Barcelona’s greatness is undeniable, but it’s not a greatness that has translated into a dynasty at the highest levels of competition. While Barca has been the dominant team in La Liga, it’s only won three of the last ten Champions League titles despite making each of the last ten tournaments. This means that the Champions League may not even be as “aristocratic” as the NBA:  eight different teams have won the Champions League while only six have won the NBA championship in the same span. And, unlike La Liga Continue reading

Basketball questions

I’ve been watching my fair share of basketball during the playoffs — very exciting, compelling series so far, despite the injuries. However, I have a few questions:

  1. Why do you have to hand the ball to the ref before you throw it in? Wouldn’t it be much more exciting to let players throw it in as soon as they can get a ball, like a soccer throw in? Teams are allowed to do this after a made basket already. Add another commercial break to balance out the faster pace if that’s what it takes.
  2. Why can you only draw a charge if you stay on the ground and fall over? The offensive player can draw a foul while jumping and keeping his feet, why not the defense? If the defender jumps, the best case is a no call. Referees have a big say at the end of basketball games, but it’s not a bigger say than baseball umpires, for example, who must make every ball and strike call. I think one of the reasons people persecute basketball refs (besides the Tim Donaghy scandal) is that the foul rules aren’t especially consistent.
  3. Why do teams get so many timeouts, especially in the first half? They have lots of practiced plays that they can signal in from the sideline. I suppose that the endgame timeout flurries increase the tension on those individual plays, but the downtime in between is no fun, and I bet the rest of the game seems less important by comparison. Again, if we need a couple more evenly spaced TV timeouts or sponsors on the jerseys to compensate, I’m fine with that.

These are my questions. Do you have any answers?

More NBA spatial data

Adrian the Canadian — my designated Deadspin trawler — sent me an interesting graphic by Kirk Goldsberry and Matt Adams showing the highest percentage shooters from various regions of the court. You might recall that Goldsberry presented similar work at the Sloan Sports Analytics Conference in March (runner up for the research award). My take on this work is that, while interesting and impressive in terms of data, much of the spatial variation in shooting could be explained by factors other than location-specific shooting ability (this will sound familiar if you read my post yesterday on player tracking data).

First, random chance is an issue, especially when trying to identify the best shooters at each location. I think Goldsberry requires a certain number of shots for inclusion at each spot, but he doesn’t do the statistical analysis to determine whether the differences he presents are statistically significant (i.e., large enough such that they are probably not due to chance variation). His big surprise — Rondo leading the league in one mid-range zone — is likely based on a fairly small sample of shots.

Second, defensive position is missing from the analysis. A big red flag for this one is that Durant, at only 40% shooting, leads in the three point zone just to the shooter’s right at the top of the key. Every other three point zone has a guy over 50%. Unless there’s something challenging for right handers Continue reading