# Tag Archives: sports statistics

## 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:

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:

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

## 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