Tag Archives: Indiana basketball

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

The hitting streak

I just finished reading “56,” a retelling of Joe DiMaggio’s hit streak by Kostya Kennedy (thanks to my buddy Jake for the book!). He unfolds the 1941 streak like a story, complete with what the players were thinking/saying and lots of contextual details concerning DiMaggio’s family life, World War II, Italian American immigrants, etc. The book has a bit too much typical baseball nostalgia, perhaps (witty newspaper reports, grand ballparks and announcers, exaggerated personalities), but the story is undeniably fascinating and the writing is pretty good. Kennedy also sprinkles in some discussion of other hitting streaks and finishes with a good summary of quantitative work that’s been done on streaks.

The big debate about both good and bad streaks is whether they arise due to chance alone or whether they reflect Continue reading