Tag Archives: Rebound (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

Sloan Sports research rundown

Following on my general analysis of the Sloan Sports Analytics Conference, here’s a look at the research presentations (you’ll note: nothing on the sports side of football or soccer! I submitted one of each but they were rejected . . . ):

An Expected Goals Model for Evaluating NHL Teams and Players (Brian MacDonald)

This paper tries to predict future performance better by incorporating more measurable statistics than past models (goals, shots, blocked shots, missed shots, hits, faceoff %, etc.). His prediction tests show that he makes improvements, and at the team level, I think these results have some value. However, moving to the individual level in a sport like hockey (or basketball, football, soccer, or rugby) is hard because of complementarities between players. For example, trying to measure one player’s contribution to team wins or goal differential based on the number of shots they take is hopelessly confused with the actions of other players on the ice that affect the quality and number of these shots.

Another issue in the paper is that MacDonald controls for team level statistics (such as faceoff win percentage) in the individual level regressions, when in fact much of player value may be driven by these statistics. For example, one of Red Wing Pavel Datsyuk’s strengths is faceoff win percentage, while one of his weaknesses is hitting. The value that individuals bring through these variables is caught up in MacDonald’s team level control variables. Still, the team-level analysis is a reasonable way to improve what’s out there.

Big 2’s and Big 3’s: Analyzing How a Team’s Best Players Complement Each Other (Robert Ayer)

This paper categorizes the top three players on each team Continue reading