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:
Posted in Basketball, College Sports, Sports Stats
Tagged basketball, basketball graphic, Boston, Boston Celtics, box score, Celtics, Celtics offensive rebounding, Clippers, college hoops, defensive breakdowns, Dick Vitale, Free throw, Game Stack, game stacks, Golden State Warriors, graphical statistics, graphics sports, Hoosier, Houston Rockets, Indiana, Indiana basketball, indiana game, Lakers, lakers pistons, Los Angeles Lakers, Michigan, Michigan basketball, NBA, nba game, offensive rebound, Pistons, point attempts, Rebound (basketball), rebounding advantage, Rockets, Rockets 23 three pointers, Rockets three pointers, shooting percentages, shot attempts, Sports, sports statistics, Three-point field goal, turnover rates, visual shooting percentages, visual statistics, visualization, visualizing basketball games, Warriors, Wolverines
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)
- 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
Posted in Basketball, College Sports, Innovative Ideas, Sports Stats
Tagged basketball, basketball graphic, box score, Celtics, Celtics offensive rebounding, Clippers, college hoops, defensive breakdowns, Dick Vitale, Free throw, Game Stack, graphical statistics, graphics sports, Hoosier, Indiana, Indiana basketball, Lakers, Michigan, Michigan basketball, NBA, nba game, Pistons, point attempts, Rebound (basketball), rebounding advantage, shooting percentages, shot attempts, Sports, sports statistics, Three-point field goal, visual shooting percentages, visual statistics, visualization, visualizing basketball games, Wolverines
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
Posted in Baseball, Basketball, Causal Analysis, College Sports, Hockey, Probability Analysis, Research Papers, Sports Stats
Tagged Allan Maymin Philip Maymin, An expected goals model, basketball, Big 2s and 3s, Brian MacDonald, Celtics, CourtVision, cumulative win probabilities NCAA basketball, deconstructing the rebound, Effort vs. Concentration, experience and winning NBA, free throw shooting under pressure, Gartheeban Ganeshapillai, Goldsberry, hockey, James Tarlow, John Guttag, Justin Rao, Kirk Goldsberry, machine learning, Mark Bashuk, Matt Goldman, MIT News, MIT Sloan Sports Analytics Conference, motion tracking analysis NBA, National Basketball Association, National Hockey League, NBA, NBA chemistry, NBA synergies, NHL, optical analysis NBA, optical tracking data, Peter Dizikes, Predicting the Next Pitch, Rajiv Maheswaran, Ray Allen, Rebound (basketball), rebound study wrong sloan sports, research papers Sloan Sports Conference, Robert Ayer, Sloan Sports Conference, Sloan Sports Conference research overview, Sloan Sports Conference review, Sloan Sports Conference summary, spacial analysis NBA, USC rebound study