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
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
Posted in Baseball, Basketball, Book Reviews, Causal Analysis, Probability Analysis
Tagged 56, at 'em ball, Babe Ruth, Barry Bonds steroids, baseball, basketball, Bernoulli trials, book review, Detroit Tigers broadcaster, free throws, hack-a-Shaq, Hank Aaron, hit streaks, hitting streaks, home runs, Indiana basketball, Italian Americans, Jim Price, Joe DiMaggio, Kostya Kennedy, Major League Baseball, Mark McGwire steroids, Michigan basketball, Mickey Mantle, New York Yankees, Pacific Coast League, Pete Rose, RBI, Roger Maris, Shaq, Shaquille O'Neal, Stu Douglass, Ted Williams, Willie Keeler, Willie Mays, World War II