This post was prompted by this twitter exchange:
I want to clarify this issue for non-stats people. Adjusted plus-minus and similar analytics, including my own adjusted defensive impact analytic that I debuted a couple days ago, use multivariate regression to ‘adjust’ the raw numbers. The basic idea is that a lot of players who are not very good get to share the floor with great players sometimes. Udonis Haslem gets a fair bit of floor time with LeBron James. When that happens, Haslem’s raw plus-minus is going to look great! But that’s mostly because James was also in the game with him. So the regression controls for all the players on the court, and tries to extract the contribution made by each.
I’m finally ready to share a big project I’ve been working on for months. Try selecting a player from the drop down menu below. The visualization that appears shows the defensive impact a player has adjusting for all other defensive players and the offensive player who is actually shooting. Blue squares indicate that when the selected player is on the court, the probability that an offensive player will make his shot declines (so blue is good defense). Red squares indicate that when the selected player is on the court, the probability of any offensive player making his shot rises. Go ahead and take a second to play with the drop down menu. I’m not ready to rollout every player yet, but the Pacers, Bulls, and Grizzlies are all on there. After the jump, I explain the full methodology, and answer some questions that you’ll probably have after looking at the charts.
Adjusted Defensive Impact by Court Location
Face it: Greg Monroe is not going to be dealt. Kyle Lowry is not going to be dealt. Pau Gasol is not going to be dealt. We’re all going to wake up on Friday and ask ourselves, “wait a second, wasn’t yesterday the trade deadline?” Here’s a look at some NBA defenses to ease the pain.
How about them Pacers? The boxes here are colored according to the league average, so blue indicates that opponents shoot worse than the league average when they face the Pacers, while warm colors indicate that opponents shoot better than the league average. No big surprises here–the Pacers have a dominant defense. But something interesting jumped out at me and I’ve flagged it by labeling the PPS (points per shot) of high volume locations. The PPS for some midrange shots is actually higher than the PPS for some 3-pt and rim shots! That’s just crazy. Generally speaking, mid-range shots are a poor value compared to 3s and shots at the rim. In a version of this graph that used 4-week old data, there were even more mid-range locations that paid off, but it looks Indiana has even gotten a little better since then. Just brutal.
Opposing FG% compared to league average, Indiana Pacers
More graphs below the jump!
This post is about the amazing Roy Hibbert
. It has been a long time since my last post because I have been writing a tool for collecting and analyzing NBA game data. The first fruit of that effort is the two graphs below. Before I get to them, I have to explain why I thought any of this was worth doing in the first place.
NBA observers are always talking about how some player makes everyone around him better. This sports cliche is almost always used in basketball to talk about point guards or a combo/wing player with good court vision in the mold of Kobe or LeBron. The ‘makes his teammates better’ meme is actually particularly apt for describing basketball. Sure, a quarterback and a receiver need each other, and a quarterback needs his offensive line. And yeah, one bad fielder can ruin a good double play. But teammates in these sports are not as reliant on each other as the five players on a basketball court are.