More NBA spatial data

Adrian the Canadian — my designated Deadspin trawler — sent me an interesting graphic by Kirk Goldsberry and Matt Adams showing the highest percentage shooters from various regions of the court. You might recall that Goldsberry presented similar work at the Sloan Sports Analytics Conference in March (runner up for the research award). My take on this work is that, while interesting and impressive in terms of data, much of the spatial variation in shooting could be explained by factors other than location-specific shooting ability (this will sound familiar if you read my post yesterday on player tracking data).

First, random chance is an issue, especially when trying to identify the best shooters at each location. I think Goldsberry requires a certain number of shots for inclusion at each spot, but he doesn’t do the statistical analysis to determine whether the differences he presents are statistically significant (i.e., large enough such that they are probably not due to chance variation). His big surprise — Rondo leading the league in one mid-range zone — is likely based on a fairly small sample of shots.

Second, defensive position is missing from the analysis. A big red flag for this one is that Durant, at only 40% shooting, leads in the three point zone just to the shooter’s right at the top of the key. Every other three point zone has a guy over 50%. Unless there’s something challenging for right handers

Since the dawn of Linsanity

Since it all began for Jeremy Lin on Saturday, February 4th against the Nets, Jeremy Lin has shot 42-73 from the field (58%!) over four games. Lin’s shooting percentage his senior year at Harvard? 52%. His first four games as the starter for the Knicks are even more anomalous considering that he is only 3-14 from three point range. He shot 60% on two pointers his senior year, compared with 66% over the last four games.

You probably know what’s coming. That’s right, Lin has had a great start to his career, but also a lucky start. Although his performance has transformed the Knicks’ demeanor, don’t expect the insane shooting to continue. Teams will also start backing off on pick and rolls to see if he can reliably make NBA threes. If you still want to jump on the bandwagon, Brother Conor can tell you what to expect.

I also have great news today! One of my submissions to the Sloan Sports Analytics Conference was accepted for the poster session. The paper (available at my academic website, written with Christopher Walters) estimates the causal impact of NBA draft incentives on tanking as well as the causal impact of winning the NBA draft lottery. In short, we find that teams tank a lot — teams that can improve their draft position by losing have lower winning percentages than teams that can’t by about 15 percentage points. There’s good reason for all this tanking. After adjusting for team quality, winning the draft lottery provides a four year attendance boost (though only a small increase in winning percentage). I’ll explain the details in a future post.

Fair market value for college athletes

A few months ago, my buddy Jeff and I did some research for ESPN the Magazine on paying college athletes. We ignored all the institutional issues and got right to the accounting: considering costs and revenues, how much profit is each player worth to his team?

We focused on the University of Florida and found that top college football players are worth millions of dollars, while basketball players are worth a couple hundred thousand. Check out the details on the Sloan Sports Analytics Conference blog or in my previous post.

Maximizing offensive efficiency

During Kobe’s “hot streak,” I’ve been writing that he’s actually inefficient compared to Andrau Gasnum, the Lakers’ superb tw0-man post presence. I’ve said that he should give up some shots until his efficiency equalizes with Gasnum’s. Adrian the Canadian was quick to send me a Sloan Sports Analytics Conference paper arguing that teams  might equalize offensive efficiency too much already. The author (Brian Skinner) uses some network theory for unknown reasons (it’s not related to his point), but the paper boils down to

Stop Kobe before he shoots again; gearing up for the Sloan Sports Analytics Conference

My apologies for missing the last couple days on the blog, but don’t worry, I was hard at work on two projects that I’ve just submitted with a couple other guys to the Sloan Sports Analytics Conference. I’ll have more to say about them soon — one project looks at the effects of temperature, rest time, and turf type on MLS games, and the other examines the true value of winning the NBA draft lottery and measures how much tanking really goes on in the NBA.

In the meantime, Kobe Bryant is lighting up scoreboards and shot charts. He must be reading this blog, but I think all I did was make him angry. He’s taken 31 shots in each of the last three games and managed 40 points in all of them. Reading the ESPN write up from the last one, it looks like we have our new MVP.

However, over those games he’s made 47 shots for a

Sucking wind in La Paz – Sloan Sports Analytics Conference poster

Last year, I ran across an article analyzing whether high altitudes matter in South American international soccer. The authors run regressions with the away team’s altitude change as an explanatory variable and find that climbing but especially descending hurts the away team. Descending 2,000 meters seems to lower away team winning percentage by over 10 points. Pretty surprising finding.

However, these regressions only control for home team quality. It just so happens that Bolivia, Ecuador, and Colombia (i.e., the descending teams) are historically weak teams. What we have here is omitted variables bias — the naive analysis implies that descending harms performance, when in reality descending teams just aren’t very good.

On my quest for causation,