Tag Archives: MIT Sloan Sports Analytics Conference

Who tanks in the NBA?

Tanking: intentionally losing in order to improve draft position.

After my PhD buddy Chris and I circulated our findings that NBA teams tank a lot, we’ve been asked a few times, “Which teams are tanking?” Today I offer a quick look at teams that have likely tanked.

First, a refresher: we measure tanking by comparing performance before and after playoff-eliminated teams “clinch” their lottery spot. In the last couple games of the season, many teams lock in their spot, so they no longer have an incentive to lose. Those games act as our control. The problem with doing it this way is that some tankers may keep trying to lose even after they clinch their spot. This could happen because teams shut down star players because of “injury” or just because teams develop a habit of losing.

So, the big caveat with the results below (and the results in our paper) is that we are almost certainly missing some tankers. Some teams 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

Sloan Sports Conference publicity

I spent most of Friday and Saturday at the MIT Sloan Sports Analytics Conference in Boston, checking out other research and discussing my work (with my buddy Chris) on the NBA draft and tanking. Peter Dizikes wrote a nice article for MIT News discussing our project and some of the other work by MIT affiliates. I was also interviewed by a fellow named David Staples from the Edmonton Journal about our project.

David mentions another project on tanking presented at the conference. Adam Gold, who’s a PhD student at the University of Missouri, presented his “solution” for tanking. The proposal: total team wins after playoff elimination should determine draft order. My problem with this: teams that are eliminated sooner have more time to accumulate wins post-elimination, so, rather than race for the overall worst record, teams would race to be eliminated first. I think this would make the problem worse, since teams with low expectations  might give up early in the season, even if these expectations were wrong.

Adam’s response was that no teams will tank early, since they all try to make the playoffs first and foremost. I wish that were true, Continue reading

To tank or not to tank

Last week, I mentioned that my paper with my PhD cohort Chris was accepted for the poster session at the MIT Sloan Sports Analytics Conference. I’ll give the summary and some pictures today (you can find the full paper on my academic website). The project looks at the age old subject of tanking for position in the NBA draft lottery. We answer two questions:

  1. Should teams tank for a better draft position?
  2. How much do teams actually tank?

For the first question, we head right to the lottery. We are interested in the causal effect of obtaining the top pick in the draft. If the first pick is truly valuable, then teams should be willing to lose intentionally to get it. LeBron James, Tim Duncan, and Shaquille O’Neal were all first picks, but so were Greg Oden and Michael Olowokandi. We want the average value of all the first picks since the draft lottery took its current form in 1990.

Since there is some randomness in who wins the lottery, Continue reading