GoKawiilIn basketball, a big part of the mental game comes down to decisions around when to shoot and when to pass. But it’s not so easy to determine which players are making good or bad decisions. So MIT researchers created a metric called expected action value (EAV), which is essentially an assessment of the likelihood of a play’s success. Using a model trained on all 786,208 passes from the 2018–’19 NBA season and all 1.4 million shots from 2013 to 2019, they were able to figure out expected outcomes of different plays.
EAV takes into account the velocity of the shot and the acceleration of the player making the shot as well as the positions of players on the court. For instance, an uncontested three-point shot from the corner has a higher EAV than a two-point attempt from a player getting double-teamed closer to the basket (or “in the paint”). This approach can tell you not only the likelihood of a successful shot but also the chances of a successful pass. If the player decides to pass instead of shoot, and the receiver of the pass has a reasonable chance to make the shot, then that was a good decision by the passer.
A consistent record of high-EAV choices—passing at some times, shooting at others—means a player is making good decisions. “You can just calculate: How many times do players make good decisions? How many times do they make bad decisions? And we can rank NBA players by good decision-makers and bad decision-makers,” says Hosoi.
This approach can also help teams see if points are being left on the table. Given that teams averaged about 110 points per 100 possessions in the 2019 NBA season, or 1.1 points per possession, if a player passes up a play option with an EAV of more than 1.25 for a play with a lower EAV, the Sports Lab’s model classifies it as a “missed opportunity.” Flagging these moments saves time for coaches, who have to review video for at least 82 games every season. “If we can point to the time stamps of the different games where your guys might have missed an opportunity, you can take advantage of that, right?” Hosoi says.
At this point, the MIT Sports Lab doesn’t really need to advertise its services. “If you are good in sports, everybody who needs to know will know,” says Hosoi. The lab’s partners come to it if they need answers to questions—as the NFL did during the covid crisis.
At the beginning of the 2020 season, some teams had opened their stadiums for limited in-person attendance while others didn’t allow any fans. In March 2021, “there was a paper that was published that said in the cities where NFL stadiums have opened, there are spikes in covid cases,” Hosoi recalls. “And the NFL called us and said, ‘Wait, is this true? Because if this is true, we’re going to stop. Can you guys do an analysis on this?’”
After investigating, the Sports Lab identified a problem with the original paper. NFL teams made decisions about opening stadiums in conjunction with stadium owners and local governments. What the paper didn’t consider, however, was that some states had stricter covid protocols than others, and it was stadiums in those places that tended to stay closed to fans.
The lab accounted for the confounding factors involved and found that opening a stadium with distancing and masking protocols had no effect on covid cases. In fact, the analysis found that in some places, in-person attendance was correlated with case totals that were lower than expected. Hosoi hypothesizes that this was not only because the open stadiums required distanced seating and other safety measures but also because if fans were at the stadium, they were usually outdoors—not mingling in a crowded bar or at a friend’s house. Partly on the strength of these findings, the NFL decided to open all stadiums for in-person attendance in the 2021 season.