Analytics and the Greg Popovich decision

NBA commissioner David Stern is upset with San Antonio Spurs coach Greg Popovich for sending four of his top players home on a plane to rest, instead of playing them in last night’s game against the Miami Heat. Popovich rested his players in order to prepare them for a Saturday home game. ESPN notes:

> The Spurs were playing their fourth game in five nights, their sixth game of a road trip and their 11th road game in November.

On top of this, the Heat had several days of rest before the game.

Analytics Question 1: How did this happen?

When I read this, I thought that surely this is something the NBA schedule makers should have taken into consideration – the quote above reads like a list of optimization constraints. It turns out they probably did:

> We always hear about the "four games in five nights" and back-to-backs. Are there certain limitations or considerations you take into account?
> This year, for instance, we had an outer limit of 23 back-to-backs and four "four out of fives." At some points during the process, there were teams with more than that. You look at those things and you correct them before the schedule is final. "Oh, this team has 25 back-to-backs? We can't go with this. We have to find a way to get them down."

The NBA schedule makers use software to assist them with the scheduling process. But what is the approach? Does it involve optimization? (A quick search did not turn up anything definitive.) Does it consider not only “four games in five nights” situations, but also interactions between teams? The Spurs situation would have been far less egregious had the Heat been coming off a few games themselves – they’d be equally fatigued. Ideally, the schedule creation process should take this into account to create “fair” matchups.

Analytics Question 2: Did Popovich do the right thing?

How would we evaluate this coaching decision? The analytics question is to forecast the expected number of wins for sitting and playing the four players involved. The factors involved include:

• The increased chances of losing the Heat game. Of course, we now know that the Spurs lost last night’s game. But let’s pretend that it is a couple of weeks ago and we advising Pop. We’d want to consider not only the comparative value of the players who would play in the place of the resting players, but also the “team effects” as described in this Michael Trick post. Wayne Winston looks at the relative effectiveness of different lineups on his blog all the time. This seems relatively straightforward.
• The short- and long-term value gained from giving his guys a break. We can break this down into two factors. First, the reduced chance of injury. Presumably not too hard. Second, the increased effectiveness due to Pop’s four top players being well rested. This could be estimated by running a regression on a player performance metric with a parameter being the number of days of rest.

I have no idea whether Popovich did the right thing, and running the numbers would not give us an authoritative answers (because all models have assumptions). But wouldn’t it be interesting to see what analytics would tell us?