Most NBA teams past the twenty game mark over the holiday weekend, approximately a quarter of the season. Statistically, however, we are closer to the halfway point, at least in the sense that a team’s average margin of victory (MOV) should predict a bit over half of the variation in win percentage over the rest of the season.

In some ways that’s a low bar to jump over as what has happened in terms of margin of victory so far, based on history, also won’t explain nearly 50% of the typical team’s win percent variation from here on out. To give you a sense of what the relationship of MOV to win percentage from here on out, below is a plot of MOV in the first twenty games to win percentage to the end of the season for 2010 to 2017.

For those fans whose favorite team has been sub-par to date (hi Clips fans!), you can take solace in the turnaround in outliers over the line like the 2013-2014 Brooklyn Nets, who came out of the twenty game mark at 6 and 14 with an MOV of -7.9, but still won 61% of their games going forward.

And any fans on teams exceeding expectations, before you get ahead of yourselves (You know who you are), just remember the 2011-2012 Philadelphia 76ers, who came in at 14 and 6 with a plus 11.7 MOV. Yet they managed to win less than half of the rest of their games.

Another interesting thing is that, at this point the winning percentage of each team is almost as good a predictor as the MOV for performance over the rest of the season. In the last six seasons winning percent after twenty games has an R^2 of .489 with win percent for the remaining schedule.

In some ways that’s not that surprising, the whole reason we’re interested in MOV is that it’s correlated to winning and stabilizes more quickly than wins and losses. But, what we find in this analysis is that the gap by now is pretty small in terms of prediction for the rest of the year.

To go further, a few years ago Benjamin Morris found on his site found that using the MOV and record from the other 81 games in a season both factors, winning and MOV were statistically valid predictors, and using both improved the prediction. So, I wanted to use the MOV and win percent from the just first twenty games to predict the rest of the season wins. In an OLS model with the six seasons as well as various subsets of the data and in a Partial Least Squares regression, both the MOV and Win Pct were statistically important predictors and showed a very marginally improved prediction.

In the combined model 67% of the prediction came from he MOV, and 33% from a team’s win percentage, with some variation in the subsamples.1 Given how well correlated the two predictors are it is tough to draw too much from the split, though it was encouraging to see the relative stability in the sub-samples. On the other hand using a sampling method, called Bootstrap sampling, whether Win Pct was significant at the 95% level depended on which Bootstrap method was chosen.

So the most we can probably say from this is that average margin of victory is the stronger indicator of team quality at the twenty game mark, but that Win Pct is *probably* something of an indicator as well. At least given these two factors, if you’re favorite team is under-performing in close games, while that will mostly even out, it may not entirely.

The differences between the two models applied to the rest of the season are pretty small for most teams. Teams that have over performed their MOV to date, like the Boston Celtics and Detroit Pistons are projected to win just over one more game over the rest of the season than their MOV would indicate. The biggest movers by some distance are the Thunder, projected to win about three fewer games from here on out than in the MOV model.

Why winning might be a skill is tough to say definitively. Other research gives some interesting hypothesis, such as indications that ahead teams simply relax and give up part of their lead even controlling for who’s in the game. Or Ben Falk’s finding better projections without garbage time being included in the data. But there is probably need for more research.

Below are the model projections using both the MOV only and combined Win Pct and MOV model along with the differences:









