A couple of years ago Evan Zamir built a model to convert Dean Oliver's Four Factors of basketball, effective field goal percentage (eFG%), rebounds, turnovers, and free throw rate, to net point differential. Last year I applied Zamir's formula to regressed early season four factor numbers to derive point differentials for each team.

This year I decided to redo Zamir's analysis using more recent seasons, as Zamir's had been done with seasons from the Tim Duncan and early Garnett Celtics era. One thing I noticed about Zamir's numbers is that there was slightly more weight to the defensive side of the four factors.

By contrast my similar analysis regressing the four factors on both sides of the ball as eight variables against point differential by team over the last four years I found that the offensive factors explained more of the net point differential than the defensive side. The break down, as shown below, is close to 55/45 in favor of offense.

The raw coefficients look a little closer than that. For example, the raw offensive coefficient is only 1.5% larger than the defensive coefficient. But, the spread between the best and worst eFG% teams has been much larger on offense than it has been on defense over the last four years. So that when we standardize the variable that indicates that eFG% has contributed more to winning and losing on offense over the last four years.

The table below has the model results with the raw coefficients, standard deviation for each factor, and the contribution to variance. In addition there is a column that gives the cumulative percentage by offense and defense.

The second big difference comes from the gap between offensive rebounds and defensive rebounds, which is somewhat surprising given the low regard offensive rebounds have fallen into for most teams.

In this case it is both the raw coefficient on the gap between teams that increases the variation. That is definitely interesting, and casts some question on how we value, or don't offensive rebounding especially from centers who can grab o-rebs without messing up a team's defensive floor balance. But, it doesn't tell the whole picture, as the reason teams have cut down on offensive rebounds is to increase shooters on the floor to increase their eFg% and focused on getting back on defense to cut down on their opponent's eFG%, the number one and two factors in effect on winning.

The only case case where the defensive factor has more impact than its offensive counter part is free throw rate. In that case we probably have Dwight Howard, Andre Drummond and DeAndre Jordan and any other Hack-a victim to blame.

So then we can apply the Four Factors Point Differential Model to this season to date as an out of sample test, and the model performs almost as well as on the training data. The image below is the model compared to the margin of victory via Basketball Reference as of November 15th, with an R^2 of 96%.

The one little dot higher on the model estimate than the current MOV in the middle of chart? Dwight Howard's Charlotte Hornets, where he's shooting 30& of their free throw attempts, and hitting only 41%. Otherwise the PD model explains point differential pretty well.

Lastly, here are a couple of the Added Variable Plots from the regression to get a visual of the difference in effect:

Contrasted with the less tightly aligned plot for ORebs.

Lastly, the offensive free throw rate, or FTA/FGA

The outlier dot on the lower left happens to be the Dwight Howard 2015 Houston Rockets.