Four Factors and Team Efficiency in the 2024 WNBA

The four factors are a useful way to break down basketball efficiency because they focus on the areas that usually decide games: shooting, turnovers, rebounding, and free throws. Instead of just saying a team was good or bad on offense or defense, they show where that performance actually came from.

For this analysis, I used the 2024 WNBA Advanced Stats table from Basketball-Reference. After copying the table into Excel and saving it as a CSV, I loaded the data into R to compare how well the four factors explained Offensive Rating and Defensive Rating across the league.

Data Used

The table below includes the team ratings and the offensive and defensive four-factor numbers used in the two regression models. The logos are included just to make the table easier to read, and the color shading helps show where teams were stronger or weaker in each category.

2024 WNBA Advanced Stats Used in the Regression Models
Offensive and defensive four-factor data by team
TEAM
RATINGS
OFFENSIVE FOUR FACTORS
DEFENSIVE FOUR FACTORS
ORTG DRTG OFF EFG% OFF TOV% OFF ORB% OFF FT/FGA DEF EFG% DEF TOV% DEF DRB% DEF FT/FGA
New York Liberty 109.6 97.9 0.521 14.2 25.1 0.203 0.476 14.5 79.2 0.167
Connecticut Sun 105.0 96.4 0.488 13.8 25.2 0.239 0.482 17.6 78.2 0.204
Minnesota Lynx 104.6 96.5 0.518 15.3 22.3 0.182 0.460 16.5 74.2 0.181
Las Vegas Aces 108.0 101.2 0.523 12.4 16.6 0.223 0.488 14.1 79.5 0.189
Seattle Storm 104.2 98.6 0.478 13.5 24.2 0.211 0.477 16.5 74.6 0.210
Indiana Fever 106.1 109.5 0.523 15.7 24.6 0.194 0.507 12.8 76.7 0.228
Atlanta Dream 99.0 102.5 0.452 14.0 25.4 0.227 0.488 14.3 78.3 0.208
Washington Mystics 99.7 103.4 0.506 17.0 20.6 0.172 0.502 16.7 75.2 0.241
Phoenix Mercury 103.6 107.8 0.503 15.1 20.0 0.222 0.496 13.1 71.6 0.189
Chicago Sky 99.1 105.6 0.457 14.3 29.5 0.187 0.499 14.2 76.8 0.228
Los Angeles Sparks 98.6 107.7 0.478 16.6 22.0 0.226 0.522 14.2 76.1 0.211
Dallas Wings 104.2 114.0 0.490 15.8 30.2 0.206 0.536 13.8 73.4 0.236

Offensive Four Factors Model

The first model looks at offense. I regressed ORtg on the four offensive factors together: offensive effective field goal percentage, offensive turnover percentage, offensive rebound percentage, and offensive free throws per field goal attempt.

The idea is pretty straightforward. If the four factors really explain offensive efficiency, then they should explain a large portion of the differences in Offensive Rating from team to team.

Offensive Regression Coefficients
ORtg regressed on the offensive four factors
TERM ESTIMATE STANDARD ERROR T STATISTIC P VALUE
Intercept 25.4153 9.5264 2.6679 0.0321
Offensive eFG% 158.4509 11.8203 13.4050 0.0000
Offensive TOV% −1.2798 0.1946 −6.5770 0.0003
Offensive ORB% 0.4288 0.0746 5.7459 0.0007
Offensive FT/FGA 40.4983 13.7468 2.9460 0.0215

The offensive regression had an R-squared of 0.972. That means the offensive four factors explained about 97.2% of the variation in Offensive Rating across WNBA teams.

Defensive Four Factors Model

The second model looks at defense. I regressed DRtg on the four defensive factors together: opponent effective field goal percentage, opponent turnover percentage, defensive rebound percentage, and opponent free throws per field goal attempt.

This is the same general idea as the offensive model, but flipped to the defensive side. A good defense should force worse shooting, create turnovers, finish possessions with rebounds, and avoid sending teams to the line too often.

Defensive Regression Coefficients
DRtg regressed on the defensive four factors
TERM ESTIMATE STANDARD ERROR T STATISTIC P VALUE
Intercept 92.9998 9.3435 9.9534 0.0000
Defensive eFG% 129.2923 14.2768 9.0561 0.0000
Defensive TOV% −1.6573 0.1383 −11.9841 0.0000
Defensive DRB% −0.5172 0.0676 −7.6497 0.0001
Defensive FT/FGA 50.6029 11.0244 4.5901 0.0025

The defensive regression had an R-squared of 0.994. So the defensive four factors explained about 99.4% of the variation in Defensive Rating across teams.

Offense vs. Defense

R-Squared Comparison
Which side of the ball was explained better by the four factors?
SIDE REGRESSION R SQUARED PERCENT EXPLAINED
Offense ORtg on offensive four factors 0.972 97.2%
Defense DRtg on defensive four factors 0.994 99.4%

The offensive regression had an R-squared of 0.972, while the defensive regression had an R-squared of 0.994. I got these numbers by running two separate multiple linear regressions in R. The first regression used ORtg, or Offensive Rating, as the outcome variable and the four offensive factors as the explanatory variables. The second regression used DRtg, or Defensive Rating, as the outcome variable and the four defensive factors as the explanatory variables.

The R-squared value shows how much of the variation in each respective rating is explained by the model. In the offensive regression, the R-squared of 0.972 means the four offensive factors explained about 97.2% of the variation in Offensive Rating across teams. In the defensive regression, the R-squared of 0.994 means the four defensive factors explained about 99.4% of the variation in Defensive Rating.

Both numbers are extremely high, which shows that the four factors play a major role in explaining team efficiency. That makes sense because shooting efficiency, turnovers, rebounding, and free throw rate are all directly connected to how teams score points as well prevent points from being scored.

Since the defensive model had the higher R-squared, the answer to the main question is that defense was more easily explained by four-factor variation across teams. The difference between the two models is small, so offense was still explained very well, but defense had the slightly stronger fit in this dataset.