What offense and defense each control
By George Boyle · Updated Nov 9, 2026 · 6 min read
The Matchups page's four-factors table puts each team's shooting, turnovers, rebounding and free-throw numbers side by side. This study measures, factor by factor, how much of what happens belongs to the team doing it, how much to its opponent, and how much is closer to noise.
- Most predictable factor
- 3-point attempt rate
- 19.8% better than guessing the league average
- Least predictable factor
- 3-point FG%
- 0.5% — barely beats a coin flip
- Genuinely defense-driven
- Blocks & steals
- the only two, besides free-throw %
- Four factors + FT% explain
- 97.4% of scoring efficiency
- R² on an unseen season
The question behind the table
A four-factors table quietly assumes that a shooting or rebounding number is roughly half offense, half defense. That sounds right, but it isn't obviously true for every stat in it. This measures it directly.
For each factor, two numbers each get a weight: the team's own rate so far this season, and its opponent's rate of allowing that same factor. Both are taken strictly before the game and reset every season. The weights are fit to explain what happens in the game.
A weight near 1.0 means that side's own history carries straight into the matchup. A weight near zero means it barely matters, and the league average is just as good a guess.
The weights were fit on three seasons (2022-23 through 2024-25). Every number below is how they performed on seasons they never saw.
Who controls each factor
Every factor beats guessing the league average, by anywhere from a fraction of a percent to nearly a fifth.
"Shared" means both sides carry real, comparable weight. "Offense-driven" or "defense-driven" means one side has roughly 60% of the combined weight.
| Factor | Offense weight | Defense weight | Beats league average by | Who controls it |
|---|---|---|---|---|
| 3-point attempt rate | 0.80 | 0.64 | 19.8% | shared, offense-leaning |
| Assist rate | 0.65 | 0.65 | 8.7% | shared (dead even) |
| Offensive rebound rate | 0.70 | 0.49 | 8.3% | shared, offense-leaning |
| Turnover rate | 0.57 | 0.64 | 8.1% | shared |
| 2-point FG% | 0.61 | 0.55 | 6.6% | shared |
| Steals forced/allowed | 0.40 | 0.65 | 6.4% | defense-driven |
| Shots blocked | 0.41 | 0.68 | 6.1% | defense-driven |
| Free-throw rate | 0.50 | 0.61 | 5.4% | shared |
| Effective FG% | 0.58 | 0.51 | 5.2% | shared |
| Points in the paint | 0.25 | 0.34 | 4.3% | shared, defense-leaning |
| Free-throw % | 0.49 | n/a | 1.8% | offense-driven (only side there is) |
| 3-point FG% | 0.25 | 0.16 | 0.5% | shared (barely beats the mean) |
Shot volume and shot accuracy are opposite stories
Three-point attempt rate is the most predictable factor in the table, and it isn't close: nearly double the next one. It is shared close to evenly, tilted toward the shooting team. Whether a team hunts threes is a real, stable identity, and a defense built to run shooters off the line moves the number too.
Three-point percentage is the opposite: the weakest factor by a wide margin, with the two smallest weights in the table. A team's or a defense's 3-point percentage to date says almost nothing about what happens in the next game.
How often a team shoots threes tells you far more than how well it has shot them.
Blocks and steals are a defensive skill
Blocks and steals are the only two factors where the defense clearly carries more weight than the offense. (Free-throw percentage has no defensive side to compare against.)
A team's own tendency to get its shots blocked or the ball stolen matters less than how often this particular defense blocks shots and takes the ball. These read as real, lasting defensive skills, not something that happens evenly to whoever a good defense faces.
Assist rate is the cleanest 50/50 split in the table. Ball movement is as much a habit of the offense as something the defense allows, which is a useful check that the method does not default to "offense wins".
Does the slower team really control pace?
Yes, measurably. The slower of the two teams, by its own pace so far this season and not by which team is expected to win, pulls the game toward its tempo more than the faster team pushes it the other way.
The two effects are about seven combined standard errors apart, which is not close.
| Model | Error on the unseen season (possessions per 40 min) |
|---|---|
| Slower team weighted more (0.80) than the faster team (0.66) | 3.60 |
| Both teams weighted equally (0.73) | 3.64 |
| Guess the league-average pace every time | 4.27 |
0.80 against 0.66 is a real, measurable gap, but it is a lean, not a rule. "This game plays at the slow team's exact pace" overstates it. "The pace leans toward the slower team" is the honest version.
What each factor is worth in points
The four factors, plus free-throw percentage, explain almost all of a team's scoring efficiency in a game: 97.4% of it, on an unseen season. That is why this five-number breakdown has been the standard way to read a box score for decades.
Turning each factor into points shows they are far from equal.
| Factor | Points per 100 possessions |
|---|---|
| Effective FG% | +1.42 |
| Turnover rate | −1.23 |
| Offensive rebound rate | +0.61 |
| Free-throw % | +0.18 |
| Free-throw rate | +0.12 |
How the Matchups page uses this
The four-factors table itself is unchanged: both teams' raw numbers next to the Division I average.
Under it, a plain-English section turns the weights above into sentences about the two teams in front of you: which of their edges are worth points, which factors are close to a coin flip whatever either team's history says, which are one-sided, and which way the pace leans.
Method
how this was measured
Every factor weight is fit by ordinary least squares with no intercept, on three seasons of D-I against D-I box scores, and tested on seasons it never saw. (No intercept is needed: when both teams' deviations are zero the model already reads "league average".)
A side's weight has to clear two standard errors from zero to count at all. Every weight above clears that easily, since each factor has tens of thousands of games behind it.
The points-per-100-possessions conversion is a separate regression, fit on each game's own box score. That is why it can explain 97.4% of scoring efficiency: it describes a game after the fact and does not predict one in advance.
See it live
Written by George Boyle, who builds The Sport Stack. Corrections and questions: hello@thesportstack.io. Who runs this.