In my article about what kind of teams beat Clemson this year, I dipped my toes into the world of EPA. In a previous life, I was an engineer and loved data. I had heard of EPA, but didn’t know much about it. So, I did a fair amount of research. What I did not […]
In my article about what kind of teams beat Clemson this year, I dipped my toes into the world of EPA. In a previous life, I was an engineer and loved data. I had heard of EPA, but didn’t know much about it. So, I did a fair amount of research. What I did not consider was does everyone else know what EPA is, why it was created and how to interpret it? I surely did not. Below is a summary of that research.
If you watch enough college football, you already know traditional stats don’t always tell the full story. A five-yard gain on 3rd and 4 feels very different from a 5-yard gain on 3rd and 12. A team can pile up yards between the 20s and still struggle to score. That gap between what happened and what it meant is why Expected Points Added—better known as EPA—has become one of the most useful tools in modern football analysis.
EPA doesn’t replace the box score. It adds context. It tells us how much value each play added or subtracted relative to what an average team would be expected to do in the same situation.
What “Expected Points” Means
At the core of EPA is Expected Points (EP). Expected Points represent how many points an offense is expected to score on a drive given the current situation. That expectation is derived from historical college football play-by-play data.
Before the snap, every play has an expected points value. That value is driven primarily by:
- Down
- Distance
- Field position
For example, 1st and 10 at your own 25 is historically worth far fewer Expected Points than 1st and 10 at the opponent’s 25. The offense is simply closer to scoring in the second situation, and Expected Points models quantify that difference.
Expected Points are probabilistic. An EP value of 1.5 does not mean a team will score exactly 1.5 points. It means that, on average, teams in that situation score about a point and a half before the drive ends.
From Expected Points to Expected Points Added
Expected Points Added measures how much a single play changes an offense’s scoring outlook.
The formula is straightforward:
EPA = EP (after the play) – EP (before the play)
If a play improves the offense’s situation, EPA is positive. If it makes scoring less likely, EPA is negative. From the defense’s perspective, the signs flip—forcing negative EPA is success.
This framing is why EPA works so well. It measures change, not just production.
How EPA Is Calculated on Every Play
Every EPA calculation follows the same process.
First, we identify the game state before the snap—down, distance, and yard line—and assign an Expected Points value based on historical data.
Second, the play happens.
Third, we identify the new game state after the play and assign a new Expected Points value.
Finally, we subtract the two values using the EPA formula.
This approach naturally captures leverage or the offense’s odds of turning the drive into points.
Touchdowns create massive EPA swings. Turnovers produce sharp negative values. A short gain on third down is treated very differently than the same gain on first down.
Interpreting EPA for Teams and Players
Once EPA is calculated for each play, it can be aggregated.
EPA per play gives a snapshot of overall efficiency. Teams with positive EPA per play consistently improve their scoring outlook. Teams with negative EPA are falling short of expectation.
For quarterbacks, EPA per dropback often tells a clearer story than yards or completion percentage. It includes sacks, scrambles, and interceptions—plays that traditional passing stats often underweight or ignore.
At the team level, EPA helps explain why some offenses consistently outscore opponents even when total yardage looks similar. Explosive plays matter, and EPA captures their impact directly.
EPA vs. Traditional Stats
Traditional stats treat all yards the same. EPA does not.
A 6-yard gain on 1st and 10 may barely move expected points. A six-yard gain on 4th and 5 can completely change a drive. EPA reflects that difference.
That’s why EPA tends to correlate more strongly with winning than raw yardage. It measures value, not volume.
What EPA Does Not Do Well
EPA isn’t perfect. It depends on historical data and modeling choices. Garbage-time plays can distort results. Small samples—especially for individual players—can be misleading.
EPA also doesn’t explain why a play worked: missed tackles, coverage busts, great WR blocking downfield, perfect play call vs a blitz, etc. It only measures the outcome, not technique. Film still matters.
But as a way to understand impact and efficiency, EPA remains one of the clearest lenses we have.
A Real Drive, Step by Step, Using EPA
To see EPA in action, let’s walk through the first scoring drive from the Clemson vs South Carolina game. Clemson opened the scoring with a 10-yard touchdown run by Adam Randall on a seven-play drive.
Starting Point
1st and 10 at Clemson 22
Most college football EP models assign this situation an expected points value of roughly:
EP = +0.9
Play 1
1st and 10 at Clemson 22 — 31-yard pass
New situation: 1st and 10 at SC 47
EP = +2.1
EPA = 2.1 – 0.9 = +1.2
A chunk gain on an early down that more than doubles Clemson’s scoring outlook for the drive.
Play 2
1st and 10 at SC 47 — 2-yard run
New situation: 2nd and 8 at SC 45
EP = +2.0
EPA = 2.0 – 2.1 = -0.1
A modest gain, but it slightly reduces leverage by creating a longer/off-schedule second down.
Play 3
2nd and 8 at SC 45 — 2-yard run
New situation: 3rd and 6 at USC 43
EP = +1.7
EPA = 1.7 – 2.0 = -0.3
Same yardage as the previous run, but the tougher down and distance makes the situation slightly worse in EPA terms.
Play 4
3rd and 6 at SC 43 — 26-yard pass
New situation: 1st and 10 at SC 17
EP = +4.8
EPA = 4.8 – 1.7 = +3.1
This is the drive-defining play, flipping Clemson from fringe scoring range into a near-certain touchdown opportunity.
Play 5
1st and 10 at SC 17 — 5-yard run
New situation: 2nd and 5 at SC 12
EP = +5.1
EPA = 5.1 – 4.8 = +0.3
An efficient run that keeps Clemson ahead of the chains in the red zone.
Play 6
2nd and 5 at SC 12 — 2-yard run
New situation: 3rd and 3 at SC 10
EP = +4.7
EPA = 4.7 – 5.1 = -0.4
Even positive yardage can lower expected points if it creates a higher-leverage third down near the goal line.
Play 7
3rd and 3 at SC 10 — 10-yard run for a TD
Touchdowns are assigned an Expected Points value of +7.0.
EPA = 7.0 – 4.7 = +2.3
The drive cashes in, converting field position and leverage into points.
Bringing It All Together
Across seven plays, Clemson moved from a position of scoring about +0.9 points to a 7-point touchdown. EPA shows not just that Clemson scored, but how each play changed the offense’s scoring outlook along the way.
Key takeaways from this drive:
- Positive EPA swings come from plays that rapidly improve field position or convert key downs
- Identical yardage runs can produce different EPA depending on down and distance
- Explosive gains and scoring plays create the largest EPA jumps
Why EPA Matters
Expected Points Added gives us a way to evaluate football plays in context. By anchoring every snap to down, distance, and field position, EPA tells us how much each play actually changes a team’s chance of scoring. It rewards efficiency, captures explosiveness, and penalizes empty yardage in a way traditional stats cannot.
EPA isn’t perfect, and different models will produce slightly different numbers. But the underlying idea is consistent: football is a game of leverage. EPA helps us measure that leverage, one play at a time. For fans trying to understand why games unfold the way they do—not just what happened—EPA is one of the most useful tools available.
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