Performance Metrics.
WAR, EPA, xG and the composite metrics that compress a season into a decimal.
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Modern sport runs on composite numbers — WAR, EPA, xG — that compress thousands of decisions into one figure. They are genuinely useful and routinely misquoted, usually by people reading the decimal places.
What this field actually measures
- WAR (wins above replacement)
Estimated wins a player adds over a freely available replacement.
ReferencePublic versions from FanGraphs and Baseball-Reference use different models and regularly differ by around a win for the same player-season.
LimitIf two credible models disagree by a win, the third decimal place is decoration.
- EPA per play
Expected points added by a play, given down, distance, and field position.
ReferenceOffensive EPA/play is one of the most stable public predictors of NFL team quality.
LimitIt is descriptive of value created, not of who created it.
- Expected goals (xG)
Probability a shot becomes a goal, from historical shots of similar type.
ReferenceSingle-match xG is noisy; the metric stabilises over roughly 10+ matches.
Evidence & metrics
The published work behind the numbers above, and the public datasets you can open to check us. If a claim can’t be traced here, we don’t print it.
Citations
The Book: Playing the Percentages in Baseball
Tango, Lichtman & Dolphin · 2007
Formalised linear-weights run estimation and the regression needed before small samples mean anything.
Expected Points Added and nflfastR model documentation
Baldwin, Carl et al. — nflfastR · 2020–present
Publishes the open EPA and win-probability models used across public NFL analysis, with their inputs and error bounds.
Datasets
nflverse · free
Every NFL play since 1999 with EPA, WP, and win-probability fields.
FanGraphs and Baseball-Reference WAR leaderboards
FanGraphs / Sports Reference · free
Two independently modelled WAR implementations for the same player-seasons.
Still open
- How should credit be split between player, scheme, and teammate in any single-number metric?
- What sample size makes each public metric stable enough to argue about?
- Should public models publish their uncertainty intervals by default? (Yes.)
What we won’t say
We will not settle a debate by citing one number to two decimals. A metric with an error bar the size of the argument has not ended the argument.
- VO-1Evidence before wit
- VO-2Punch at claims, never at people
- VO-3Say the uncertainty out loud
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