Analytics.
The discipline of asking, relentlessly, how much of what we just saw was noise.
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Analytics changed sport by asking a boring question relentlessly: how much of what we just saw was signal? Most disputes about analytics are actually disputes about sample size.
What this field actually measures
- Stabilisation point
Sample size at which a statistic starts reflecting skill more than noise.
ReferenceVaries enormously by metric — some baseball rates stabilise in dozens of events, others need multiple seasons.
- Regression to the mean
Extreme results drifting toward average with more data.
ReferenceThe default explanation for most breakout and collapse narratives.
- Out-of-sample performance
How a model does on data it never saw.
ReferenceThe only test that separates a model from a description of the past.
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
Wins Above Replacement model documentation
FanGraphs and Baseball-Reference · ongoing
Two published WAR implementations differ in fielding and pitching inputs, so the same season can differ by around a win.
nflfastR EPA and win-probability methodology
Baldwin & Carl — nflverse · 2020–present
Open-sources the models behind public EPA/play analysis, including their training data and limitations.
Datasets
Retrosheet · free
Event-level MLB data going back over a century.
StatsBomb · free
Event data with shot freeze frames for selected competitions.
Still open
- How should public models communicate uncertainty to a general audience?
- Where does tracking data add genuine information over box-score aggregates?
- What decisions have actually changed because of analytics, and what only sounds different?
What we won’t say
We will not present a 12-game sample as a trend. It is a sample, and it is small.
- VO-1Evidence before wit
- VO-2Punch at claims, never at people
- VO-3Say the uncertainty out loud
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Where Analytics overlaps.
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