Six questions before interpretation
| Risk | Audit question |
|---|---|
| Definition | What exactly was counted or estimated? |
| Coverage | Which matches, leagues, players and events are missing? |
| Sample | How uncertain is the rate or ranking? |
| Context | Do opponent, venue, score state and role explain the number? |
| Causality | Is the statistic an outcome, a proxy or an intervention? |
| Validation | Did the method work on matches it could not see? |
Measurement is not the event itself
Two providers can disagree because they classify possession, pressures, assists or shots differently. Modelled fields add assumptions about targets, features and training data. Preserve provider, version, retrieval date and corrections.
Outcome variation is not model proof
A team can create 1.8 xG and score zero without the metric being false; a low-probability outcome can occur. Conversely, a correct winner does not validate the probability assigned. Calibration guidance explains why probabilistic forecasts are assessed across groups of future cases.
Leakage can make weak methods look strong
Using corrected lineups, closing information or season aggregates unavailable at prediction time contaminates a backtest. scikit-learn's common-pitfalls guide documents leakage and inconsistent preprocessing. Time-stamp every input and reproduce the historical availability delay.
Extreme numbers need uncertainty
Regression-to-the-mean research shows why extreme repeated measurements can move closer to a mean under measurement error and natural variation. It does not license an automatic reversal forecast.
Minimum reporting block
- Question and target outcome.
- Provider and field definitions.
- Inclusion, exclusion and missing-data rules.
- Sample count and uncertainty.
- Baseline and out-of-sample metric.
- Known failure groups and update date.
Football benchmark research reinforces that model results depend on datasets, features and evaluation design.
Red-team an attractive finding
Before publishing a strong pattern, ask another analyst to try to break it:
| Challenge | Diagnostic |
|---|---|
| Definition sensitivity | Recalculate with another defensible field rule |
| Sample sensitivity | Remove the largest match or player contribution |
| Time sensitivity | Repeat by season and rolling cutoff |
| Context sensitivity | Split venue, strength, score state or competition |
| Leakage | Reconstruct exactly what was knowable at prediction time |
| Baseline | Compare with a simpler model or historical rate |
A finding that changes direction under a minor defensible choice should be presented as uncertain. A stable association can still be non-causal, but the sensitivity record tells readers how much rests on one implementation.
Publish enough to reproduce the conclusion
Name the source and version, eligibility rules, time period, missing-data treatment, formulas, train-test split and evaluation metric. Include denominators beside rates and the number of forecasts beside performance summaries. Keep unsuccessful specifications in the analysis log.
Peer-reviewed football model-evaluation research supports the baseline, time-order, and reporting protocol used below.
For human readers, lead with the decision that the evidence supports, then state the boundary in ordinary language. "This model improved the Brier score in two later seasons" is more useful than "advanced analytics proves the edge" because it identifies what was measured and where the claim stops.
Related resources
Continue with statistical model approaches or training and testing.
Continue learning
- Next guide: Football Data Providers
- Related guide: Football Prediction Models
Assumptions and limitations
This checklist does not rank providers or models and cannot identify every bias. Access to richer tracking data can improve context while introducing new measurement and coverage constraints.
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