One archive · Two time windows
Ask whether later settled forecasts behave differently from earlier ones
The engine sorts accepted matches chronologically, assigns the latest rows to the recent window, leaves the declared gap out of both estimates, and uses the immediately preceding rows as the reference. Every reported difference is recent minus reference.
- match identity
- A stable unique key prevents one fixture appearing twice
- publication time
- Zoned timestamp strictly before kick-off
- kick-off time
- Orders the accepted record without inspecting the result
- model version
- Required provenance; a version change can confound a time comparison
- 1X2 probabilities
- Complete home, draw and away distribution on one file-wide scale
- outcome
- One settled home, draw or away result
- league
- Optional composition label; missing labels stay explicit
- window settings
- Reference, gap and recent row counts are part of the receipt
- SHA-256
- Binds the report to exact local bytes, not to historical publication
Three signals, three meanings
Do not collapse error, prediction shape and sample mix into one badge
A recent loss increase is outcome-based performance evidence. A changed mean predicted 1X2 mix describes model outputs. A changed league or result mix describes the evaluated sample. They can move together without having the same cause.
Did forecast error change?
Compare class-averaged Brier score and log loss first. Hit rate and ten-bin ECE add context, not a replacement verdict.
Did the probabilities move?
Total-variation distance compares the mean predicted home, draw and away mix without calling the movement good or bad.
Did the tested football change?
Outcome and league distributions can make an aggregate score move even when the underlying forecasting process did not degrade.
Directional comparison
Recent minus reference keeps every difference interpretable
Positive Brier or log-loss differences mean the later window has higher average error. A negative hit-rate difference means fewer uniquely ranked top choices were correct. ECE is the sample-weighted mean absolute accuracy–confidence gap across ten fixed confidence bins: it is bin-sensitive, is not a proper scoring rule and receives no universal pass line.
- 01
Full probabilities
Tied top labels still receive Brier and log loss; they leave only hit-rate and top-label calibration denominators.
- 02
Visible gap
Gap rows separate the adjacent scored windows and remain counted instead of disappearing.
- 03
Approximate intervals
The displayed 95% intervals assume independent rows and can be too narrow under team, league or serial dependence.
- 04
No threshold hunt
The browser does not search for the split, window size or metric that produces the most dramatic result.
Causal boundary
Performance drift is not the same as covariate or concept drift
Covariate drift concerns the distribution of model inputs. Concept drift concerns a changing relationship between inputs and outcomes. This CSV contains probabilities, results and optional labels—not the complete feature stream—so it can expose a performance change but cannot identify either hidden process.
- Version changes confound time.If the model version differs within or between windows, the conservative verdict is downgraded instead of crediting age, retraining or football conditions.
- Composition changes confound averages.A league, outcome or mean prediction-mix shift can change aggregate loss without proving model degradation.
- Short histories amplify noise.A small recent window can move sharply after a few unusual matches. Version 1 uses 30 settled rows per window as a disclosed editorial floor for a directional label, not a universal statistical threshold.
- Monitoring is not an action policy.The result does not automatically recommend retraining, changing a threshold, stopping a model or placing a wager.
Primary literature
Proper scores and drift terminology set the claim boundary
These publications ground probability scoring, calibration and drift language. They do not certify this implementation, an uploaded archive or the cause of an observed change.
- Brier (1950), verification of probability forecasts.Original paper and DOI
- Gneiting & Raftery (2007), strictly proper scoring rules.Journal paper and DOI
- Gneiting, Balabdaoui & Raftery (2007), calibration and sharpness.JRSS Series B paper and DOI
- Gama et al. (2014), concept-drift adaptation survey.ACM Computing Surveys paper and DOI
- Franck et al. (2025), monitoring probability calibration over time.Open preprint and DOI
Cite this tool as: Football Proof AI. “Football Prediction Model Drift Monitor.” Version football-model-drift/1.0.0, 13 July 2026, https://footballproofai.com/tools/football-prediction-model-drift-monitor. Editorial technical note; not externally peer reviewed.