The site's whole premise is that paper pedigree and results diverge. Here it is tested properly: on the six complete tournaments (2002–2022; 2026 is live and held out), leave-one-tournament-out cross-validation predicts each squad's actual outlast_pct, and asks whether squad pedigree improves on the Elo Monte-Carlo baseline. 192 squads.
The Elo baseline wins. Adding squad market value and pedigree makes out-of-sample forecasts worse, not better — MAE rises from 0.256 (Elo alone) to 0.273 (Elo + Paper Index). Every candidate that prices in squad value lands at best level with, usually below the ratings-only model. The lock's Elo→Poisson method isn't a shortcut — it's what the data supports.
Fit on five tournaments, predict the sixth, pooled. The Elo baseline is the raw MC sim (no fitting). Predictions clipped to [0,1].
| Model | Form | MAE | RMSE | ρ |
|---|---|---|---|---|
| Elo baseline | the Monte-Carlo Elo expectation itself — what /leaderboards uses | 0.2562 | 0.3151 | +0.467 |
| Elo, recalibrated | OLS(actual ~ Elo) | 0.2675 | 0.3138 | +0.465 |
| Elo + Paper Index | OLS(actual ~ Elo + squad-pedigree composite) | 0.2727 | 0.3196 | +0.443 |
| Elo + pedigree vector | OLS(actual ~ Elo + all five z-components) | 0.2722 | 0.3216 | +0.393 |
Squad pedigree correlates with the Elo rating at r = +0.80 — Elo, being results-derived, already prices most of it in. What's left is the Elo residual (over/under-performance), and no squad feature explains it — every correlation below is statistically indistinguishable from zero, and faintly negative: the fancier the squad relative to its rating, the more it tends to underperform. That is the paper-vs-performance cliff, quantified.
| Squad feature | Definition | ρ vs Elo miss | n |
|---|---|---|---|
| Value Momentum | share of squad with rising value | -0.045 | 148 |
| Rust Share | share under 900 season minutes | -0.042 | 192 |
| Paper Index | squad pedigree composite | -0.021 | 192 |
Six tournaments is a small sample; leave-one-tournament-out is the fair unit (no within-tournament
leakage) but the confidence bands are wide. This says squad value adds no bankable signal over Elo at World
Cup scale — not that squad quality is irrelevant to football. 2026's rows settle once the tournament finishes;
the fit here never uses them. Method: pure-Python OLS, deterministic — see scripts/model_squad_v2.py.
Elo-baseline over/under → · the paper-vs-performance scatter → · method