{
  "artifacts_reviewed": [
    "analysis.json",
    "article.md",
    "provenance.json",
    "research-memo.json",
    "data/next-season-pairs.csv",
    "data/walk-forward-predictions.csv",
    "data/regression-quintiles.csv",
    "data/attrition-by-season.csv",
    "figures/forecast-error.png",
    "figures/regression-quintiles.png"
  ],
  "created_at": "2026-08-31T01:08:54.864860+00:00",
  "decision": "revise",
  "findings": [
    {
      "check": "calibration",
      "evidence": "The article states the expected-TD baseline 'won' in 100.0% of 5,000 resamples, but this is a bootstrap over a single paired sample of 649 observations, not 5,000 independent tests. Reporting this as a win rate without qualification overstates the strength of evidence; the bootstrap interval (0.18\u20130.45) already conveys the appropriate uncertainty.",
      "recommendation": "Remove or heavily qualify the 100% win-rate claim, e.g., 'the bootstrap interval excludes zero' rather than presenting it as a replication count.",
      "severity": "warning"
    },
    {
      "check": "selection_effects",
      "evidence": "The article discloses that the test conditions on players returning to \u226540 targets, but does not state the direction of the resulting bias: high-TD players who regress hard are more likely to drop below the threshold and be excluded, which biases the estimated regression toward zero, while low-TD players who improve are more likely to clear it, biasing in the opposite direction. The net effect on the headline MAE comparison is not obvious and is not discussed.",
      "recommendation": "Add a sentence noting that the survivor conditioning affects the two quintile tails asymmetrically and that the headline forecast comparison may be biased in either direction.",
      "severity": "warning"
    },
    {
      "check": "claims_vs_estimand",
      "evidence": "The locked estimand is the 'difference in next-season forecast error between prior-year raw receiving TDs and prior-year expected receiving TDs among returning WR/TE seasons.' The article's dek and opening frame this as a test of whether the baseline 'predicts next season better,' which is accurate, but the 'How to use it' section extends to individual-level draft advice ('prefer the one whose prior production depended less on outlier conversion') that goes beyond what an aggregate MAE comparison over survivors supports.",
      "recommendation": "Soften the 'How to use it' section to make clear the evidence supports a population-level tiebreaker heuristic, not an individual-level projection adjustment with known magnitude.",
      "severity": "warning"
    },
    {
      "check": "baseline_fairness",
      "evidence": "Both baselines are deliberately naive and the article is transparent about this. The comparison is fair in the sense that both use the same features window and are evaluated on the same pairs. No issue.",
      "recommendation": "None needed.",
      "severity": "note"
    },
    {
      "check": "temporal_leakage",
      "evidence": "Walk-forward training uses only earlier seasons for each 2018\u20132025 test season, and the 2025 data comes from a separate per-season release retrieved after the legacy snapshot. No leakage detected.",
      "recommendation": "None needed.",
      "severity": "note"
    }
  ],
  "model": "z-ai/glm-5.3",
  "role": "statistician",
  "summary": "The walk-forward design is sound and the headline MAE improvement (2.65 \u2192 2.34, 11.7%) is internally consistent with the reported bootstrap interval, but the article's framing overstates the evidence in three places: it presents the expected-TD baseline as beating raw TDs without acknowledging that both forecasts are weak in absolute terms, the 100% bootstrap win rate is reported without noting it is a resampling artifact of a single paired comparison rather than independent replication, and the quintile narrative implies predictive skill at the individual level that the aggregate correlation (-0.50) does not support. The survivor-bias limitation is disclosed but its direction is not: conditioning on \u226540 targets in the following season mechanically inflates apparent regression for high-TD players who are more likely to fall below the threshold. These are presentation-level issues that do not invalidate the core finding but require revision before publication."
}