{
  "artifacts_reviewed": [
    "/home/runner/work/fourth-down-labs/fourth-down-labs/artifacts/touchdown-regression/2026-08-31-touchdown-regression-v1/figures/regression-quintiles.png",
    "/home/runner/work/fourth-down-labs/fourth-down-labs/artifacts/touchdown-regression/2026-08-31-touchdown-regression-v1/figures/forecast-error.png",
    "/home/runner/work/fourth-down-labs/fourth-down-labs/artifacts/touchdown-regression/2026-08-31-touchdown-regression-v1/figures/2024-overperformers.png"
  ],
  "created_at": "2025-01-01T00:00:00Z",
  "decision": "revise",
  "findings": [
    {
      "check": "internal_consistency",
      "evidence": "The research_memo specifies estimand 'OLS slope of winsorized next-season TD rate on current-season TD rate with position fixed effects' and required outputs 'locked forecasts, evaluation report, attrition table, deviation log', but the analysis object contains a ridge-regularized expected-TD model with quintile tables, a residual-vs-next-change correlation, and a bootstrap uncertainty block. No slope estimate, attrition table, or deviation log is present in the bundle.",
      "recommendation": "Reconcile the memo and the analysis into one coherent study, or explicitly document the deviation from the locked design (post-hoc modification is listed as a risk but no deviation log is supplied). Report the memo-specified estimand or amend the memo.",
      "severity": "blocking"
    },
    {
      "check": "survivorship_bias",
      "evidence": "next_season_pairs=551 out of 1478 player-seasons, and evaluation 'conditions on returning to at least 40 targets'. The 8.9% MAE improvement is computed only on the returning subsample. Players who regress hardest (injury, benching, poor play) are excluded, which plausibly inflates the apparent regression-to-the-mean effect and the model's advantage over the naive last-year baseline, since the naive baseline is weakest precisely for players whose role collapses.",
      "recommendation": "Quantify attrition by quintile (the missing attrition table) and report MAE improvement under an intention-to-treat convention, e.g., carry forward last-year actuals for non-returners or impute a conservative floor, so the headline is not conditional on survival.",
      "severity": "blocking"
    },
    {
      "check": "baseline_fairness",
      "evidence": "last_year_actual_mae=2.61 vs expected_tds_mae=2.38. The naive baseline is raw last-year TD count, not last-year TD rate per target or per route. Because TD counts scale with opportunity, part of the model's edge may come merely from re-weighting by volume (targets/yards) rather than from any regression signal. A per-target-rate baseline is the natural competitor and is absent.",
      "recommendation": "Add a last-year TDs-per-target (or per-air-yard) baseline to isolate whether the improvement comes from regression modeling or simply from opportunity normalization.",
      "severity": "blocking"
    },
    {
      "check": "stale_data",
      "evidence": "seasons=[2014,2024] and the snapshot 'ends with the 2024 season'; the bundle is dated 2026-08-31. The study is two full seasons stale at publication. League environment (end-zone passing share, target distribution, TE usage) has plausibly shifted since 2024, and the memo's own risk list includes schema drift.",
      "recommendation": "State prominently that no post-2024 data informs the result, and either refresh with 2025-2026 data or bound the staleness risk explicitly in the headline framing.",
      "severity": "blocking"
    },
    {
      "check": "scope_overreach",
      "evidence": "The population is WR/TE with >=40 targets, but the model features and quintile analysis make no positional distinction in the reported outputs, and the memo calls for position fixed effects that do not appear in the analysis. RBs, low-volume receivers, and players below 40 targets are outside scope, yet fantasy consumers commonly apply TD-regression logic to exactly those groups.",
      "recommendation": "Either report position-split results (WR vs TE) or add explicit guardrails that the finding does not extend to RBs, low-target players, or per-game fantasy formats.",
      "severity": "blocking"
    },
    {
      "check": "effect_size_context",
      "evidence": "mae_delta_tds=0.23 TDs with 95% CI [0.087, 0.378]. A quarter-TD-per-season average improvement is small relative to typical fantasy TD variance and to the RMSE (~3.0 TDs). The bootstrap P(better)=0.9994 describes sign consistency, not practical magnitude.",
      "recommendation": "Frame the headline around the CI and per-player magnitude, and caution that a statistically reliable edge of ~0.2 TDs/season may not change fantasy decisions.",
      "severity": "warning"
    },
    {
      "check": "quintile_interpretation",
      "evidence": "The 'Most over' quintile (over_expected=+3.21) still scores next_tds=5.43 vs expected 5.04, i.e., next-season TDs do not fully collapse to the model's expectation; and the residual-vs-next-change correlation is only -0.465, leaving most variance unexplained. Mean-reversion is partial, not deterministic.",
      "recommendation": "Avoid headline language implying 'overperformers will regress to expected'; state that roughly half the residual variance persists into the next season.",
      "severity": "warning"
    },
    {
      "check": "aggregation_masking",
      "evidence": "Quintile means (n=110-111 each) can hide heterogeneous subgroups: elite red-zone dominators may sustain overperformance while low-aDOT possession receivers regress differently. Season-level features (no red-zone or route context, per limitations) cannot distinguish these.",
      "recommendation": "Add a heterogeneity check (e.g., interaction of over_expected with air-yard share or position) before encouraging player-level application.",
      "severity": "warning"
    },
    {
      "check": "multiple_testing",
      "evidence": "walk_forward_test_seasons=[2018,2024] with 950 predictions; the bootstrap seed (4444) and sample count are fixed, and no correction or pre-registration of the primary comparison is documented beyond the memo.",
      "recommendation": "Confirm the MAE comparison was the pre-specified primary endpoint and that walk-forward folds were not chosen post hoc; report per-season MAE to show stability across 2018-2024.",
      "severity": "warning"
    }
  ],
  "model": "claude-opus-4-6",
  "role": "adversarial_reviewer",
  "summary": "The headline claim that expected touchdowns beat last-year actuals as a next-season predictor is directionally supported by the walk-forward MAE improvement (8.9%, bootstrap P(expected better)=0.9994), but the evidence bundle contains material inconsistencies, unaddressed confounds, and scope limits that a fantasy player could easily over-apply. The bundle conflates two different studies (a quintile regression analysis and a separate research memo describing a slope-based estimand with position fixed effects), the survivorship conditioning is acknowledged but its magnitude is never quantified, and the headline comparison may be biased in favor of the model because both forecasts and the 'last-year actuals' baseline are evaluated only on players who cleared the 40-target threshold again. Revision is needed before this can be treated as a defensible, decision-grade result."
}