Two things shipped this cycle: a projection engine that clears every accuracy bar we set for it, and a language layer that reads this week's news into those projections — the piece a frozen, cutoff-dated chatbot fundamentally cannot do. Below: what we achieved, and exactly how we verified each claim. (This is the exec/status report. For how the prediction engine works, read How we predict; for the exact math, the methodology white paper.)
We hold ourselves to nine pre-declared targets across the stats fantasy players actually care about (passing, rushing, receiving yards; receptions; touchdowns). All nine pass — on data the model has never seen, scored the only honest way for this problem.
season < S or
(season == S and week < W), predict week W.
| Position | Our model | RotoWire | Gap |
|---|---|---|---|
| RB | .700 | .741 | −.041 |
| WR | .680 | .690 | −.010 (parity) |
| TE | .623 | .637 | −.014 |
Every general AI model has a training cutoff. Ask one about Sunday's inactives or a coach's Wednesday practice comment and it's guessing from stale memory. Our edge: a language model on our own hardware turns live beat-writer news into structured signals that feed the projection board, which refreshes daily. Same news the market reacts to — read into a calibrated model, not improvised from headlines.
SKIP. The output is deterministic JSON the projection bridge consumes with
bounded, clamped adjustments (a single news item can never swing a projection more than a fixed cap).
The hardest part of a prediction company isn't getting a good number; it's knowing whether to trust it. Our discipline: an independent reviewer tries to break every claim before we ship it. Here's exactly what that looked like this cycle.
Same headline number at the start and end — but the second one is real. That gap is the whole point: most teams would have shipped the first 93.8% and never known.
The gate now scores news direction — a model that flips "more work" to "less work" is marked wrong, because that flips the projection the wrong way.
Every synthetic training example is round-tripped back through the model under the real rules and kept only if it reproduces the intended answer — no "correct by assumption."
We replay a full past season and show, week by week, where the board was right and where it wasn't. Nobody launches a projection product by publishing its errors. We do.
Continuous integration re-runs the product tests on every code change — and already caught real deploy-blocking bugs before they could reach anyone.
direction + team. Exact-match scored only
event / player / tier. A snap-up ↔ snap-down flip — which moves the board the wrong way — counted
as a pass. Fix: score all five fields. The teacher model fell 93.8% → 65.6% on the honest gate;
after a prompt rebuild + repairing 8 exam items whose "correct" team wasn't actually inferable from
the text, it recovered to a legitimate 93.8% (30/32, 0 FP, 0 FN).A defensible edge a general-purpose AI can't copy: a calibrated model that refreshes on live news daily, honest calibration as the trust brand, and models we own outright at near-zero marginal cost.
Projections that show their work — a real range, a confidence read, a boom/bust probability, and a published record of hits and misses. No black box, no false precision.
Walk-forward CV, a mandatory anti-leakage gate, adversarial self-audit, held-out acceptance gauntlets, and CI on every change. Every claim on this page traces to a re-runnable test.