Performance
Batter Hits is running below the rate it converts without us (-7.7 pts, n=53).
Overall hit rate is 60.2%, +7.1 pts over baseline — down 2.9 points on the previous 7 days, which ran +10.5 pts.
Performance summary
The model is modestly ahead of baseline over this period (+7.1 pts on 98 graded predictions).
What's working
Every market ranked by the edge it holds over the rate its own event happens without us — not by how often it converts. The tables further down read served predictions only, so the “all” figure beside each edge is the check that a market which never clears the floor is not mistaken for one that does not work.
Trust board
The page’s verdict, from edge over baseline, sample size and recent trend together — never from hit rate alone. A tier with nothing in it is not shown.
- Batter Hits-7.7 pts n=53
- SP Strikeouts+43.4 pts n=14improving, +40.2 pts
- Points+35.3 pts n=24
- SP Hits Allowed+21.8 pts n=11
- Rebounds+19.8 pts n=20
- Batter Ks+18.2 pts n=5
- Assists+11.4 pts n=19
Score calibration
The scoring system is worth having only if a higher score means a better outcome. Each band is measured against the base rate of the props it actually holds — the top band is almost purely 1+ hit, so a raw-rate reading of this table would credit it for carrying a common event.
Not enough graded predictions yet to judge whether higher scores perform better.
The 75–79, 80–84, 85–89, 90–94, 95–98 and 99–100 bands remain too small to read.
Where the model has edge
Segments by market, ranked by lift over their own base rate. Anything under the minimum sample is separated and labelled rather than ranked alongside — a high percentage on twenty predictions is not a discovered edge.
Over vs Under
Under signals are outperforming Over by 37.7 points of lift (+43.2 pts against +5.5 pts). The under side has only 4 graded predictions, so treat the gap as an observation.
Consistency
Performance has been stable around 60%–63% across every window with a real sample.
Market pulse
Each market’s recent slates against its own period average. The trend column is the row’s answer; the grid behind it fades with age.
By month
Model version performance
2 of 7 current model versions are outperforming the versions they replaced; MLB Batter Hits, MLB SP Strikeouts, WNBA Points, WNBA Rebounds and WNBA Assists are behind. WNBA Rebounds and MLB Batter Ks rest on a sample too small to call.
Methodology
- Edge over baseline, never a raw hit rate
- Every “vs base” figure is the observed hit rate minus how often that exact prop’s event happens on its own, for the population that could plausibly have been offered it. A batter getting 1+ hit happens ~61% of the time unprompted; a starter striking out 8+ happens ~13% of the time. Against one shared average the rare-event markets look bad no matter how well they are picked.
- Opportunity Score is a ranking signal, not a probability
- 70 is the publication floor. A 93 is not “93% likely” — it is an estimate of edge over the market’s own base rate. Observed hit rates on this page are outcomes, not forecasts.
- Sample size is a headline, not a footnote
- Under 30 graded predictions a segment is separated and marked rather than ranked. Predictions inside one slate are correlated, so a run of good days is less independent evidence than its count suggests.
- Cohorts
- All qualifying is every published prediction scoring 70+. Featured is the eight carried on Today. Other qualifying is the remainder — the split exists to check that ranking, not just scoring, is doing something.
- Calibration reads the current engine only
- A superseded scorer’s calibration is not a fact about the one running today. Pooling engines once made the top band look anti-predictive when it was not. Per-version records stay in the model table above.
- What is not here
- No odds are consumed anywhere on this page. Total bases and batter walks are excluded — both were retired for being unservable rather than for being wrong. Voids and pending predictions are excluded from every hit rate.