Daysbel Hernández is a RP projected for 0 fantasy points on 0 IP, 0 K, 4 ERA, 1.447 WHIP by the managr projection engine. Monte-Carlo outcome odds put an elite season at 0% and a bust at 18%.
Projection snapshot: 2026-10-18. Rebuilt daily.
Hernández's one season with meaningful innings came in 2025 at age 28: 37 innings with 33 strikeouts. A single season is a thin base for a projection.
Tracking data from 2025: Hernández struck out 20.2% of batters and walked 17.8%, a strikeout-minus-walk rate of 2.4 points, and hitters missed on 29.2% of their swings. Contact against Hernández averaged 89.4 mph off the bat, with a 9.1% barrel rate.
Hernández's projected range runs from a 75-point floor to a 105-point upside, around an 81-point median: a spread of 30 points, or 37% of the median.
By statistical profile, the closest comparisons to Hernández on the board are Adam Mazur, José Castillo and Richard Fitts.
At 29, Hernández is in the age range where pitchers typically hold their skills.
This report is generated from the managr engine's 2026-10-18 snapshot and rebuilt daily.
Curated picks where the model has the highest decision conviction. Updated every render.
Run rankings to populate Market Edge.
Run rankings first.
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Studies and awards - the research-side surfaces of the managr platform.
Live snapshot of the projection system.
Backtest, parsimony, ablation, and benchmark - one synthesized report. Output renders in Validation.
Final Value = Raw Projection × MasterConfidence × TeamContext. Each layer answers one question - no overlap.
Recency weighting: prior seasons contribute by recency (more recent = more weight) and sample size (more PA = more weight).
Shrinkage: observed rates regress toward position-specific population means by an amount inversely proportional to sample size. Catches small-sample outliers.
Quality-of-contact adjustments: xwOBA, barrel rate, and bat-tracking metrics replace luck-driven outcome stats with skill-driven ones.
PT modeling: projected PA is anchored to workload tier (full-time, regular, platoon, backup catcher) using historical role distributions.
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Tune component weights against historical seasons. Stored in session.
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Walk-forward backtests, calibration quality, and benchmark comparisons. Run walk-forward first - most tools depend on its pair pool.
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Last known status of each underlying data source.
Source status will appear once data has been refreshed.
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