Thomas Hatch is a RP projected for 0 fantasy points on 0 IP, 0 K, 4.4 ERA, 1.347 WHIP by the managr projection engine. Monte-Carlo outcome odds put an elite season at 0% and a bust at 17%.
Projection snapshot: 2026-10-18. Rebuilt daily.
Across 3 seasons with meaningful innings, Hatch's biggest strikeout year came in 2023 at age 28: 26 strikeouts over 29 innings, with 1 win. Innings across 2020, 2023 and 2025 ran 26, 29 and 34.
Tracking data from 2025: Hatch struck out 14.5% of batters and walked 11.2%, a strikeout-minus-walk rate of 3.3 points, and hitters missed on 19.2% of their swings. Contact against Hatch averaged 87.6 mph off the bat, with an 8.0% barrel rate.
Hatch's projected range runs from an 81-point floor to a 125-point upside, around a 101-point median: a spread of 44 points, or 44% of the median.
By statistical profile, the closest comparisons to Hatch on the board are Jackson Kowar, Max Kranick and Jordan Leasure.
At 31, Hatch is at the age where velocity and durability become the leading risks for pitchers.
This report is generated from the managr engine's 2026-10-18 snapshot and rebuilt daily.
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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.
Monte Carlo: N simulated seasons per player using projected mean + uncertainty, producing P10 / P50 / P90 distribution and bust%.
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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.
Issues from the most recent data refresh, if any.
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