Matt Bowman is a RP projected for 0 fantasy points on 0 IP, 0 K, 4.2 ERA, 1.318 WHIP by the managr projection engine. Monte-Carlo outcome odds put an elite season at 0% and a bust at 13%.
Projection snapshot: 2026-10-18. Rebuilt daily.
Across 6 seasons with meaningful innings, Bowman's biggest strikeout year came in 2016 at age 25: 52 strikeouts over 68 innings, with 2 wins. Innings across 2019, 2024 and 2025 ran 32, 31 and 25.
Tracking data from 2025: Bowman struck out 15.8% of batters and walked 5.3%, a strikeout-minus-walk rate of 10.5 points, and hitters missed on 17.4% of their swings. Contact against Bowman averaged 88.4 mph off the bat, with a 9.1% barrel rate.
Bowman's projected range runs from a 70-point floor to a 96-point upside, around a 72-point median: a spread of 26 points, or 36% of the median.
By statistical profile, the closest comparisons to Bowman on the board are José Leclerc, Ken Waldichuk and Bobby Miller.
At 35, Bowman 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.
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.
Monte Carlo: N simulated seasons per player using projected mean + uncertainty, producing P10 / P50 / P90 distribution and bust%.
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VAR: Value Above Replacement at the player's primary position. Replacement levels are floored per position so SS scarcity doesn't artificially inflate stable middle infielders.
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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The most important governance tools. Output renders in Validation.
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The smallest model that retains predictive power. Every feature beats the burden of proof.
Live pipeline status and data freshness.
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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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A growing collection of fantasy baseball studies. Each card opens a detail page with the methodology, the data sources, and (where the data exists today) live computed findings.
Active player injuries, refreshed from upstream sources.
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