Michael Fulmer is a New York Yankees RP projected for 30 fantasy points on 14.5 IP, 19 K, 3.3 ERA, 1.387 WHIP by the managr projection engine. Monte-Carlo outcome odds put an elite season at 0% and a bust at 13%.
Projection snapshot: 2026-09-28. Rebuilt daily.
Michael Fulmer's 2026 projection is built on 15 innings out of the bullpen.
Across 7 seasons with meaningful innings, Fulmer's biggest strikeout year came in 2016 at age 23: 132 strikeouts over 159 innings, with 11 wins. Innings across 2021, 2022 and 2023 ran 70, 64 and 57.
Fulmer's projected range runs from a 28-point floor to a 46-point upside, around a 30-point median: a spread of 18 points, or 60% of the median.
By statistical profile, the closest comparisons to Fulmer on the board are Evan Phillips, Keegan Akin and Pablo López.
At 33, Fulmer is at the age where velocity and durability become the leading risks for pitchers.
This report is generated from the managr engine's 2026-09-28 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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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.
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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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