Matt Krook is a RP projected for 23 fantasy points on 8.4 IP, 8 K, 4.1 ERA, 1.373 WHIP by the managr projection engine. Monte-Carlo outcome odds put an elite season at 0% and a bust at 14%.
Projection snapshot: 2026-10-18. Rebuilt daily.
Matt Krook's 2026 projection is built on 8 innings out of the bullpen.
Matt Krook has no season of regular major-league playing time on record, so the forecast leans on the model's priors rather than a big-league track record, and the projected range below is correspondingly wide.
Krook's projected range runs from a 5-point floor to a 36-point upside, around a 23-point median: a spread of 31 points, or 135% of the median.
By statistical profile, the closest comparisons to Krook on the board are Brandan Bidois, Brody Hopkins and Nate Lavender.
At 31, Krook 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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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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