Robert Stephenson is a Los Angeles Angels RP projected for 0 fantasy points on 0 IP, 0 K, 3.9 ERA, 1.368 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, Stephenson's biggest strikeout year came in 2017 at age 24: 86 strikeouts over 85 innings, with 5 wins. Innings across 2021, 2022 and 2023 ran 46, 58 and 52.
Stephenson's projected range runs from a 74-point floor to a 108-point upside, around a 92-point median: a spread of 34 points, or 37% of the median.
By statistical profile, the closest comparisons to Stephenson on the board are Justin Wilson, Reed Garrett and Edwin Uceta.
At 33, Stephenson 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.
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Issues from the most recent data refresh, if any.
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