Where Managr sees more value than the market. The players to target, and the stars you should not let anyone talk you out of.
Every projection is a disagreement with the market. Managr's mispricing score measures the size of that gap, how much more the model values a player than where they are being drafted and rostered. These eight carry the biggest positive edges on the board.
The model's top buy tier, ranked by mispricing edge (0 to 100), its disagreement with the market. Tap a name for the profile.
| Name | Proj | Buy Edge |
|---|---|---|
| James Wood | 510 | 82 |
| Miguel Vargas | 466 | 80 |
| Ben Rice | 449 | 79 |
| Shohei Ohtani | 800 | 78 |
| Yordan Alvarez | 566 | 77 |
| Pete Crow-Armstrong | 430 | 76 |
| Junior Caminero | 485 | 75 |
| Xavier Edwards | 426 | 75 |
The gettable value is in the names you can still pry loose. Miguel Vargas (80), Ben Rice (79), Pete Crow-Armstrong (76), and Xavier Edwards (75) all grade as elite buys the market has not caught up to. These are the trade targets and the waiver adds, the players whose price still lags their projection.
The other group is the stars: James Wood (82), Ohtani (78), Alvarez (77), Caminero (75). You are not buying these low, nobody is selling them. Their place on this list means the opposite. The model still values them above where they are rostered, so do not let a slow week or a nervous leaguemate talk you into moving them. Hold.
Chase the gettable edges (Vargas, Rice, Crow-Armstrong, Edwards); hold the stars the model still loves (Wood, Ohtani, Alvarez, Caminero). The mispricing edge is the size of the model's disagreement with the market, so act on the biggest gaps.
Curated picks where the model has the highest decision conviction. Updated every render.
Run rankings to populate Market Edge.
Run rankings first.
Mark a player as drafted to get a recommendation.
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%.
Confidence layers: four orthogonal multipliers: Roster (depth chart), Role (PT certainty), Sample (career PA), Market (consensus alignment). The Audit tab shows all four for every player.
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.
Projects every historical batter-season from prior data only. Reports RMSE, MAE, Spearman ρ, hit rates. Populates the pair pool other validators use.
Walk-forward over historical seasons. Reports RMSE, MAE, Spearman ρ, top-N hit rates.
Distribution, uncertainty, per-archetype performance, calibrated tiers, draft sim.
Load any rival projection or ADP source. Auto-detects player_name + rank / adp / projected.
Where the model finds value the market is missing.
Curated picks from current rankings.
Where the model has measurable advantage.
The safety layer. Realism, false-confidence, forensics - what to remove.
The most important governance tools. Output renders in Validation.
Per-era ablation, error clusters, bias, correlations, removals. Each tells you what to remove. Run walk-forward first.
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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MVP, Cy Young, Rookie of the Year - historical winners with their stats, plus model-predicted current-season winners.
Predictions from the current 2026 projection model. Top candidates per award based on projected fantasy points + advanced-stat underlying. League assignment from current team.
Coaching rosters for every MLB team plus a multi-year Fantasy Points Above Average ranking of every MLB coach.
Findings article + all-time top-100 performer leaderboard from every World Baseball Classic (2006-2026).
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.
| Player | Team | Pos | Status | Explanation | Replacement |
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Enter players for each side, one per line. Values use your scoring weights and injury-adjusted projections.
| Player | Pos | ProjPts | Status |
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| Player | Pos | ProjPts | Status |
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Every signal, formula, and data source the model uses for player evaluation - organized by product.
Configure your draft session. This stays on this device only.