10 players we rank far above the dynasty consensus, 10 far below.
Our dynasty board against the dynasty consensus, across the 284 players both rank. Read this one carefully, because the two are not trying to produce the same number: our dynasty rank is a three-year production forecast, while a consensus dynasty rank is a market price that also carries contract situation, prospect romance and name value. A gap is often the two answering different questions rather than either being wrong.
Our three-year forecast likes these more than the market does.
| Player | Pos | Our dynasty rank | Consensus | Spread |
|---|---|---|---|---|
| Nolan Schanuel | 1B | 44 | 312 | 1 |
| Cole Young | 2B | 64 | 313 | 1 |
| Miguel Vargas | 3B/1B | 29 | 232 | 146 |
| Taj Bradley | SP | 67 | 267 | 4 |
| Alec Bohm | 3B/1B | 134 | 315 | 1 |
| Ryan Weathers | SP | 102 | 283 | 2 |
| Colt Keith | 3B/1B | 88 | 269 | 4 |
| Bryson Stott | 2B/3B | 119 | 296 | 1 |
| Nick Pivetta | SP | 63 | 240 | 6 |
| Justin Crawford | OF | 103 | 273 | 2 |
The market is paying more than our forecast supports. Worth knowing that our dynasty board runs colder than the consensus overall, the median player here is ranked 32 places higher by the market, so this side of the list is longer by construction, not by conviction.
| Player | Pos | Consensus | Our dynasty rank | Spread |
|---|---|---|---|---|
| Jhostynxon Garcia | OF | 318 | 1234 | 1 |
| Connelly Early | SP | 244 | 1080 | 165 |
| Emmanuel Rodriguez | OF | 205 | 976 | 8 |
| Hayden Birdsong | SP+RP | 320 | 961 | 1 |
| Triston Casas | 1B | 280 | 847 | 2 |
| Christian Moore | 2B | 249 | 806 | 4 |
| Colt Emerson | SS/3B | 177 | 710 | 2 |
| Logan O'Hoppe | C | 258 | 787 | 4 |
| Pablo López | SP | 129 | 635 | 1 |
| Shane Bieber | SP | 243 | 731 | 6 |
Our dynasty rank forecasts three years of production. It does not price a contract, a service-time clock, or how much fun a prospect is to own, all of which move a market rank and none of which are production.
The cutoffs are percentiles of the gap spread rather than a fixed number of places. The whole spread is shifted, because our board runs colder than the consensus; a symmetric cutoff would have produced a long list one way and almost nothing the other, which would describe the shift rather than the disagreements.
A gap is not evidence. Our own research is explicit that disagreeing with the market does not by itself make a model right, and this piece reports the direction and size of the disagreement without claiming the market is wrong.
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.
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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.