Projected points tell you who scores the most. League-Winning Score tells you who wins you the league. They are not the same list.
Projected points tell you who scores the most. They do not tell you who wins you the league. Those are different questions, and the gap between them is where titles are decided.
Managr's League-Winning Score ranks players by championship equity, how much a player lifts your title odds, blending the central projection with ceiling, talent tier, and category impact. A steady 500-point bat and a volatile 500-point bat are not equal: the one with the higher ceiling wins more championships. Here is the current top of the board.
Top of the board by League-Winning Score. Numbers from the Managr engine; tap a name for the full profile.
| Name | Proj | LW Score |
|---|---|---|
| Shohei Ohtani | 800 | 1045 |
| Yordan Alvarez | 566 | 722 |
| Junior Caminero | 485 | 661 |
| James Wood | 510 | 628 |
| Otto Lopez | 463 | 609 |
| Matt Olson | 455 | 601 |
| Ketel Marte | 431 | 595 |
| Bryce Harper | 450 | 594 |
Ohtani sits in a tier of his own, a 1045 League-Winning Score on an 800-point projection and an 84% shot at a superstar season. Behind him the board mixes proven bats (Alvarez, Olson, Marte, Harper) with the young ceiling plays that actually swing titles: Junior Caminero (22) and James Wood (23) both crack the top four on upside alone, and Wood carries an 82% superstar-outcome share.
Now look at Matt Olson at number six with just a 21% superstar share. He is on this list for floor and volume, not ceiling. That is the whole point of the score: it values the different ways a player wins you a title, not just the raw total.
League-Winning Score is projected points adjusted for how a player actually wins championships: ceiling, floor, and category impact. When you are deciding who to build around, sort your board by it, not by raw points.
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.
-
-
-
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.
-
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.
-
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 |
|---|
Enter players for each side, one per line. Values use your scoring weights and injury-adjusted projections.
| Player | Pos | ProjPts | Status |
|---|
| Player | Pos | ProjPts | Status |
|---|
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.