104 ranked rookies from the 2026 class, 10 worth rostering.
104 players who debuted in 2026 appear on the ranked board, and 10 of them are worth rostering in a twelve-team league. The median rookie is worth 24. That gap is the whole story of a rookie class and it is the part nobody prints: the names get written about in proportion to how exciting they were as prospects, not to what they are producing.
Ranked by what the engine says they are worth right now, not by pedigree. A rookie moves up this list by producing and down by not, which is why it looks different from a prospect ranking.
| Player | Pos | Debut | Value | Redraft rank | Dynasty rank |
|---|---|---|---|---|---|
| Kevin McGonigle | SS/3B | Mar 26 | 296 | #20 | #9 |
| JJ Wetherholt | 2B/SS | Mar 26 | 217 | #71 | #27 |
| TJ Rumfield | 1B/DH/3B | Mar 27 | 215 | #79 | #121 |
| Munetaka Murakami | 1B/3B | Mar 26 | 213 | #38 | #106 |
| Carson Benge | OF | Mar 26 | 199 | #159 | #45 |
| Sam Antonacci | OF/2B/3B | Apr 15 | 192 | #113 | #34 |
| Walbert Ureña | SP | Mar 26 | 188 | #638 | #353 |
| Cole Carrigg | OF | Jun 9 | 182 | #117 | #144 |
| Kazuma Okamoto | 3B | Mar 27 | 152 | #200 | #417 |
| Esmerlyn Valdez | OF/DH | May 22 | 136 | #214 | #216 |
| Justin Crawford | OF | Mar 26 | 131 | #350 | #107 |
| A.J. Ewing | OF | May 12 | 128 | #210 | #97 |
These carry a much better dynasty rank than redraft rank, which is the engine saying the same thing in two timeframes: not much help this year, a real asset over three. In a keeper league they are worth more than the redraft column suggests.
| Player | Pos | Debut | Value | Redraft rank | Dynasty rank |
|---|---|---|---|---|---|
| Konnor Griffin | SS | Apr 3 | 62 | #349 | #49 |
| Cooper Pratt | SS | Jun 16 | 83 | #394 | #61 |
| Jose Fernandez | 1B/3B | Mar 31 | 79 | #623 | #89 |
| Max Clark | OF | Jul 31 | 33 | #996 | #105 |
| Justin Crawford | OF | Mar 26 | 131 | #350 | #107 |
| Luis Lara | OF | Jul 7 | 45 | #635 | #217 |
| Gage Jump | SP | May 26 | 113 | #650 | #312 |
| Walbert Ureña | SP | Mar 26 | 188 | #638 | #353 |
That is not pessimism, it is the base rate. 94 of the 104 ranked rookies sit below roster-relevant value, and the engine carries roughly 1,300 players in total, so a rookie being on the board at all already puts him ahead of most debutants. Treating every call-up as an opportunity costs more roster spots than it wins.
A rookie here is a player whose first major-league game came this season. That is deliberately simpler than official rookie eligibility, which also turns on service time and prior-season at-bats, neither of which this site carries, so using the official rule would mean guessing at it.
Value is the replacement-adjusted number the boards sort on, and roughly 134 is the 300th-ranked player. The dynasty column is a three-year production forecast, which is why a young player can look ordinary in one column and valuable in the other.
This is a season-long view of the class. The call-up piece covers the rolling window of recent debuts and answers a different question, what just happened, rather than who has established himself.
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.
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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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Distribution, uncertainty, per-archetype performance, calibrated tiers, draft sim.
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Where the model finds value the market is missing.
Curated picks from current rankings.
Where the model has measurable advantage.
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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.
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