10 players we rank far above the dynasty consensus, 10 far below.
Our dynasty board against the dynasty consensus, across the 129 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 |
|---|---|---|---|---|
| Sal Stewart | 1B/3B/DH | 8 | 127 | 32 |
| Drake Baldwin | C/DH | 22 | 134 | 2 |
| Cam Schlittler | SP | 56 | 165 | 12 |
| Jesús Luzardo | SP | 14 | 116 | 5 |
| Tyler Soderstrom | OF/1B | 20 | 114 | 14 |
| Luke Keaschall | 2B/OF | 71 | 160 | 10 |
| Carson Benge | OF | 50 | 137 | 22 |
| Ben Rice | 1B/DH/C | 27 | 87 | 10 |
| Nico Hoerner | 2B/SS | 75 | 135 | 40 |
| Brice Turang | 2B | 36 | 95 | 9 |
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 34 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 |
|---|---|---|---|---|
| Max Clark | OF/DH | 61 | 662 | 6 |
| Spencer Schwellenbach | SP | 69 | 497 | 22 |
| Spencer Strider | SP | 62 | 483 | 15 |
| Michael King | SP | 104 | 486 | 6 |
| Luis Robert Jr. | OF | 158 | 484 | 22 |
| Colt Emerson | SS/3B | 162 | 478 | 8 |
| Cole Ragans | SP | 54 | 365 | 2 |
| Marcelo Mayer | 2B/SS/3B | 119 | 407 | 13 |
| Austin Riley | 3B | 49 | 329 | 0 |
| Bubba Chandler | SP | 65 | 344 | 0 |
Start with Max Clark, because he is the cleanest case of a gap that is not an argument. Consensus has him 61st; our dynasty forecast has him 662nd. Nobody at either end thinks he is going to hit 30 home runs next April. The market is paying for the right to own six cheap years that begin at some point in the future, and our number is a three-year production total that mostly runs out before those years start. Colt Emerson works the same way, 162 against our 478. These are not opinions in conflict. They are answers to two different questions, and if your league pays for future surplus rather than counting stats through the window, you should ignore our rank on both and move on.
The genuine fights are the arms with track records, because there the production question is the only question. Strider at consensus 62 and ours 483 is the one I would argue about. A three-year forecast that buries a pitcher that far is not saying he is bad, it is saying the chance he throws a full, healthy, dominant three seasons is small enough that the average outcome collapses. Ragans, at 54 against our 365, carries the same shape. Schwellenbach at 69 against 497 is the harshest of the three, and that one I am less comfortable with: the market is buying an arm with real big-league performance behind it, and our number reads like a durability discount applied with a very heavy hand.
Here is the case against me, made properly. Pitcher forecasts that lean on health history have a bad habit of double-counting: the injury already showed up in the innings total, and then it gets charged again through the rate stats. Three of our ten lowest arms sit inside the consensus top 70, which is not a coincidence, it is a policy. If that policy is a touch too harsh, then Strider at 483 is not a read, it is a systematic markdown wearing a rank. The falsifier is specific and it arrives early: a full, uninterrupted starter's workload from Strider or Ragans over one season should pull that number hundreds of spots. If it does not, the model is not learning from innings, and you should stop trusting its pitcher tail.
On the other side, Sal Stewart at 8 against a consensus 127 is the boldest thing in the table, and it is bold in a way the Clark comparison makes interesting. Both are young. Only one of them is being credited with production inside the window. So the driver cannot be youth. It has to be that the forecast expects Stewart's bat to play at the top level immediately and at a high level, and the market is still pricing him as a prospect with a service-time path to clear. Drake Baldwin at 22 against 134 is the same story at catcher, where a bat that plays gets an enormous positional boost in any three-year count.
What I would actually do. Buy Stewart and Baldwin if your leaguemates are still valuing them off prospect lists, because you are getting a starter at a prospect price and the downside is that you waited a year. Sell nothing on Clark or Emerson, since our rank there is not evidence. And on Strider, I would not sell at consensus 62, I would sell at a small discount to it: the market price is closer to right than 483, but our forecast is telling you the distribution of outcomes is wider than the price implies. Being wrong on Stewart costs you a roster spot. Being wrong on Strider costs you an ace.
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.
-
-
-
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.