12 rostered players on the IL, with measured timelines by injury type.
12 rostered players are on the injured list. This is not a return-date report, nothing on this site knows when anyone comes back, but it does pair how long each player has already been out against what that injury has actually cost the 309 players who have returned from one this season.
Days already missed, against the measured median for the same injury. The range in brackets is the middle half of those cases, for an elbow it spans three weeks to three months, which is why no single date belongs here.
| Player | Pos | IL | Injury | Days out | Typical for this injury | Value |
|---|---|---|---|---|---|---|
| Juan Soto | OF/DH | IL10 | Left calf strain | 31 | 45 days (n=17, 20-60) | 228 |
| Casey Schmitt | OF/3B/DH/1B/2B | IL10 | Left knee meniscus tear | 28 | 20 days (n=19, 15-36) | 138 |
| James Wood | OF/DH | IL10 | Left oblique strain | 21 | 31 days (n=37, 20-42) | 326 |
| Nick Kurtz | 1B | IL60 | Strained right thumb | 21 | no sample | 163 |
| Riley Greene | OF/DH | IL10 | Right hamstring strain | 13 | 26 days (n=38, 15-39) | 179 |
| Tyler Soderstrom | OF/1B | IL60 | Left hip impingement | 9 | 16 days (n=14, 14-26) | 173 |
| Dansby Swanson | SS | IL10 | Left oblique strain | 8 | 31 days (n=37, 20-42) | 139 |
| Hunter Goodman | C/DH | IL10 | Left shoulder strain | 7 | 34 days (n=39, 22-67) | 150 |
These players have now been out longer than the median case for their injury. That is not a prediction that they are close, it is as often a sign of a setback as of an imminent return, but it is the point at which a replacement stops being a short-term hold.
| Player | Pos | IL | Injury | Days out | Typical for this injury | Value |
|---|---|---|---|---|---|---|
| Casey Schmitt | OF/3B/DH/1B/2B | IL10 | Left knee meniscus tear | 28 | 20 days (n=19, 15-36) | 138 |
Every returned case, by body part. Sample sizes are shown because they are the whole story: a median off nine cases is a hint, and one off forty-three is close to a planning number.
| Injury | Cases | Median days | Middle half |
|---|---|---|---|
| Elbow | 49 | 46 | 24-80 |
| Shoulder | 39 | 34 | 22-67 |
| Hamstring | 38 | 26 | 15-39 |
| Oblique | 37 | 31 | 20-42 |
| Knee | 19 | 20 | 15-36 |
| Forearm | 18 | 36 | 21-51 |
| Back | 18 | 17 | 12-26 |
| Calf | 17 | 45 | 20-60 |
| Wrist | 14 | 19 | 12-61 |
| Hip | 14 | 16 | 14-26 |
| Ankle | 12 | 24 | 11-56 |
| Concussion | 12 | 10 | 8-14 |
Days out is counted from the transaction that placed the player on the list. Current status comes from the live injury feed, which is authoritative for who is out but carries no dates at all, the two are joined per player.
The typical column is a median with its sample size and interquartile range, and it describes other players, not this one. It is context for how long an injury of that kind has been costing people, not a forecast for anybody.
Only players above roster-relevant value appear. An injury to someone nobody rosters is not a fantasy event, and listing every one would bury the handful that change a lineup.
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.