The week's top producers from August 31 to September 6, 2026, led by Rafael Devers.
The seven days from August 31 to September 6, 2026, sorted by fantasy points actually banked. Points, not rates: a hitter with seven plate appearances and a .571 average had seven chances to do something, and a leaderboard that puts him first is measuring luck rather than production.
| Batter | Team | Pts | PA | HR | R | RBI | SB | AVG | OPS | Our rank |
|---|---|---|---|---|---|---|---|---|---|---|
| Rafael Devers | SFG | 49 | 33 | 6 | 8 | 13 | 0 | .385 | 1.669 | #39 |
| Thomas Saggese | STL | 41 | 25 | 3 | 9 | 8 | 2 | .526 | 1.718 | #794 |
| Alex Bregman | CHC | 38 | 30 | 4 | 7 | 8 | 0 | .346 | 1.279 | #18 |
| Mookie Betts | LAD | 35 | 29 | 2 | 6 | 11 | 0 | .360 | 1.168 | #52 |
| Ryan Jeffers | MIN | 35 | 25 | 4 | 5 | 11 | 0 | .261 | 1.233 | #34 |
| T.J. Rumfield | COL | 34 | 26 | 2 | 6 | 6 | 0 | .500 | 1.531 | #59 |
| Carter Jensen | KCR | 32 | 21 | 3 | 6 | 6 | 0 | .421 | 1.634 | #137 |
| Ronald Acuna Jr. | ATL | 32 | 26 | 3 | 5 | 9 | 1 | .440 | 1.303 | #246 |
| Jake McCarthy | COL | 32 | 30 | 1 | 5 | 5 | 4 | .346 | 1.049 | #66 |
| Ivan Herrera | STL | 30 | 30 | 2 | 7 | 9 | 0 | .393 | 1.076 | #43 |
| Pitcher | Team | Pts | IP | K | W | SV | ERA | WHIP | Our rank |
|---|---|---|---|---|---|---|---|---|---|
| Gavin Williams | CLE | 51 | 13.0 | 24 | 1 | 0 | 1.38 | 0.77 | #27 |
| Bryan Woo | SEA | 51 | 14.0 | 16 | 2 | 0 | 0.64 | 0.64 | #134 |
| Jacob deGrom | TEX | 46 | 13.0 | 15 | 1 | 0 | 1.38 | 0.46 | #140 |
| Walbert Urena | LAA | 44 | 15.0 | 12 | 1 | 0 | 1.20 | 0.60 | #530 |
| Michael King | SDP | 35 | 12.0 | 9 | 2 | 0 | 0.00 | 1.17 | #105 |
| Anthony Molina | SFG | 32 | 10.0 | 12 | 2 | 0 | 2.70 | 0.80 | unranked |
| Taj Bradley | MIN | 32 | 12.0 | 15 | 2 | 0 | 3.75 | 1.08 | #130 |
| Payton Tolle | BOS | 32 | 11.2 | 18 | 1 | 0 | 3.86 | 1.03 | #133 |
| Aaron Nola | PHI | 31 | 12.0 | 9 | 1 | 0 | 1.50 | 1.00 | #274 |
| Cameron Schlittler | NYY | 29 | 8.0 | 9 | 0 | 0 | 1.12 | 0.25 | #7 |
4 of the 20 players above sit below roster-relevant value on our board despite the week they just had. That is the useful column in these tables. A big week from a top-30 player is confirmation; a big week from someone the engine still will not rank is a sample, and the difference decides whether you are looking at a pickup or at noise.
Points are the window total from our own scoring, over the exact dates named above. Sorting on points rather than on rate stats means playing time counts for something, which is how fantasy actually works, a player who sat four of seven days did not help you, whatever he hit when he played.
Our rank is the engine's view looking forward, not a grade on the week. The two disagreeing is the interesting case, not an error: the week is what happened, and the rank is what we expect next.
Curated picks where the model has the highest decision conviction. Updated every render.
Run rankings to populate Market Edge.
Run rankings first.
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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.
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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.
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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.