Form by the rates: 16 heating up, 16 cooling off across hitters and pitchers.
Form, measured as a rate rather than a total. Comparing the week of July 27 to August 2, 2026 against the whole of July 1 to July 31, 2026, per plate appearance for hitters and per inning for pitchers, because a player who simply started more games last week has not changed, he has just played.
Heating up. The top tenth of the group by change in points per plate appearance, a bar of +0.36 set by this run's own spread rather than by a fixed number.
| Player | Team | Week pts/PA | Month pts/PA | Change | Week PA | Our rank |
|---|---|---|---|---|---|---|
| Rhys Hoskins | CLE | 1.67 | 0.37 | +1.29 | 6 | #669 |
| Dominic Smith | ATL | 1.43 | 0.53 | +0.90 | 7 | #420 |
| Daniel Susac | SFG | 1.86 | 1.08 | +0.78 | 14 | #365 |
| Kyle Manzardo | CLE | 1.00 | 0.26 | +0.74 | 20 | #627 |
| Brett Sullivan | COL | 1.43 | 0.69 | +0.74 | 7 | #447 |
| Mike Yastrzemski | ATL | 1.33 | 0.63 | +0.70 | 12 | #419 |
| Matt Vierling | DET | 1.00 | 0.30 | +0.70 | 6 | #456 |
| James Outman | DET | 0.71 | 0.02 | +0.70 | 7 | #1008 |
Cooling off. The bottom tenth, at -0.45 or worse.
| Player | Team | Week pts/PA | Month pts/PA | Change | Week PA | Our rank |
|---|---|---|---|---|---|---|
| Richie Palacios | TBR | -0.57 | 0.57 | -1.14 | 7 | #370 |
| Zach Dezenzo | HOU | -0.60 | 0.32 | -0.92 | 5 | #1213 |
| Austin Hedges | CLE | -0.11 | 0.76 | -0.87 | 9 | #655 |
| Joey Ortiz | MIL | -0.14 | 0.72 | -0.86 | 7 | #366 |
| Sung-Mun Song | SDP | -0.20 | 0.63 | -0.83 | 5 | #560 |
| Victor Mesa Jr. | TBR | -0.23 | 0.59 | -0.82 | 13 | #661 |
| Gabriel Rincones | PHI | -0.60 | 0.16 | -0.76 | 5 | #1203 |
| Adrian Del Castillo | ARI | -0.25 | 0.50 | -0.75 | 8 | #322 |
Heating up. The top tenth of the group by change in points per inning, a bar of +1.55 set by this run's own spread rather than by a fixed number.
| Player | Team | Week pts/IP | Month pts/IP | Change | Week IP | Our rank |
|---|---|---|---|---|---|---|
| Cristopher Sanchez | PHI | 4.00 | 0.78 | +3.22 | 6 | #24 |
| Cal Quantrill | TEX | 3.78 | 1.10 | +2.69 | 5 | #514 |
| Will Warren | NYY | 3.81 | 1.31 | +2.50 | 6 | #319 |
| Zebby Matthews | MIN | 2.60 | 0.23 | +2.37 | 5 | #678 |
| Kyle Freeland | COL | 3.11 | 0.79 | +2.33 | 9 | #928 |
| German Marquez | SDP | 4.00 | 1.96 | +2.04 | 5 | #738 |
| David Peterson | CHC | 2.75 | 1.14 | +1.61 | 12 | #421 |
| Logan Webb | SFG | 2.33 | 0.73 | +1.60 | 6 | #142 |
Cooling off. The bottom tenth, at -1.65 or worse.
| Player | Team | Week pts/IP | Month pts/IP | Change | Week IP | Our rank |
|---|---|---|---|---|---|---|
| Landen Roupp | SFG | -0.95 | 2.26 | -3.21 | 8 | #308 |
| Foster Griffin | WSN | -1.31 | 1.81 | -3.12 | 5 | #69 |
| Jesus Luzardo | PHI | 0.38 | 3.03 | -2.66 | 6 | #59 |
| Nathan Eovaldi | TEX | -1.00 | 1.59 | -2.59 | 5 | #198 |
| Kyle Bradish | BAL | -0.33 | 1.96 | -2.29 | 7 | #284 |
| Nick Martinez | TBR | -0.17 | 1.87 | -2.03 | 6 | #121 |
| Luis Castillo | SEA | -1.20 | 0.57 | -1.77 | 5 | #601 |
| Zack Wheeler | PHI | 1.20 | 2.89 | -1.69 | 5 | #14 |
Start with Cristopher Sanchez, because he is the one case where the hot week and the forward view agree. Four points per inning last week against 0.78 over the month is the largest pitching jump on the board, and the engine already has him 24th. That combination is rare here. Nothing in this data tells you whether he changed a pitch or simply drew two soft lineups, so I will not pretend to know the mechanism. What I can say is that a rank of 24 was not built on last week: it survived a month that averaged 0.78, which means the engine is looking through the month, not the week. Start him and stop thinking about it.
The genuinely arguable name is Foster Griffin. He posted minus 1.31 points per inning last week, the worst mark in the cooling group, and he is still ranked 69th. That gap is the whole story. One start with a crooked inning drags a weekly rate below zero in a way a month never can, and the five-day overlap between the windows means the month figure of 1.81 already contains some of the damage. So the true drop is smaller than 3.12 looks. The case against me is straightforward and not stupid: a rank of 69 for a pitcher who was at 1.81 for the month is the engine leaning hard on something the recent results are not showing, and if that something is role, a swingman start can vanish without warning. Here is the falsifier. If he takes a normal turn and the rate lands back near his monthly 1.81, the forward rank is right and the week was one blowup. If he throws two or three innings in relief in his next outing, the rank is stale and you drop him.
Jesus Luzardo and Zack Wheeler are the same situation with better nameplates and no decision attached. Wheeler went from 2.89 to 1.20 and sits 14th. Luzardo fell from 3.03 to 0.38 and sits 59th. Neither move is large enough to outrun the noise in a one-week sample, and the engine did not blink. Nobody is benching either. The names in this group that would change a lineup are further down: Nick Martinez at 121 and Nathan Eovaldi at 198 both went negative for the week, and Eovaldi is the one I would still start, because a forward rank of 198 already prices in a pitcher who was only at 1.59 for the month.
On the hitting side, treat almost all of it as a hot week. Rhys Hoskins is the clean example. He jumped from 0.37 to 1.67 points per plate appearance, the biggest batter move here, and the engine has him 669th. That is not a disagreement, it is the engine telling you a week of plate appearances is too few to move anything. Kyle Manzardo at 627 is the same story on the same roster. James Outman went from 0.02 to 0.71 and is ranked 1008th, which is the engine saying the month was closer to the truth than the week.
The one I would actually act on is Daniel Susac. His month was 1.08, already respectable, and the week took it to 1.86. He is ranked 365th, far ahead of the rest of the hot bats, and a catcher whose baseline was never zero is a different animal from a first baseman spiking off 0.37. Add him in two-catcher formats. The cost of being wrong is a bench spot for two weeks. The falsifier is playing time: if he is not starting four or five times a week, the rate does not matter.
Everything here is a rate. Points per plate appearance and points per inning remove the schedule from the comparison, which matters more than it sounds: the single most common reason a player "broke out" last week is that he started six games instead of three.
The two windows overlap, the week sits inside the month, so every player is partly being compared against himself. That pulls each number toward zero, which means real form changes are understated here and none of them are manufactured by the method.
The cutoffs are the tenth and ninetieth percentiles of the group being shown, recomputed every time this runs. A pitcher and a hitter are never held to the same bar, because their spreads are not the same shape.
A player needs 5 plate appearances or innings in the week and 20 in the month to appear. A rate built on two at-bats is not form, it is arithmetic.
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.