Advanced MLB predictions powered by WAR, xFIP, wOBA, and cutting-edge sabermetrics. Statistical analysis built for anyone who trusts the numbers over narratives.
Data-driven analysis of the WBC's impact on the 2026 MLB season. Pitcher workloads, player performance projections, and how international tournament play affects regular season outcomes.
Run environment model lands at 7.30 projected runs against a closing total of 8.0. Leiter's 4.5 percent walk rate and 96.8 mph fastball, Holton's opener role, and a 47-degree Comerica all push the under at -105 on Sunday Night Baseball.
Calibrated 4.05-run projection vs the 4.5 line, +0.45 edge. Burke's xFIP-vs-ERA gap of 1.36 runs flagging an over-performing surface ERA, Michael King's run-suppression on the home side, and the Petco Park HR factor of 77 stack the under direction at -125.
Calibrated total model on the Over 7 at -112. Rea volatility tail plus Gallen fresh-off-IL pitch cap plus stretched bullpens stack the same direction. Wrigley wind-in priced. The over case does not depend on home runs.
A 10-19 Mets offense walks into a 53-degree Citi Field night against Cade Cavalli. Peterson on the New York side. The data model lands the Mets distribution well under the 4.5 line and the price holds residual edge.
Park factor, a command-first Paddack matchup, and Yamamoto's dominance on the Miami side combine to flatten the LA scoring distribution. The model takes the Dodgers team total under 5.5 with calibrated edge.
Bibee at home, command-first contact suppression, and a Progressive Field run environment combine to flatten the Tampa Bay scoring distribution against the Guardians. Model takes the Rays team total under 3.5.
Identical 3.69-run point projection in Game 1 and Game 2 against a 4.5 line. +0.81 edge per side, prices at -115 and -125. Residual learning, per-season bias correction, and a calibrated 62.4 percent under probability on the Poisson translation.
Michael King 2.28 ERA and 1.07 WHIP versus Ryne Nelson 6.97 ERA with an xFIP closer to 4.85. Bullpens at 3.50 / 3.79 ERA. The 15.5 total is anchored to the 2023 ghost game and Saturday spillover. The calibrated projection lands at 10.5 to 11.5 runs.
Two 17-9 teams, but the xFIP gap between Colin Rea (3.57) and Roki Sasaki (5.17) is the largest on the slate. Add Chicago's 152 wRC+ over the last 14 days and the model converges on Cubs +123 as the most underpriced moneyline of April 25.
Brewers plus-money price against Tarik Skubal's Tigers with Sproat carrying favorable xFIP and bullpen leverage on the Milwaukee side. Advanced-stats breakdown of why the underdog price holds value.
Giants priced at a heavy plus-money tag against the Dodgers in San Francisco with Ohtani-Mahle dynamics and an Oracle Park run-suppression edge. Full data-model breakdown.
Roupp's profile vs Yamamoto at Oracle Park gives the Giants a sharper plus-money number than the moneyline implies. Full data-model breakdown of the underdog edge.
Two strike-first starters share one of the American League's more consistent run-suppressing parks. Park factor, bullpen coverage, and pitch-mix depth all point the model under 9 at -115.
Heavy plus-money price on Colorado against Los Angeles in a Coors Field run environment that historically distorts the favorite tax. Data-model breakdown of why +231 has hidden value when the bullpen leverage swings to the home side.
Cincinnati priced as a road dog at +154 against Minnesota with a starter mismatch the model reads as much closer than the moneyline implies. Park-neutral breakdown of why the underdog price is sharp.
Petco run environment + Castillo's command profile against Buehler's return arc gives the Mariners a sharper number than the market implies. Full data-model breakdown.
Dylan Cease and Reid Detmers look like a run-suppressing matchup, but the model projects 8.4 combined runs once pitch-mix, xwOBA splits, park factor, and bullpen workload are priced in.
Every Saturday matchup through the lens of the projection model. Expected runs, win probabilities, and the biggest edges against market prices on the board today.
Skubal's strikeout rate meets Brayan Bello's ground-ball profile at Fenway. The model finds Boston's lineup matchup advantages outweighing Detroit's ace on paper.
The model flags Aaron Nola's 4.19 xERA against a Cubs lineup that leads the NL in walk rate. Plate discipline edge drives the win probability projection at Wrigley.
Connelly Early brings a 2.63 ERA and 15 strikeouts into a matchup with Simeon Woods Richardson, whose low strikeout rate and three homers allowed create Boston's clearest model edge.
Model projects 9.2 combined runs. Perez's 9 walks in 16 IP feed a Braves lineup hitting .274 with .791 OPS and 5.63 R/G. Three double-digit scoring games at Truist in the last 2 weeks.
Model projects Dodgers at 59.8% win probability, creating +4.7% expected value at -124. Sasaki's 1.56 WHIP and 1.37 K/BB ratio face a lineup hitting .297 with 6.38 R/G.
Chris Bassitt is carrying a 14.21 ERA through two starts, Baltimore has lost seven of ten, and Logan Webb's xFIP sits nearly two full runs below his ERA. Full 15-game Saturday breakdown with a pick on every matchup.
Dustin May's 15.95 ERA is a small-sample mirage. Three independent models (xFIP adjusted, platoon split, park factor) all push St. Louis above the +124 implied probability. Jordan Walker leads MLB with five home runs. The sharpest underdog on the April 10 fifteen-game slate.
Multiple projection models converge in the 62-70% win probability range. Nolan McLean ranked #1 pitcher on Thursday's slate by ESPN projections. Arizona 1-4 on the road, cold 47-degree weather at Citi Field suppresses offensive volatility.
Ohtani's 0.00 ERA collides with an 18-strikeout debut in Toronto. The model projects 6.4 combined runs, a full run below the 7.5 total, with the visiting lineup's MLB-best .292 average creating a decisive offensive gap.
Paul Skenes at PNC Park in cold April weather against Nick Pivetta. The model projects 5.0 combined runs, a full run below the posted 6.0 total. xFIP convergence, park factor suppression, and K-rate dominance all point under.
Logan Gilbert's 6.75 ERA through two starts masks elite underlying metrics. His 2.27 xERA represents the largest ERA-xERA gap among qualified starters, suggesting sharp regression ahead.
Data-driven analysis backing Mets-Giants Under 7.5 at Oracle Park. Senga's velocity spike, Webb's 54.9% ground-ball rate, and park factor convergence.
Shota Imanaga's 7.20 ERA is noise over one start. His career 3.34 ERA and 298 K track record vs Cecconi's projected 4.38 ERA creates a clear statistical edge. Run expectancy and K% differential analysis inside.
Dylan Cease (1.69 ERA, franchise-record 12 K debut) faces Grant Taylor (4.50 ERA, converted reliever) in a massive pitching mismatch. Run expectancy modeling, K% analysis, and the statistical case for the run line.
Crochet's 2025 campaign produced 255 strikeouts, a 2.59 ERA, and a 1.05 WHIP across 205.1 innings. His 97.6 mph average fastball velocity and 5.54 K/BB ratio place him in rare statistical territory for left-handed starters. Full projection model output and velocity sustaining analysis.
Complete statistical projection analysis for all 15 Monday pitching matchups. Sasaki vs Messick at Dodger Stadium leads the slate with the widest win probability gap. Verlander aging curves, Castillo at T-Mobile Park, and early-season calibration signals.
Luzardo (2.90 FIP, 6.6% barrel rate, .285 xwOBA) vs Gore (3.74 FIP, 10.6% barrel rate, .322 xwOBA) at Citizens Bank Park. Complete Statcast contact quality breakdown and model output analysis.
Advanced metric projections for all 8 March 27 pitching matchups. Skubal vs Pivetta at Petco Park, Yamamoto vs Gallen at Dodger Stadium, and Sale vs Ragans headline the slate with park-adjusted FIP analysis.
Statistical breakdown of all 11 Opening Day pitching matchups. Four sub-2.60 ERA starters, two back-to-back Cy Young winners, and the World Series MVP all take the mound on March 26.
Tarik Skubal's 2.45 FIP and 0.89 WHIP meet Nick Pivetta's 3.49 FIP at pitcher-friendly Petco Park (runs factor 94). Full statistical breakdown of the Opening Day pitching mismatch.
Max Fried's 2.86 ERA and 3.07 FIP meet Logan Webb's 2.56 FIP and 3.10 home ERA at Oracle Park, where the HR factor sits 24% below league average. Full projection breakdown for Opening Night.
Deep statistical analysis of MLB's new ABS challenge system launching in 2026. How automated ball-strike technology impacts umpire accuracy, strikeout rates, walk rates, and what it means for totals and prop betting models.
Advanced analytics breakdown of Yoshinobu Yamamoto's dominant spring training finale: 7 K's in 5 scoreless innings vs Padres. Pitch mix data, 2025 advanced stats, and 2026 season projections.
Data-driven analysis of Rob Manfred's proposal to move the WBC to midseason. How would a mid-year international tournament impact player performance, injuries, and win totals?
Advanced analytics deep dive on Nolan McLean's meteoric rise from Mets prospect to Team USA WBC Championship starter. Statcast data, Stuff+ grades, spin rates, and 2026 MLB season projections.
Deep statistical breakdown of Venezuela's 3-2 WBC championship victory over Team USA. Pitching matchup data, bullpen usage, and why Eduardo Rodriguez's 4.1 scoreless innings changed everything.
How WBC performances, pitcher workloads, and advanced stats project into the MLB regular season for key players. Paul Skenes, Ohtani, and the data behind Venezuela's surprise run.
NPB to MLB transition projections for Team Japan's top WBC prospects. WAR estimates and advanced metrics for Itoh, Sato, Morishita, Maki, and Takahashi.
Data-driven analysis of Moreno's forearm tightness and what it means for Arizona's catching depth, projected WAR output, and offensive production behind the plate in 2026.
Data-driven breakdown of every WBC tiebreaker scenario keeping Team USA alive. Head-to-head, run differential, ERA, and batting average tiebreaker rules explained with statistical context.
Deep sabermetric analysis of Clayton Kershaw's legendary career. 2.53 ERA, 3,052 strikeouts, .699 W-L%. ERA+, FIP, WAR and advanced stats prove why he's the best left-handed pitcher in modern baseball history.
Pitch-by-pitch Statcast analysis of Roki Sasaki's cutter vs slider experiment. Spin rate data, movement profiles, proStuff+ breakdown and why the Dodgers' rotation hinges on one pitch.
Marte brings a career 30.9 fWAR and elite baserunning metrics to Kansas City. Full WAR projections, defensive range analysis, and what the advanced stats say about his fit in the Royals' lineup construction.
Advanced statistical analysis of Cooperstown-track players in the 2026 World Baseball Classic. Ohtani, Judge, Soto, Machado, and Witt Jr. career WAR trajectories and Hall of Fame probability projections through age-adjusted models.
Statistical deep dive into all 20 WBC rosters. 304 MLB players, 78 former All-Stars. Aggregate talent metrics reveal which pools are toughest and where the data says the market is mispricing contenders.
Statistical breakdown of the top 5 MLB lineups for 2026. Dodgers, Braves, Mets, Mariners, Blue Jays ranked by OPS+, wOBA, barrel rate, and projected run production.
How MLB offseason moves shifted 2026 WAR projections and projected win totals. Dodgers at 100 wins. Astros collapse to 81. Cubs surge with Bregman addition.
Three developments out of MLB camps carry real analytical weight for 2026 projections: Roki Sasaki's pitch arsenal expansion, Atlanta's compounding rotation losses with Schwellenbach on 60-day IL, and a New York Yankees slugger's elbow recovery timeline ahead of the WBC.
Houston reacquired Joey Loperfido from Toronto for Jesus Sanchez. Loperfido's 2025 slash of .333/.879 OPS in 41 games vs his .197/.236/.343 in 2024 creates a fascinating analytical question about sample size vs true talent level.
The former MVP re-signs for a third of last year's salary. His OPS+ cratered from 178 in 2022 to 100 by 2024. At $4M for a platoon role, the WAR math projects 0.5-1.0 fWAR against lefties.
Paredes' .353 wOBA against a .320 xwOBA suggests 2025 overperformance. His 87.4 mph exit velocity and 6.3% barrel rate raise questions, but plate discipline and positional versatility project 2.5-3.0 fWAR.
Data-driven MLB analytics, statistical analysis, and advanced metrics.
Data-driven MLB analytics, statistical analysis, and advanced metrics.
Data-driven MLB analytics, statistical analysis, and advanced metrics.
Data-driven MLB analytics, statistical analysis, and advanced metrics.
Data-driven MLB analytics, statistical analysis, and advanced metrics.
Data-driven MLB analytics, statistical analysis, and advanced metrics.
Data-driven MLB analytics, statistical analysis, and advanced metrics.
Data-driven MLB analytics, statistical analysis, and advanced metrics.
Data-driven MLB analytics, statistical analysis, and advanced metrics.
Data-driven MLB analytics, statistical analysis, and advanced metrics.
Data-driven MLB analytics, statistical analysis, and advanced metrics.
Data-driven MLB analytics, statistical analysis, and advanced metrics.
Data-driven MLB analytics, statistical analysis, and advanced metrics.
Data-driven MLB analytics, statistical analysis, and advanced metrics.
Data-driven MLB analytics, statistical analysis, and advanced metrics.
Data-driven MLB analytics, statistical analysis, and advanced metrics.
Data-driven MLB analytics, statistical analysis, and advanced metrics.
Data-driven MLB analytics, statistical analysis, and advanced metrics.
Data-driven MLB analytics, statistical analysis, and advanced metrics.
Data-driven MLB analytics, statistical analysis, and advanced metrics.
Data-driven MLB analytics, statistical analysis, and advanced metrics.
Data-driven MLB analytics, statistical analysis, and advanced metrics.
Data-driven MLB analytics, statistical analysis, and advanced metrics.
Data-driven MLB analytics, statistical analysis, and advanced metrics.
Data-driven MLB analytics, statistical analysis, and advanced metrics.
Data-driven MLB analytics, statistical analysis, and advanced metrics.
Data-driven MLB analytics, statistical analysis, and advanced metrics.
Data-driven MLB analytics, statistical analysis, and advanced metrics.
Data-driven MLB analytics, statistical analysis, and advanced metrics.
Data-driven MLB analytics, statistical analysis, and advanced metrics.
Data-driven MLB analytics, statistical analysis, and advanced metrics.
Data-driven MLB analytics, statistical analysis, and advanced metrics.
Data-driven MLB analytics, statistical analysis, and advanced metrics.
Data-driven MLB analytics, statistical analysis, and advanced metrics.
Free MLB projections and WAR-based analysis with public grading standards, source notes, and price-sensitivity warnings.