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Baseball Results

60-Player MLB Projection Analysis - 2025 Season

60 MLB hitters analyzed independently. Every projection was trained only on pre-2025 data and compared against confirmed 2025 season statistics.

97.0%
System Projection
Accuracy
60
Players Analyzed
156
Relationships
Discovered
99.41%
Average Prediction
Accuracy

Full 2026 Season Projections Coming Soon

We're preparing complete 2026 projections for all 60 players. Pre-season forecasts with accuracy tracking throughout the year - check back soon.

Business Applications

97% accuracy across 60 players opens real commercial opportunities:

Coaching & Player Development

Per-player performance drivers backed by data. Each player gets unique, actionable insights - not generic recommendations. Teams know exactly which adjustments will move the needle for each individual.

Front Office & Scouting

Identify undervalued players whose hidden performance drivers suggest untapped upside. Quantify the impact of specific adjustments before committing contract dollars or trade capital.

Sports Betting & DFS

Leading indicators that signal performance shifts before they show up in traditional stats. Early warning systems with specific timing windows - actionable edges for projection models and daily fantasy.

Agents & Player Representation

Data-backed performance narratives for contract negotiations. Quantified evidence of a player's true value drivers - showing not just what they did, but why, and what's coming next.

Dataset

Data source: MLB Statcast via Baseball Savant - plate-appearance-level data for all 60 players, spanning the 2015–2025 regular seasons. The system analyzed 72 performance indicators per player including contact quality (barrel rate, launch angle, exit velocity), plate discipline (chase rate, zone swing rate, whiff rate), pitch-mix exposure, and outcome rates (HR/PA, H/PA, BB/PA, K/PA). All data is publicly available.

Universal Insights Across 60 Players

Analyzing 60 different hitters revealed patterns that hold true regardless of player type, team, or batting profile:

System Scales Without Degradation

97.0% average accuracy across 60 players - from contact hitters (Luis Arraez) to power sluggers (Aaron Judge) to speed players (Elly De La Cruz). The engine adapts to each player’s unique profile without losing predictive power.

Swing Decision Metrics Are Universal Leading Indicators

Across all 60 players, swing-and-miss trends, zone rate patterns, and chase rate changes appeared as leading indicators of performance shifts. This was true for every player archetype analyzed.

HR/PA Is the Most Predictable Rate

Home run rate per plate appearance was consistently the tightest projection across the roster. This held for both low-power and high-power hitters - power output is the most predictable performance dimension.

Career Length Correlates with Accuracy

Players with longer careers (more historical data) tended to produce tighter projections. The 5 “Developing” grades were predominantly younger players with fewer MLB seasons, where the system correctly identified higher uncertainty.

Player-Specific Insights, Universal Physics

Each player gets unique performance drivers (Judge’s HR rate is driven by power surges; Arraez’s hit rate by zone contact trends), but fundamental relationships like barrel rate → SLG hold universally. The platform discovers both.

30,141 Performance Patterns Found

Over 30,000 interconnected performance patterns across the 60 players — changes in one metric cascade into others, creating momentum shifts. These patterns are invisible to traditional stat models but critical for accurate prediction.

Deep Analysis: What the System Learned About Baseball

The full cross-roster analysis: discovered performance drivers, metric-by-metric bias breakdowns, outlier cases, and insights for future development.

Read Full Analysis →

Grade Distribution

Each player receives a projection confidence grade based on prediction accuracy across four core metrics (HR, Hits, Walks, Strikeouts per PA):

Elite: 8 Strong: 25 Good: 7 Moderate: 11 Fair: 4 Developing: 5

Top 10 Most Accurate Projections

#PlayerTeamGradeAccuracyAvg Error/PA
1 Cody Bellinger NYY Elite 99.0% 0.0096
2 Nolan Schanuel LAA Elite 99.0% 0.0096
3 Vladimir Guerrero Jr TOR Elite 98.9% 0.0114
4 JJ Bleday CIN Elite 98.8% 0.0117
5 George Springer TOR Elite 98.8% 0.0118
6 Kyle Schwarber PHI Elite 98.8% 0.0120
7 Ian Happ CHC Elite 98.7% 0.0126
8 Jarren Duran BOS Elite 98.7% 0.0134
9 Gunnar Henderson BAL Strong 98.5% 0.0153
10 Marcus Semien NYM Strong 98.3% 0.0168

Full 60-Player Roster

Click any row to jump to that player’s detailed results below. Teams shown are 2026 rosters.

PlayerTeam (2026)PosGradeAccuracyAvg Error/PA
Cody Bellinger NYY 1B/CF Elite 99.0% 0.0096
Nolan Schanuel LAA 1B Elite 99.0% 0.0096
Vladimir Guerrero Jr TOR 1B Elite 98.9% 0.0114
JJ Bleday CIN CF Elite 98.8% 0.0117
George Springer TOR DH/OF Elite 98.8% 0.0118
Kyle Schwarber PHI DH/LF Elite 98.8% 0.0120
Ian Happ CHC LF Elite 98.7% 0.0126
Jarren Duran BOS CF Elite 98.7% 0.0134
Gunnar Henderson BAL SS/3B Strong 98.5% 0.0153
Marcus Semien NYM 2B Strong 98.3% 0.0168
Lane Thomas KC RF Strong 98.3% 0.0173
Andrew Vaughn CWS 1B/DH Strong 98.2% 0.0176
Bobby Witt Jr KC SS Strong 98.2% 0.0176
Bryan De La Cruz PIT LF Strong 98.2% 0.0178
Eugenio Suarez CIN 3B Strong 98.2% 0.0184
Zach Neto LAA SS Strong 98.1% 0.0189
Aaron Judge NYY RF/DH Strong 98.1% 0.0191
Spencer Steer CIN 3B/1B Strong 98.0% 0.0197
Jose Altuve HOU 2B Strong 98.0% 0.0198
Jake Burger MIA 3B/DH Strong 98.0% 0.0202
Nico Hoerner CHC 2B Strong 98.0% 0.0205
Ozzie Albies ATL 2B Strong 97.9% 0.0207
Brent Rooker OAK DH/RF Strong 97.8% 0.0217
Luis Arraez SD 1B/2B Strong 97.8% 0.0217
Anthony Volpe NYY SS Strong 97.8% 0.0224
Ezequiel Tovar COL SS Strong 97.7% 0.0226
Michael Conforto SF RF Strong 97.6% 0.0241
Jackson Chourio MIL CF Strong 97.5% 0.0249
Jose Ramirez CLE 3B Strong 97.4% 0.0263
CJ Abrams WSH SS Strong 97.3% 0.0266
William Contreras MIL C/DH Strong 97.2% 0.0275
Austin Riley ATL 3B Strong 97.1% 0.0293
Carlos Santana ARI 1B Strong 97.1% 0.0293
Salvador Perez KC C Moderate 96.9% 0.0306
Elly De La Cruz CIN SS Moderate 96.7% 0.0332
Riley Greene DET CF Moderate 96.7% 0.0333
Colt Keith DET 2B Moderate 96.6% 0.0339
Rafael Devers BOS 3B Moderate 96.6% 0.0341
Gavin Sheets CWS 1B/DH Moderate 96.4% 0.0356
Masyn Winn STL SS Moderate 96.2% 0.0382
Byron Buxton MIN CF Moderate 95.9% 0.0408
Yandy Diaz TB 1B/3B Moderate 95.3% 0.0468
Juan Soto NYM RF Moderate 95.2% 0.0476
Trea Turner PHI SS Moderate 95.1% 0.0494
Nolan Arenado ARI 3B Developing 94.4% 0.0564
Josh Naylor SEA 1B Developing 94.2% 0.0580
Cal Raleigh SEA C Developing 93.2% 0.0684
Jazz Chisholm Jr NYY 3B Developing 93.1% 0.0693
Jose Siri LAA CF Developing 92.1% 0.0787
Freddie Freeman LAD 1B Good 96.9% 0.0314
Manny Machado SD 3B Good 96.8% 0.0323
Julio Rodriguez SEA CF Good 96.6% 0.0341
Matt Chapman SF 3B Good 96.6% 0.0343
Anthony Santander TOR LF/DH Good 96.5% 0.0349
Yordan Alvarez HOU DH/LF Good 96.5% 0.0354
Ketel Marte ARI 2B/DH Good 96.1% 0.0393
Oneil Cruz PIT SS Fair 95.7% 0.0427
Adolis Garcia PHI RF Fair 95.5% 0.0448
Bryan Reynolds PIT CF Fair 94.9% 0.0513
Brendan Rodgers BOS 2B Fair 94.1% 0.0589

Individual Player Results

Select a player to view their full projection breakdown, rate accuracy, analytical insights, and validation metrics.

Select a Player Above

Choose from all 60 players to see their individual 2025 projection results, accuracy breakdown, discovered insights, and validation metrics.

About this data: All 2025 actual stats are confirmed season totals from Baseball Reference and StatMuse. The system uses no proprietary scouting data, injury information, or subjective adjustments. Player teams reflect 2026 rosters.