MLB Batting Splits 2025
Performance splits by handedness, home/away, and situational categories.
| Player | Team | Split | AB | H | HR | RBI | AVG | OBP | SLG | OPS ▲ |
|---|---|---|---|---|---|---|---|---|---|---|
 |
Atlanta Braves | 11 | 3 | 2 | 4 | .273 | .308 | .818 | 1.126 | |
 |
Colorado Rockies | 22 | 7 | 2 | 5 | .318 | .444 | .682 | 1.126 | |
 |
Colorado Rockies | 22 | 10 | 0 | 2 | .455 | .536 | .591 | 1.127 | |
 |
Boston Red Sox | 22 | 7 | 3 | 5 | .318 | .400 | .727 | 1.127 | |
 |
New York Mets | 11 | 2 | 2 | 4 | .182 | .400 | .727 | 1.127 | |
 |
Chicago Cubs | 12 | 5 | 1 | 1 | .417 | .462 | .667 | 1.128 | |
 |
Houston Astros | 12 | 5 | 0 | 1 | .417 | .462 | .667 | 1.128 | |
 |
St. Louis Cardinals | 12 | 5 | 0 | 0 | .417 | .462 | .667 | 1.128 | |
|
Seattle Mariners | 23 | 8 | 2 | 6 | .348 | .433 | .696 | 1.129 | |
|
Toronto Blue Jays | 23 | 9 | 1 | 4 | .391 | .391 | .739 | 1.130 | |
 |
Colorado Rockies | 21 | 7 | 2 | 11 | .333 | .417 | .714 | 1.131 | |
|
Minnesota Twins | 23 | 10 | 1 | 7 | .435 | .480 | .652 | 1.132 | |
|
Milwaukee Brewers | 10 | 2 | 2 | 3 | .200 | .333 | .800 | 1.133 | |
|
Toronto Blue Jays | 10 | 3 | 1 | 1 | .300 | .533 | .600 | 1.133 | |
  |
Philadelphia Phillies | 20 | 10 | 0 | 0 | .500 | .583 | .550 | 1.133 | |
 |
Arizona Diamondbacks | 12 | 4 | 1 | 3 | .333 | .467 | .667 | 1.133 | |
|
Detroit Tigers | 15 | 6 | 1 | 7 | .400 | .400 | .733 | 1.133 | |
 |
Seattle Mariners | 14 | 7 | 0 | 2 | .500 | .563 | .571 | 1.134 | |
 |
Boston Red Sox | 17 | 7 | 1 | 6 | .412 | .545 | .588 | 1.134 | |
|
San Diego Padres | 20 | 7 | 2 | 3 | .350 | .435 | .700 | 1.135 | |
|
Chicago White Sox | 12 | 4 | 1 | 1 | .333 | .385 | .750 | 1.135 | |
|
Toronto Blue Jays | 11 | 5 | 0 | 3 | .455 | .500 | .636 | 1.136 | |
|
Cincinnati Reds | 11 | 4 | 0 | 2 | .364 | .500 | .636 | 1.136 | |
|
Tampa Bay Rays | 11 | 5 | 0 | 1 | .455 | .500 | .636 | 1.136 | |
|
New York Yankees | 11 | 6 | 0 | 2 | .545 | .500 | .636 | 1.136 | |
 |
Detroit Tigers | 42 | 15 | 3 | 5 | .357 | .471 | .667 | 1.137 | |
|
Los Angeles Angels | 15 | 6 | 1 | 1 | .400 | .471 | .667 | 1.137 | |
|
Chicago White Sox | 10 | 4 | 0 | 2 | .400 | .538 | .600 | 1.138 | |
|
Detroit Tigers | 16 | 5 | 1 | 2 | .313 | .450 | .688 | 1.138 | |
 |
Baltimore Orioles | 16 | 5 | 2 | 6 | .313 | .389 | .750 | 1.139 | |
|
Cleveland Indians | 21 | 9 | 1 | 5 | .429 | .520 | .619 | 1.139 | |
|
Philadelphia Phillies | 26 | 9 | 3 | 10 | .346 | .370 | .769 | 1.140 | |
|
Seattle Mariners | 12 | 3 | 2 | 5 | .250 | .308 | .833 | 1.141 | |
|
Oakland Athletics | 12 | 3 | 2 | 4 | .250 | .308 | .833 | 1.141 | |
|
Los Angeles Angels | 46 | 15 | 5 | 9 | .326 | .446 | .696 | 1.142 | |
|
Colorado Rockies | 24 | 7 | 3 | 6 | .292 | .393 | .750 | 1.143 | |
|
Cleveland Indians | 19 | 7 | 2 | 5 | .368 | .458 | .684 | 1.143 | |
|
San Diego Padres | 14 | 6 | 1 | 2 | .429 | .429 | .714 | 1.143 | |
|
Arizona Diamondbacks | 14 | 7 | 0 | 1 | .500 | .500 | .643 | 1.143 | |
 |
Cleveland Indians | 11 | 4 | 1 | 2 | .364 | .417 | .727 | 1.144 | |
 |
Washington Nationals | 13 | 4 | 2 | 4 | .308 | .375 | .769 | 1.144 | |
|
Minnesota Twins | 11 | 5 | 0 | 1 | .455 | .417 | .727 | 1.144 | |
|
Tampa Bay Rays | 25 | 10 | 2 | 3 | .400 | .464 | .680 | 1.144 | |
|
New York Mets | 11 | 4 | 1 | 3 | .364 | .417 | .727 | 1.144 | |
|
Minnesota Twins | 17 | 6 | 1 | 3 | .353 | .556 | .588 | 1.144 | |
|
Los Angeles Dodgers | 15 | 6 | 1 | 7 | .400 | .545 | .600 | 1.145 | |
|
Atlanta Braves | 18 | 6 | 2 | 3 | .333 | .478 | .667 | 1.145 | |
|
New York Mets | 10 | 5 | 0 | 1 | .500 | .545 | .600 | 1.145 | |
|
Houston Astros | 25 | 8 | 3 | 11 | .320 | .346 | .800 | 1.146 | |
|
Oakland Athletics | 17 | 7 | 1 | 4 | .412 | .500 | .647 | 1.147 |
Understanding Opponent Splits
Opponent splits reveal how a hitter performs against each MLB team. These splits capture the combined effect of a team's pitching staff, defensive alignment, and park factors. Some hitters consistently dominate certain teams due to favorable pitching matchups.
Team-Specific Matchups
Some hitters own certain teams. This often reflects favorable matchups against that team's pitching staff — handedness advantages, pitch-type weaknesses, or familiarity from division play. Division rivals face each other 13+ times per season, creating larger sample sizes.
Stacking by Opponent
For DFS, opponent splits help identify entire lineups to stack. If multiple hitters on a team have strong splits against today's opponent, that's a high-correlation stack. Combine with the opposing starter's recent form for maximum edge.
Sample Size Caution
Opponent splits against non-division teams can be small (3-4 games per season). Weight division matchups more heavily since they have 13+ games of data. A .400 AVG in 10 at-bats against a team is interesting but not predictive on its own.
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Data Source & Methodology
Batting splits sourced from MLB Stats API. Stats reflect current season data and update daily as games are played.
Frequently Asked Questions
- How reliable are opponent batting splits?
- Division opponent splits are the most reliable since hitters face those teams 13+ times per season. Interleague and non-division splits have smaller sample sizes (3-7 games) and should be weighted less heavily. Always check the at-bat count before drawing conclusions.
- How do I use opponent splits for DFS stacks?
- Find teams where multiple hitters have strong splits against today's opponent. Stack 3-4 hitters from that team in your DFS lineup for high correlation. This works especially well when the opposing starter is also weak against that lineup's handedness profile.
- Why do some hitters crush certain teams?
- It usually comes down to pitching staff matchups. A hitter might face favorable pitch types, have platoon advantages against most of a team's rotation, or thrive at that team's home park. Division familiarity also plays a role — hitters see the same pitchers repeatedly.