MLB Batting Splits 2025
Performance splits by handedness, home/away, and situational categories.
| Player | Team | Split | AB | H | HR | RBI | AVG | OBP | SLG | OPS ▲ |
|---|---|---|---|---|---|---|---|---|---|---|
 |
Boston Red Sox | 13 | 5 | 2 | 4 | .385 | .438 | .923 | 1.361 | |
 |
Pittsburgh Pirates | 18 | 8 | 2 | 4 | .444 | .474 | .889 | 1.363 | |
 |
Baltimore Orioles | 11 | 5 | 0 | 5 | .455 | .455 | .909 | 1.364 | |
 |
Chicago Cubs | 22 | 10 | 3 | 6 | .455 | .458 | .909 | 1.367 | |
 |
St. Louis Cardinals | 12 | 5 | 1 | 7 | .417 | .533 | .833 | 1.367 | |
 |
Cleveland Indians | 15 | 7 | 2 | 5 | .467 | .500 | .867 | 1.367 | |
 |
Cincinnati Reds | 11 | 4 | 2 | 3 | .364 | .462 | .909 | 1.371 | |
 |
Cincinnati Reds | 10 | 5 | 0 | 5 | .500 | .571 | .800 | 1.371 | |
 |
Pittsburgh Pirates | 11 | 4 | 2 | 3 | .364 | .462 | .909 | 1.371 | |
|
Toronto Blue Jays | 11 | 4 | 2 | 4 | .364 | .462 | .909 | 1.371 | |
|
Colorado Rockies | 12 | 6 | 0 | 4 | .500 | .538 | .833 | 1.372 | |
|
San Francisco Giants | 12 | 6 | 1 | 1 | .500 | .538 | .833 | 1.372 | |
|
Colorado Rockies | 18 | 6 | 3 | 9 | .333 | .429 | .944 | 1.373 | |
|
Miami Marlins | 15 | 5 | 3 | 6 | .333 | .375 | 1.000 | 1.375 | |
|
Cleveland Indians | 18 | 8 | 2 | 7 | .444 | .545 | .833 | 1.379 | |
 |
Los Angeles Angels | 33 | 14 | 5 | 12 | .424 | .412 | .970 | 1.381 | |
  |
New York Mets | 10 | 5 | 0 | 3 | .500 | .583 | .800 | 1.383 | |
|
Milwaukee Brewers | 10 | 5 | 1 | 4 | .500 | .583 | .800 | 1.383 | |
|
Arizona Diamondbacks | 13 | 5 | 2 | 6 | .385 | .385 | 1.000 | 1.385 | |
|
Minnesota Twins | 26 | 10 | 3 | 7 | .385 | .500 | .885 | 1.385 | |
 |
Texas Rangers | 13 | 5 | 2 | 7 | .385 | .385 | 1.000 | 1.385 | |
|
Chicago Cubs | 17 | 7 | 1 | 3 | .412 | .444 | .941 | 1.386 | |
|
Baltimore Orioles | 15 | 8 | 1 | 2 | .533 | .588 | .800 | 1.388 | |
|
Colorado Rockies | 18 | 9 | 1 | 2 | .500 | .500 | .889 | 1.389 | |
|
Toronto Blue Jays | 25 | 12 | 2 | 8 | .480 | .552 | .840 | 1.392 | |
|
Texas Rangers | 24 | 7 | 5 | 9 | .292 | .433 | .958 | 1.392 | |
|
St. Louis Cardinals | 11 | 5 | 1 | 1 | .455 | .667 | .727 | 1.394 | |
 |
Chicago Cubs | 15 | 6 | 1 | 3 | .400 | .400 | 1.000 | 1.400 | |
 |
Kansas City Royals | 20 | 8 | 4 | 5 | .400 | .400 | 1.000 | 1.400 | |
|
Washington Nationals | 10 | 4 | 1 | 3 | .400 | .500 | .900 | 1.400 | |
|
Cincinnati Reds | 10 | 6 | 0 | 0 | .600 | .600 | .800 | 1.400 | |
|
New York Yankees | 11 | 6 | 1 | 4 | .545 | .583 | .818 | 1.402 | |
 |
Pittsburgh Pirates | 38 | 18 | 5 | 14 | .474 | .535 | .868 | 1.403 | |
|
Colorado Rockies | 12 | 6 | 1 | 4 | .500 | .571 | .833 | 1.405 | |
|
Atlanta Braves | 21 | 8 | 2 | 7 | .381 | .500 | .905 | 1.405 | |
|
Arizona Diamondbacks | 11 | 5 | 1 | 4 | .455 | .500 | .909 | 1.409 | |
 |
San Diego Padres | 11 | 5 | 1 | 1 | .455 | .500 | .909 | 1.409 | |
|
Washington Nationals | 11 | 4 | 2 | 2 | .364 | .500 | .909 | 1.409 | |
|
Colorado Rockies | 17 | 8 | 2 | 4 | .471 | .471 | .941 | 1.412 | |
|
Baltimore Orioles | 10 | 5 | 0 | 3 | .500 | .615 | .800 | 1.415 | |
|
Washington Nationals | 12 | 6 | 1 | 3 | .500 | .500 | .917 | 1.417 | |
|
Chicago White Sox | 12 | 5 | 2 | 2 | .417 | .417 | 1.000 | 1.417 | |
|
Seattle Mariners | 11 | 4 | 2 | 5 | .364 | .417 | 1.000 | 1.417 | |
|
Pittsburgh Pirates | 12 | 5 | 2 | 6 | .417 | .500 | .917 | 1.417 | |
|
Arizona Diamondbacks | 13 | 6 | 1 | 2 | .462 | .500 | .923 | 1.423 | |
|
Oakland Athletics | 24 | 9 | 4 | 9 | .375 | .464 | .958 | 1.423 | |
|
Miami Marlins | 27 | 12 | 3 | 5 | .444 | .464 | .963 | 1.427 | |
|
Oakland Athletics | 15 | 8 | 0 | 3 | .533 | .563 | .867 | 1.429 | |
 |
Washington Nationals | 12 | 6 | 1 | 3 | .500 | .600 | .833 | 1.433 | |
|
Cincinnati Reds | 12 | 6 | 1 | 3 | .500 | .600 | .833 | 1.433 |
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.