MLB Batting Splits 2025
Performance splits by handedness, home/away, and situational categories.
| Player | Team | Split | AB | H | HR | RBI | AVG | OBP | SLG | OPS ▼ |
|---|---|---|---|---|---|---|---|---|---|---|
 |
San Diego Padres | 14 | 5 | 1 | 1 | .357 | .400 | .643 | 1.043 | |
 |
Cleveland Indians | 14 | 5 | 1 | 2 | .357 | .400 | .643 | 1.043 | |
 |
Minnesota Twins | 14 | 5 | 1 | 5 | .357 | .471 | .571 | 1.042 | |
  |
Atlanta Braves | 21 | 6 | 2 | 2 | .286 | .375 | .667 | 1.042 | |
 |
Milwaukee Brewers | 21 | 7 | 2 | 6 | .333 | .375 | .667 | 1.042 | |
 |
Arizona Diamondbacks | 14 | 5 | 1 | 3 | .357 | .471 | .571 | 1.042 | |
  |
Chicago White Sox | 33 | 11 | 2 | 6 | .333 | .405 | .636 | 1.041 | |
|
Arizona Diamondbacks | 19 | 5 | 2 | 4 | .263 | .462 | .579 | 1.040 | |
 |
Washington Nationals | 14 | 6 | 0 | 3 | .429 | .467 | .571 | 1.038 | |
 |
Minnesota Twins | 13 | 6 | 0 | 4 | .462 | .500 | .538 | 1.038 | |
 |
Oakland Athletics | 10 | 4 | 0 | 4 | .400 | .538 | .500 | 1.038 | |
|
Chicago White Sox | 10 | 4 | 0 | 1 | .400 | .538 | .500 | 1.038 | |
 |
Oakland Athletics | 14 | 6 | 0 | 0 | .429 | .467 | .571 | 1.038 | |
|
Oakland Athletics | 16 | 6 | 1 | 2 | .375 | .412 | .625 | 1.037 | |
|
Philadelphia Phillies | 16 | 6 | 1 | 4 | .375 | .412 | .625 | 1.037 | |
|
Tampa Bay Rays | 24 | 7 | 3 | 6 | .292 | .370 | .667 | 1.037 | |
|
Pittsburgh Pirates | 22 | 7 | 1 | 2 | .318 | .400 | .636 | 1.036 | |
 |
New York Mets | 17 | 6 | 1 | 4 | .353 | .389 | .647 | 1.036 | |
 |
Chicago Cubs | 18 | 5 | 1 | 5 | .278 | .480 | .556 | 1.036 | |
|
Boston Red Sox | 50 | 19 | 3 | 8 | .380 | .456 | .580 | 1.036 | |
|
Baltimore Orioles | 20 | 7 | 1 | 2 | .350 | .435 | .600 | 1.035 | |
|
Boston Red Sox | 25 | 9 | 1 | 4 | .360 | .514 | .520 | 1.034 | |
 |
San Diego Padres | 39 | 12 | 3 | 9 | .308 | .341 | .692 | 1.034 | |
|
Arizona Diamondbacks | 25 | 8 | 2 | 3 | .320 | .393 | .640 | 1.033 | |
|
Chicago Cubs | 21 | 7 | 1 | 5 | .333 | .462 | .571 | 1.033 | |
|
Los Angeles Dodgers | 18 | 7 | 0 | 2 | .389 | .476 | .556 | 1.032 | |
|
Chicago Cubs | 18 | 7 | 1 | 1 | .389 | .421 | .611 | 1.032 | |
|
Pittsburgh Pirates | 24 | 8 | 2 | 5 | .333 | .407 | .625 | 1.032 | |
|
Tampa Bay Rays | 20 | 7 | 1 | 5 | .350 | .381 | .650 | 1.031 | |
 |
Los Angeles Angels | 23 | 8 | 1 | 5 | .348 | .464 | .565 | 1.030 | |
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Detroit Tigers | 49 | 17 | 2 | 10 | .347 | .439 | .592 | 1.030 | |
|
Tampa Bay Rays | 21 | 8 | 1 | 1 | .381 | .458 | .571 | 1.030 | |
 |
Tampa Bay Rays | 24 | 6 | 4 | 7 | .250 | .280 | .750 | 1.030 | |
|
Kansas City Royals | 25 | 6 | 4 | 7 | .240 | .310 | .720 | 1.030 | |
|
Tampa Bay Rays | 30 | 10 | 2 | 10 | .333 | .429 | .600 | 1.029 | |
|
St. Louis Cardinals | 22 | 8 | 2 | 4 | .364 | .391 | .636 | 1.028 | |
|
Chicago White Sox | 22 | 8 | 1 | 4 | .364 | .391 | .636 | 1.028 | |
|
Seattle Mariners | 54 | 20 | 3 | 8 | .370 | .435 | .593 | 1.028 | |
|
Philadelphia Phillies | 23 | 8 | 2 | 3 | .348 | .375 | .652 | 1.027 | |
|
Tampa Bay Rays | 26 | 7 | 4 | 10 | .269 | .296 | .731 | 1.027 | |
|
San Diego Padres | 35 | 8 | 5 | 13 | .229 | .341 | .686 | 1.027 | |
|
Milwaukee Brewers | 23 | 9 | 1 | 6 | .391 | .462 | .565 | 1.027 | |
|
Detroit Tigers | 28 | 10 | 1 | 4 | .357 | .455 | .571 | 1.026 | |
|
Philadelphia Phillies | 20 | 9 | 0 | 0 | .450 | .476 | .550 | 1.026 | |
|
Atlanta Braves | 13 | 3 | 2 | 3 | .231 | .333 | .692 | 1.026 | |
|
Baltimore Orioles | 13 | 3 | 2 | 4 | .231 | .333 | .692 | 1.026 | |
 |
Toronto Blue Jays | 25 | 8 | 3 | 6 | .320 | .346 | .680 | 1.026 | |
|
Colorado Rockies | 44 | 14 | 3 | 12 | .318 | .388 | .636 | 1.024 | |
|
Philadelphia Phillies | 12 | 3 | 1 | 2 | .250 | .357 | .667 | 1.024 | |
|
Los Angeles Angels | 55 | 17 | 4 | 11 | .309 | .387 | .636 | 1.023 |
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.