MLB Batting Splits 2025
Performance splits by handedness, home/away, and situational categories.
| Player | Team | Split | AB | H | HR | RBI | AVG | OBP | SLG | OPS ▲ |
|---|---|---|---|---|---|---|---|---|---|---|
 |
Philadelphia Phillies | 22 | 3 | 0 | 1 | .136 | .174 | .182 | .356 | |
 |
Colorado Rockies | 14 | 2 | 0 | 0 | .143 | .143 | .214 | .357 | |
 |
Washington Nationals | 19 | 3 | 0 | 0 | .158 | .200 | .158 | .358 | |
 |
Colorado Rockies | 19 | 3 | 0 | 0 | .158 | .200 | .158 | .358 | |
|
Cleveland Indians | 24 | 3 | 0 | 0 | .125 | .192 | .167 | .359 | |
 |
Philadelphia Phillies | 48 | 6 | 0 | 2 | .125 | .192 | .167 | .359 | |
|
Milwaukee Brewers | 24 | 4 | 0 | 3 | .167 | .192 | .167 | .359 | |
  |
Cincinnati Reds | 25 | 3 | 1 | 3 | .120 | .120 | .240 | .360 | |
 |
Texas Rangers | 25 | 4 | 0 | 1 | .160 | .160 | .200 | .360 | |
 |
Houston Astros | 29 | 3 | 0 | 6 | .103 | .257 | .103 | .361 | |
 |
Texas Rangers | 25 | 3 | 0 | 2 | .120 | .241 | .120 | .361 | |
 |
Arizona Diamondbacks | 23 | 3 | 0 | 0 | .130 | .231 | .130 | .361 | |
|
Texas Rangers | 11 | 2 | 0 | 1 | .182 | .182 | .182 | .364 | |
|
San Francisco Giants | 11 | 2 | 0 | 1 | .182 | .182 | .182 | .364 | |
|
Baltimore Orioles | 11 | 2 | 0 | 0 | .182 | .182 | .182 | .364 | |
|
San Francisco Giants | 11 | 2 | 0 | 1 | .182 | .182 | .182 | .364 | |
 |
Los Angeles Dodgers | 11 | 2 | 0 | 0 | .182 | .182 | .182 | .364 | |
 |
Milwaukee Brewers | 11 | 2 | 0 | 0 | .182 | .182 | .182 | .364 | |
|
Texas Rangers | 11 | 2 | 0 | 1 | .182 | .182 | .182 | .364 | |
 |
New York Mets | 11 | 2 | 0 | 0 | .182 | .182 | .182 | .364 | |
|
Pittsburgh Pirates | 11 | 2 | 0 | 0 | .182 | .182 | .182 | .364 | |
|
Philadelphia Phillies | 11 | 2 | 0 | 0 | .182 | .182 | .182 | .364 | |
|
Cleveland Indians | 11 | 2 | 0 | 0 | .182 | .182 | .182 | .364 | |
|
San Francisco Giants | 11 | 2 | 0 | 0 | .182 | .182 | .182 | .364 | |
 |
Texas Rangers | 11 | 2 | 0 | 0 | .182 | .182 | .182 | .364 | |
|
Arizona Diamondbacks | 11 | 2 | 0 | 0 | .182 | .182 | .182 | .364 | |
  |
San Francisco Giants | 11 | 2 | 0 | 0 | .182 | .182 | .182 | .364 | |
|
Detroit Tigers | 11 | 2 | 0 | 0 | .182 | .182 | .182 | .364 | |
|
Milwaukee Brewers | 11 | 2 | 0 | 0 | .182 | .182 | .182 | .364 | |
|
Cleveland Indians | 11 | 2 | 0 | 0 | .182 | .182 | .182 | .364 | |
|
Minnesota Twins | 16 | 2 | 0 | 2 | .125 | .176 | .188 | .364 | |
|
New York Yankees | 21 | 2 | 0 | 1 | .095 | .174 | .190 | .364 | |
|
Seattle Mariners | 11 | 2 | 0 | 0 | .182 | .182 | .182 | .364 | |
|
Toronto Blue Jays | 11 | 2 | 0 | 0 | .182 | .182 | .182 | .364 | |
|
Tampa Bay Rays | 19 | 2 | 0 | 1 | .105 | .261 | .105 | .366 | |
|
Cincinnati Reds | 10 | 1 | 0 | 2 | .100 | .167 | .200 | .367 | |
|
Cleveland Indians | 20 | 2 | 0 | 3 | .100 | .217 | .150 | .367 | |
 |
New York Mets | 22 | 3 | 0 | 1 | .136 | .231 | .136 | .367 | |
|
San Francisco Giants | 20 | 2 | 0 | 0 | .100 | .217 | .150 | .367 | |
 |
San Diego Padres | 10 | 1 | 0 | 1 | .100 | .167 | .200 | .367 | |
|
Miami Marlins | 24 | 4 | 0 | 0 | .167 | .200 | .167 | .367 | |
 |
St. Louis Cardinals | 13 | 2 | 0 | 0 | .154 | .214 | .154 | .368 | |
|
San Francisco Giants | 13 | 2 | 0 | 1 | .154 | .214 | .154 | .368 | |
|
Arizona Diamondbacks | 13 | 2 | 0 | 0 | .154 | .214 | .154 | .368 | |
|
Arizona Diamondbacks | 13 | 2 | 0 | 0 | .154 | .214 | .154 | .368 | |
|
Miami Marlins | 24 | 3 | 0 | 1 | .125 | .160 | .208 | .368 | |
|
Detroit Tigers | 17 | 2 | 0 | 0 | .118 | .250 | .118 | .368 | |
|
Boston Red Sox | 13 | 2 | 0 | 1 | .154 | .214 | .154 | .368 | |
|
Milwaukee Brewers | 17 | 2 | 0 | 0 | .118 | .250 | .118 | .368 | |
 |
Baltimore Orioles | 17 | 2 | 0 | 1 | .118 | .250 | .118 | .368 |
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.
Related MLB Tools
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MLB DFS Stacks
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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.