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MLB Batting Splits 2026

Performance splits by handedness, home/away, and situational categories.

Batting splits break down a hitter's performance across different game situations. Platoon splits (vs LHP/RHP) are the most predictive for DFS and prop betting. Minimum 10 at-bats displayed.
Nick Gonzales headshot
Cleveland Indians
1.231 OPS
AVG .385
OBP .385
SLG .846
HR 1
AB 13
H 5
RBI 1
Tristan Peters headshot
Detroit Tigers
1.231 OPS
AVG .462
OBP .500
SLG .731
HR 1
AB 26
H 12
RBI 6
Brandon Marsh headshot
Oakland Athletics
1.231 OPS
AVG .538
OBP .538
SLG .692
HR 0
AB 13
H 7
RBI 1
Josh Naylor headshot
Tampa Bay Rays
1.230 OPS
AVG .350
OBP .480
SLG .750
HR 2
AB 20
H 7
RBI 2
Juan Soto headshot
Kansas City Royals
1.229 OPS
AVG .200
OBP .429
SLG .800
HR 2
AB 10
H 2
RBI 4
Alec Bohm headshot
St. Louis Cardinals
1.229 OPS
AVG .533
OBP .563
SLG .667
HR 0
AB 15
H 8
RBI 4
JJ Bleday headshot
Atlanta Braves
1.229 OPS
AVG .400
OBP .429
SLG .800
HR 2
AB 20
H 8
RBI 5
Brice Turang headshot
Washington Nationals
1.228 OPS
AVG .353
OBP .522
SLG .706
HR 2
AB 17
H 6
RBI 2
Matt Olson headshot
Arizona Diamondbacks
1.227 OPS
AVG .308
OBP .419
SLG .808
HR 3
AB 26
H 8
RBI 7
Andres Gimenez headshot
Oakland Athletics
1.227 OPS
AVG .455
OBP .500
SLG .727
HR 1
AB 22
H 10
RBI 6
Joshua Baez headshot
Colorado Rockies
1.227 OPS
AVG .455
OBP .500
SLG .727
HR 1
AB 11
H 5
RBI 2
Shohei Ohtani headshot
Boston Red Sox
1.227 OPS
AVG .364
OBP .500
SLG .727
HR 1
AB 11
H 4
RBI 2
Miguel Vargas headshot
San Francisco Giants
1.227 OPS
AVG .364
OBP .500
SLG .727
HR 1
AB 11
H 4
RBI 2
Wilyer Abreu headshot
San Diego Padres
1.227 OPS
AVG .455
OBP .500
SLG .727
HR 0
AB 11
H 5
RBI 1
Ty France headshot
San Francisco Giants
1.222 OPS
AVG .533
OBP .556
SLG .667
HR 0
AB 15
H 8
RBI 5
Caleb Durbin headshot
Seattle Mariners
1.222 OPS
AVG .450
OBP .522
SLG .700
HR 1
AB 20
H 9
RBI 2
Taylor Ward headshot
Texas Rangers
1.222 OPS
AVG .533
OBP .556
SLG .667
HR 0
AB 15
H 8
RBI 5
Ben Rice headshot
Oakland Athletics
1.221 OPS
AVG .391
OBP .481
SLG .739
HR 1
AB 23
H 9
RBI 7
Carlos Correa headshot
Boston Red Sox
1.221 OPS
AVG .391
OBP .481
SLG .739
HR 2
AB 23
H 9
RBI 7
Tyrone Taylor headshot
Milwaukee Brewers
1.221 OPS
AVG .438
OBP .471
SLG .750
HR 1
AB 16
H 7
RBI 6
Sam Antonacci headshot
Colorado Rockies
1.220 OPS
AVG .545
OBP .583
SLG .636
HR 0
AB 11
H 6
RBI 0
Shohei Ohtani headshot
Washington Nationals
1.220 OPS
AVG .400
OBP .353
SLG .867
HR 2
AB 15
H 6
RBI 6
Jonny DeLuca headshot
Philadelphia Phillies
1.218 OPS
AVG .333
OBP .385
SLG .833
HR 2
AB 12
H 4
RBI 3
Ben Rice headshot
Cincinnati Reds
1.218 OPS
AVG .333
OBP .385
SLG .833
HR 2
AB 12
H 4
RBI 4
Zack Gelof headshot
Philadelphia Phillies
1.218 OPS
AVG .333
OBP .385
SLG .833
HR 1
AB 12
H 4
RBI 3

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.

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.
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