Skip to main content

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.
Blake Dunn headshot
New York Mets
.678 OPS
AVG .273
OBP .360
SLG .318
HR 0
AB 22
H 6
RBI 0
Ivan Herrera headshot
Pittsburgh Pirates
.678 OPS
AVG .184
OBP .310
SLG .367
HR 2
AB 49
H 9
RBI 7
Kyle Teel headshot
Cleveland Indians
.678 OPS
AVG .172
OBP .333
SLG .345
HR 1
AB 29
H 5
RBI 6
Pete Alonso headshot
Washington Nationals
.678 OPS
AVG .182
OBP .269
SLG .409
HR 1
AB 22
H 4
RBI 4
Geraldo Perdomo headshot
Texas Rangers
.678 OPS
AVG .273
OBP .360
SLG .318
HR 0
AB 22
H 6
RBI 1
Pete Alonso headshot
Boston Red Sox
.677 OPS
AVG .245
OBP .281
SLG .396
HR 2
AB 53
H 13
RBI 4
Kyle Higashioka headshot
Houston Astros
.677 OPS
AVG .150
OBP .227
SLG .450
HR 2
AB 20
H 3
RBI 4
Mark Vientos headshot
Chicago Cubs
.677 OPS
AVG .150
OBP .227
SLG .450
HR 2
AB 20
H 3
RBI 2
Zach Neto headshot
Chicago White Sox
.676 OPS
AVG .208
OBP .259
SLG .417
HR 1
AB 24
H 5
RBI 4
Liam Hicks headshot
San Francisco Giants
.676 OPS
AVG .231
OBP .214
SLG .462
HR 1
AB 13
H 3
RBI 4
Vaughn Grissom headshot
Texas Rangers
.676 OPS
AVG .275
OBP .326
SLG .350
HR 0
AB 40
H 11
RBI 6
Dillon Dingler headshot
Houston Astros
.676 OPS
AVG .208
OBP .259
SLG .417
HR 1
AB 24
H 5
RBI 3
Jonathan Aranda headshot
Oakland Athletics
.675 OPS
AVG .250
OBP .375
SLG .300
HR 0
AB 20
H 5
RBI 2
Agustin Ramirez headshot
Philadelphia Phillies
.675 OPS
AVG .235
OBP .381
SLG .294
HR 0
AB 17
H 4
RBI 2
Otto Lopez headshot
Chicago Cubs
.675 OPS
AVG .261
OBP .370
SLG .304
HR 0
AB 23
H 6
RBI 1
Joc Pederson headshot
Arizona Diamondbacks
.675 OPS
AVG .176
OBP .263
SLG .412
HR 1
AB 17
H 3
RBI 2
Austin Riley headshot
Boston Red Sox
.675 OPS
AVG .174
OBP .240
SLG .435
HR 1
AB 23
H 4
RBI 3
Tristan Peters headshot
Chicago Cubs
.675 OPS
AVG .167
OBP .286
SLG .389
HR 1
AB 18
H 3
RBI 5
Curtis Mead headshot
San Francisco Giants
.674 OPS
AVG .158
OBP .200
SLG .474
HR 2
AB 19
H 3
RBI 3
Corbin Carroll headshot
St. Louis Cardinals
.674 OPS
AVG .207
OBP .226
SLG .448
HR 1
AB 29
H 6
RBI 3
Keibert Ruiz headshot
Philadelphia Phillies
.674 OPS
AVG .269
OBP .367
SLG .308
HR 0
AB 26
H 7
RBI 2
Marcus Semien headshot
Atlanta Braves
.673 OPS
AVG .200
OBP .273
SLG .400
HR 2
AB 30
H 6
RBI 3
Nick Loftin headshot
Cleveland Indians
.673 OPS
AVG .250
OBP .273
SLG .400
HR 0
AB 20
H 5
RBI 3
Willson Contreras headshot
New York Yankees
.673 OPS
AVG .191
OBP .269
SLG .404
HR 2
AB 47
H 9
RBI 9
Daylen Lile headshot
Los Angeles Angels
.673 OPS
AVG .200
OBP .273
SLG .400
HR 0
AB 10
H 2
RBI 1

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.
START WINNING
Start Winning →