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.
Luis Campusano headshot
San Francisco Giants
.629 OPS
AVG .250
OBP .344
SLG .286
HR 0
AB 28
H 7
RBI 3
Connor Wong headshot
Oakland Athletics
.629 OPS
AVG .143
OBP .200
SLG .429
HR 1
AB 14
H 2
RBI 2
Brent Rooker headshot
New York Yankees
.629 OPS
AVG .158
OBP .261
SLG .368
HR 1
AB 19
H 3
RBI 2
Jonny DeLuca headshot
New York Yankees
.629 OPS
AVG .265
OBP .306
SLG .324
HR 0
AB 34
H 9
RBI 2
Joc Pederson headshot
New York Yankees
.629 OPS
AVG .214
OBP .200
SLG .429
HR 1
AB 14
H 3
RBI 2
Dylan Crews headshot
St. Louis Cardinals
.629 OPS
AVG .214
OBP .200
SLG .429
HR 1
AB 14
H 3
RBI 3
Taylor Ward headshot
Chicago Cubs
.629 OPS
AVG .200
OBP .429
SLG .200
HR 0
AB 15
H 3
RBI 1
Nico Hoerner headshot
Milwaukee Brewers
.628 OPS
AVG .245
OBP .302
SLG .327
HR 1
AB 49
H 12
RBI 4
Jake Burger headshot
Minnesota Twins
.628 OPS
AVG .217
OBP .280
SLG .348
HR 1
AB 23
H 5
RBI 1
Brenton Doyle headshot
Houston Astros
.628 OPS
AVG .278
OBP .350
SLG .278
HR 0
AB 18
H 5
RBI 0
Mauricio Dubon headshot
Los Angeles Dodgers
.628 OPS
AVG .280
OBP .308
SLG .320
HR 0
AB 25
H 7
RBI 4
Wyatt Langford headshot
Minnesota Twins
.628 OPS
AVG .217
OBP .280
SLG .348
HR 1
AB 23
H 5
RBI 2
Jose Caballero headshot
Tampa Bay Rays
.628 OPS
AVG .220
OBP .238
SLG .390
HR 2
AB 41
H 9
RBI 8
Angel Martinez headshot
Detroit Tigers
.628 OPS
AVG .244
OBP .262
SLG .366
HR 0
AB 41
H 10
RBI 3
Chase DeLauter headshot
Boston Red Sox
.628 OPS
AVG .280
OBP .308
SLG .320
HR 0
AB 25
H 7
RBI 1
Manny Machado headshot
New York Mets
.628 OPS
AVG .217
OBP .280
SLG .348
HR 1
AB 23
H 5
RBI 1
Colson Montgomery headshot
Kansas City Royals
.628 OPS
AVG .200
OBP .250
SLG .378
HR 2
AB 45
H 9
RBI 4
Kody Clemens headshot
Chicago White Sox
.627 OPS
AVG .186
OBP .255
SLG .372
HR 2
AB 43
H 8
RBI 4
Oswald Peraza headshot
Texas Rangers
.627 OPS
AVG .233
OBP .294
SLG .333
HR 1
AB 30
H 7
RBI 4
Troy Johnston headshot
San Francisco Giants
.627 OPS
AVG .206
OBP .333
SLG .294
HR 0
AB 34
H 7
RBI 3
Henry Bolte headshot
Kansas City Royals
.627 OPS
AVG .294
OBP .333
SLG .294
HR 0
AB 17
H 5
RBI 1
Jonah Heim headshot
Los Angeles Angels
.627 OPS
AVG .175
OBP .227
SLG .400
HR 2
AB 40
H 7
RBI 7
Ozzie Albies headshot
Miami Marlins
.627 OPS
AVG .171
OBP .286
SLG .341
HR 2
AB 41
H 7
RBI 4
Cody Freeman headshot
Oakland Athletics
.627 OPS
AVG .294
OBP .333
SLG .294
HR 0
AB 17
H 5
RBI 2
Masyn Winn headshot
Atlanta Braves
.627 OPS
AVG .263
OBP .364
SLG .263
HR 0
AB 19
H 5
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 →