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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.
Jake Bauers headshot
Chicago Cubs
.598 OPS
AVG .171
OBP .341
SLG .257
HR 1
AB 35
H 6
RBI 6
Zach Neto headshot
New York Yankees
.598 OPS
AVG .214
OBP .313
SLG .286
HR 0
AB 28
H 6
RBI 2
Ernie Clement headshot
Tampa Bay Rays
.598 OPS
AVG .292
OBP .306
SLG .292
HR 0
AB 48
H 14
RBI 0
Drake Baldwin headshot
St. Louis Cardinals
.598 OPS
AVG .227
OBP .370
SLG .227
HR 0
AB 22
H 5
RBI 1
Braden Montgomery headshot
Houston Astros
.598 OPS
AVG .238
OBP .360
SLG .238
HR 0
AB 21
H 5
RBI 1
Edouard Julien headshot
Houston Astros
.598 OPS
AVG .250
OBP .348
SLG .250
HR 0
AB 20
H 5
RBI 4
Cal Raleigh headshot
Los Angeles Angels
.597 OPS
AVG .182
OBP .302
SLG .295
HR 1
AB 44
H 8
RBI 10
Lourdes Gurriel Jr. headshot
Milwaukee Brewers
.596 OPS
AVG .263
OBP .333
SLG .263
HR 0
AB 19
H 5
RBI 1
Mickey Gasper headshot
Atlanta Braves
.596 OPS
AVG .263
OBP .333
SLG .263
HR 0
AB 19
H 5
RBI 1
Tyler Freeman headshot
San Diego Padres
.596 OPS
AVG .261
OBP .292
SLG .304
HR 0
AB 23
H 6
RBI 3
Darell Hernaiz headshot
Kansas City Royals
.596 OPS
AVG .263
OBP .333
SLG .263
HR 0
AB 19
H 5
RBI 2
Jose Ramirez headshot
Detroit Tigers
.596 OPS
AVG .163
OBP .294
SLG .302
HR 1
AB 43
H 7
RBI 6
Jung Hoo Lee headshot
Chicago Cubs
.596 OPS
AVG .261
OBP .292
SLG .304
HR 0
AB 23
H 6
RBI 1
Leody Taveras headshot
Seattle Mariners
.596 OPS
AVG .231
OBP .250
SLG .346
HR 0
AB 26
H 6
RBI 4
Ceddanne Rafaela headshot
Seattle Mariners
.596 OPS
AVG .278
OBP .263
SLG .333
HR 0
AB 18
H 5
RBI 3
Seiya Suzuki headshot
Chicago White Sox
.596 OPS
AVG .160
OBP .276
SLG .320
HR 1
AB 25
H 4
RBI 2
Matt McLain headshot
San Diego Padres
.596 OPS
AVG .200
OBP .346
SLG .250
HR 0
AB 20
H 4
RBI 1
Jazz Chisholm Jr. headshot
Baltimore Orioles
.595 OPS
AVG .200
OBP .310
SLG .286
HR 0
AB 35
H 7
RBI 3
Austin Riley headshot
Miami Marlins
.595 OPS
AVG .163
OBP .208
SLG .388
HR 3
AB 49
H 8
RBI 10
Patrick Bailey headshot
Chicago White Sox
.594 OPS
AVG .217
OBP .333
SLG .261
HR 0
AB 23
H 5
RBI 2
Jarren Duran headshot
Toronto Blue Jays
.594 OPS
AVG .213
OBP .275
SLG .319
HR 1
AB 47
H 10
RBI 4
Eugenio Suarez headshot
Philadelphia Phillies
.594 OPS
AVG .091
OBP .231
SLG .364
HR 1
AB 11
H 1
RBI 1
Pete Alonso headshot
Milwaukee Brewers
.594 OPS
AVG .091
OBP .231
SLG .364
HR 1
AB 11
H 1
RBI 3
Jose Ramirez headshot
Texas Rangers
.594 OPS
AVG .091
OBP .231
SLG .364
HR 1
AB 11
H 1
RBI 1
Jackson Chourio headshot
Cleveland Indians
.594 OPS
AVG .091
OBP .231
SLG .364
HR 1
AB 11
H 1
RBI 2

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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