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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.
Connor Wong headshot
New York Yankees
.639 OPS
AVG .286
OBP .353
SLG .286
HR 0
AB 14
H 4
RBI 2
Andres Chaparro headshot
Chicago Cubs
.639 OPS
AVG .214
OBP .353
SLG .286
HR 0
AB 14
H 3
RBI 0
Pavin Smith headshot
Miami Marlins
.639 OPS
AVG .167
OBP .250
SLG .389
HR 1
AB 18
H 3
RBI 1
Salvador Perez headshot
Seattle Mariners
.639 OPS
AVG .261
OBP .292
SLG .348
HR 0
AB 23
H 6
RBI 5
Kyle Manzardo headshot
Texas Rangers
.639 OPS
AVG .167
OBP .250
SLG .389
HR 1
AB 18
H 3
RBI 3
JJ Wetherholt headshot
Arizona Diamondbacks
.639 OPS
AVG .265
OBP .286
SLG .353
HR 1
AB 34
H 9
RBI 2
Ryan Vilade headshot
Boston Red Sox
.639 OPS
AVG .257
OBP .325
SLG .314
HR 0
AB 35
H 9
RBI 1
Michael Massey headshot
Minnesota Twins
.639 OPS
AVG .222
OBP .222
SLG .417
HR 2
AB 36
H 8
RBI 3
Wenceel Perez headshot
Kansas City Royals
.638 OPS
AVG .200
OBP .238
SLG .400
HR 1
AB 20
H 4
RBI 3
Jonah Cox headshot
St. Louis Cardinals
.638 OPS
AVG .304
OBP .333
SLG .304
HR 0
AB 23
H 7
RBI 3
Eugenio Suarez headshot
Atlanta Braves
.637 OPS
AVG .143
OBP .208
SLG .429
HR 2
AB 21
H 3
RBI 5
Kevin McGonigle headshot
Texas Rangers
.637 OPS
AVG .280
OBP .357
SLG .280
HR 0
AB 25
H 7
RBI 3
Andrew Benintendi headshot
Cleveland Indians
.637 OPS
AVG .280
OBP .357
SLG .280
HR 0
AB 25
H 7
RBI 3
Austin Wells headshot
Seattle Mariners
.636 OPS
AVG .273
OBP .273
SLG .364
HR 0
AB 11
H 3
RBI 0
Corbin Carroll headshot
Pittsburgh Pirates
.636 OPS
AVG .200
OBP .276
SLG .360
HR 1
AB 25
H 5
RBI 2
Masyn Winn headshot
Chicago White Sox
.636 OPS
AVG .273
OBP .273
SLG .364
HR 0
AB 11
H 3
RBI 3
Ryan McMahon headshot
Baltimore Orioles
.636 OPS
AVG .258
OBP .281
SLG .355
HR 0
AB 31
H 8
RBI 4
Nick Sogard headshot
Seattle Mariners
.636 OPS
AVG .273
OBP .273
SLG .364
HR 0
AB 11
H 3
RBI 0
Griffin Conine headshot
Arizona Diamondbacks
.636 OPS
AVG .182
OBP .182
SLG .455
HR 1
AB 11
H 2
RBI 1
Christian Koss headshot
Boston Red Sox
.636 OPS
AVG .273
OBP .273
SLG .364
HR 0
AB 11
H 3
RBI 0
TJ Rumfield headshot
Los Angeles Dodgers
.636 OPS
AVG .220
OBP .319
SLG .317
HR 1
AB 41
H 9
RBI 4
David Hamilton headshot
New York Mets
.636 OPS
AVG .273
OBP .273
SLG .364
HR 0
AB 11
H 3
RBI 1
Ildemaro Vargas headshot
Pittsburgh Pirates
.636 OPS
AVG .250
OBP .286
SLG .350
HR 0
AB 20
H 5
RBI 2
Daylen Lile headshot
St. Louis Cardinals
.636 OPS
AVG .200
OBP .276
SLG .360
HR 1
AB 25
H 5
RBI 5
Brice Matthews headshot
Los Angeles Angels
.636 OPS
AVG .273
OBP .273
SLG .364
HR 0
AB 11
H 3
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.
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