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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.
Andrew Vaughn headshot
New York Mets
.940 OPS
AVG .333
OBP .417
SLG .524
HR 1
AB 21
H 7
RBI 3
Bryce Eldridge headshot
Chicago Cubs
.940 OPS
AVG .364
OBP .440
SLG .500
HR 1
AB 22
H 8
RBI 2
Hunter Goodman headshot
Toronto Blue Jays
.940 OPS
AVG .250
OBP .357
SLG .583
HR 1
AB 12
H 3
RBI 2
Matt Olson headshot
Los Angeles Angels
.940 OPS
AVG .250
OBP .357
SLG .583
HR 1
AB 12
H 3
RBI 2
Ryan O'Hearn headshot
Cincinnati Reds
.939 OPS
AVG .289
OBP .386
SLG .553
HR 3
AB 38
H 11
RBI 12
Lourdes Gurriel Jr. headshot
Colorado Rockies
.938 OPS
AVG .353
OBP .350
SLG .588
HR 1
AB 17
H 6
RBI 5
Vinnie Pasquantino headshot
Cincinnati Reds
.938 OPS
AVG .308
OBP .400
SLG .538
HR 1
AB 13
H 4
RBI 3
Riley Greene headshot
Boston Red Sox
.938 OPS
AVG .333
OBP .462
SLG .476
HR 0
AB 21
H 7
RBI 2
Hunter Feduccia headshot
Baltimore Orioles
.938 OPS
AVG .375
OBP .375
SLG .563
HR 1
AB 16
H 6
RBI 1
Jared Triolo headshot
St. Louis Cardinals
.938 OPS
AVG .391
OBP .417
SLG .522
HR 1
AB 23
H 9
RBI 2
Luis Torrens headshot
San Diego Padres
.938 OPS
AVG .250
OBP .250
SLG .688
HR 2
AB 16
H 4
RBI 3
Nathan Lukes headshot
Kansas City Royals
.938 OPS
AVG .391
OBP .417
SLG .522
HR 1
AB 23
H 9
RBI 6
Shohei Ohtani headshot
Toronto Blue Jays
.938 OPS
AVG .250
OBP .438
SLG .500
HR 1
AB 12
H 3
RBI 2
Brett Baty headshot
New York Yankees
.938 OPS
AVG .357
OBP .438
SLG .500
HR 0
AB 14
H 5
RBI 2
Jorge Mateo headshot
Boston Red Sox
.938 OPS
AVG .357
OBP .438
SLG .500
HR 0
AB 14
H 5
RBI 1
Henry Davis headshot
Washington Nationals
.938 OPS
AVG .400
OBP .538
SLG .400
HR 0
AB 10
H 4
RBI 4
Drew Gilbert headshot
Cincinnati Reds
.938 OPS
AVG .308
OBP .400
SLG .538
HR 0
AB 13
H 4
RBI 3
Jose Caballero headshot
Chicago White Sox
.937 OPS
AVG .333
OBP .381
SLG .556
HR 1
AB 18
H 6
RBI 4
Colt Keith headshot
Seattle Mariners
.937 OPS
AVG .364
OBP .391
SLG .545
HR 0
AB 22
H 8
RBI 4
Mike Yastrzemski headshot
New York Mets
.936 OPS
AVG .261
OBP .370
SLG .565
HR 2
AB 23
H 6
RBI 4
Xavier Edwards headshot
St. Louis Cardinals
.936 OPS
AVG .364
OBP .481
SLG .455
HR 0
AB 22
H 8
RBI 2
Zach McKinstry headshot
Oakland Athletics
.935 OPS
AVG .316
OBP .409
SLG .526
HR 1
AB 19
H 6
RBI 2
Aaron Judge headshot
Kansas City Royals
.935 OPS
AVG .250
OBP .385
SLG .550
HR 1
AB 20
H 5
RBI 4
Bryce Eldridge headshot
Atlanta Braves
.935 OPS
AVG .333
OBP .364
SLG .571
HR 1
AB 21
H 7
RBI 3
Kyle Karros headshot
Milwaukee Brewers
.935 OPS
AVG .350
OBP .435
SLG .500
HR 0
AB 20
H 7
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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