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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.
Miguel Amaya headshot
Philadelphia Phillies
.473 OPS
AVG .200
OBP .273
SLG .200
HR 0
AB 20
H 4
RBI 0
Christian Koss headshot
Milwaukee Brewers
.473 OPS
AVG .200
OBP .273
SLG .200
HR 0
AB 10
H 2
RBI 1
Justin Crawford headshot
St. Louis Cardinals
.472 OPS
AVG .188
OBP .222
SLG .250
HR 0
AB 16
H 3
RBI 1
Luis Arraez headshot
Toronto Blue Jays
.472 OPS
AVG .192
OBP .241
SLG .231
HR 0
AB 26
H 5
RBI 2
Willi Castro headshot
Miami Marlins
.472 OPS
AVG .167
OBP .250
SLG .222
HR 0
AB 18
H 3
RBI 2
Jonah Heim headshot
Houston Astros
.472 OPS
AVG .167
OBP .250
SLG .222
HR 0
AB 18
H 3
RBI 2
Hao-Yu Lee headshot
Cleveland Indians
.472 OPS
AVG .179
OBP .258
SLG .214
HR 0
AB 28
H 5
RBI 1
Jordan Beck headshot
Los Angeles Dodgers
.472 OPS
AVG .188
OBP .222
SLG .250
HR 0
AB 16
H 3
RBI 1
Royce Lewis headshot
Texas Rangers
.472 OPS
AVG .160
OBP .192
SLG .280
HR 1
AB 25
H 4
RBI 5
Otto Lopez headshot
St. Louis Cardinals
.472 OPS
AVG .160
OBP .192
SLG .280
HR 0
AB 25
H 4
RBI 2
Andres Gimenez headshot
Houston Astros
.472 OPS
AVG .167
OBP .250
SLG .222
HR 0
AB 18
H 3
RBI 0
Bo Bichette headshot
Miami Marlins
.472 OPS
AVG .176
OBP .276
SLG .196
HR 0
AB 51
H 9
RBI 3
Braden Montgomery headshot
New York Yankees
.472 OPS
AVG .154
OBP .241
SLG .231
HR 0
AB 26
H 4
RBI 1
Steven Kwan headshot
Toronto Blue Jays
.471 OPS
AVG .235
OBP .235
SLG .235
HR 0
AB 17
H 4
RBI 0
Ivan Herrera headshot
Atlanta Braves
.471 OPS
AVG .200
OBP .231
SLG .240
HR 0
AB 25
H 5
RBI 1
Ozzie Albies headshot
Cincinnati Reds
.471 OPS
AVG .200
OBP .231
SLG .240
HR 0
AB 25
H 5
RBI 1
Luis Arraez headshot
Seattle Mariners
.471 OPS
AVG .200
OBP .231
SLG .240
HR 0
AB 25
H 5
RBI 1
Jeremiah Jackson headshot
Tampa Bay Rays
.471 OPS
AVG .200
OBP .231
SLG .240
HR 0
AB 25
H 5
RBI 1
Jacob Young headshot
Cincinnati Reds
.470 OPS
AVG .190
OBP .280
SLG .190
HR 0
AB 21
H 4
RBI 3
Matt McLain headshot
Chicago Cubs
.470 OPS
AVG .161
OBP .212
SLG .258
HR 1
AB 31
H 5
RBI 1
CJ Abrams headshot
San Diego Padres
.470 OPS
AVG .143
OBP .280
SLG .190
HR 0
AB 21
H 3
RBI 0
Lane Thomas headshot
Detroit Tigers
.469 OPS
AVG .133
OBP .235
SLG .233
HR 1
AB 30
H 4
RBI 1
Maikel Garcia headshot
New York Yankees
.469 OPS
AVG .174
OBP .208
SLG .261
HR 0
AB 23
H 4
RBI 0
Jose Fermin headshot
Milwaukee Brewers
.469 OPS
AVG .138
OBP .194
SLG .276
HR 1
AB 29
H 4
RBI 3
Angel Genao headshot
Detroit Tigers
.469 OPS
AVG .174
OBP .208
SLG .261
HR 0
AB 23
H 4
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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