MLB Batting Splits 2019
Performance splits by handedness, home/away, and situational categories.
| Player | Team | Split | AB | H | HR | RBI | AVG | OBP | SLG | OPS ▼ |
|---|---|---|---|---|---|---|---|---|---|---|
 |
Chicago White Sox | 12 | 4 | 1 | 2 | .333 | .400 | .750 | 1.150 | |
 |
Colorado Rockies | 24 | 10 | 1 | 5 | .417 | .481 | .667 | 1.148 | |
|
Detroit Tigers | 15 | 5 | 1 | 3 | .333 | .545 | .600 | 1.145 | |
 |
Baltimore Orioles | 25 | 10 | 1 | 4 | .400 | .464 | .680 | 1.144 | |
|
Kansas City Royals | 11 | 4 | 1 | 1 | .364 | .417 | .727 | 1.144 | |
 |
Kansas City Royals | 19 | 6 | 2 | 6 | .316 | .458 | .684 | 1.143 | |
 |
Houston Astros | 21 | 8 | 2 | 5 | .381 | .381 | .762 | 1.143 | |
|
Milwaukee Brewers | 66 | 27 | 3 | 9 | .409 | .473 | .667 | 1.140 | |
  |
Atlanta Braves | 13 | 7 | 0 | 0 | .538 | .600 | .538 | 1.138 | |
 |
Boston Red Sox | 21 | 6 | 3 | 6 | .286 | .375 | .762 | 1.137 | |
|
Minnesota Twins | 24 | 9 | 2 | 7 | .375 | .385 | .750 | 1.135 | |
|
Cleveland Indians | 12 | 4 | 1 | 1 | .333 | .385 | .750 | 1.135 | |
|
Baltimore Orioles | 30 | 9 | 3 | 8 | .300 | .400 | .733 | 1.133 | |
|
Chicago White Sox | 36 | 14 | 2 | 8 | .389 | .410 | .722 | 1.132 | |
 |
Los Angeles Dodgers | 24 | 6 | 4 | 5 | .250 | .379 | .750 | 1.129 | |
 |
Chicago White Sox | 15 | 7 | 0 | 1 | .467 | .529 | .600 | 1.129 | |
 |
Milwaukee Brewers | 21 | 9 | 1 | 5 | .429 | .462 | .667 | 1.128 | |
 |
Cincinnati Reds | 19 | 5 | 3 | 5 | .263 | .391 | .737 | 1.128 | |
|
Detroit Tigers | 12 | 5 | 1 | 5 | .417 | .462 | .667 | 1.128 | |
|
Cincinnati Reds | 11 | 2 | 2 | 4 | .182 | .400 | .727 | 1.127 | |
|
Arizona Diamondbacks | 26 | 9 | 2 | 6 | .346 | .433 | .692 | 1.126 | |
|
Miami Marlins | 25 | 6 | 3 | 5 | .240 | .406 | .720 | 1.126 | |
|
Miami Marlins | 17 | 5 | 2 | 4 | .294 | .478 | .647 | 1.125 | |
 |
Los Angeles Dodgers | 23 | 7 | 2 | 5 | .304 | .385 | .739 | 1.124 | |
|
Texas Rangers | 17 | 7 | 1 | 2 | .412 | .474 | .647 | 1.121 | |
|
Philadelphia Phillies | 18 | 7 | 1 | 7 | .389 | .450 | .667 | 1.117 | |
 |
St. Louis Cardinals | 22 | 9 | 1 | 2 | .409 | .480 | .636 | 1.116 | |
|
Atlanta Braves | 73 | 24 | 7 | 20 | .329 | .430 | .685 | 1.115 | |
|
Texas Rangers | 21 | 8 | 2 | 5 | .381 | .444 | .667 | 1.111 | |
 |
Chicago Cubs | 51 | 18 | 5 | 11 | .353 | .382 | .725 | 1.107 | |
|
Texas Rangers | 69 | 24 | 6 | 14 | .348 | .425 | .681 | 1.106 | |
|
Los Angeles Angels | 14 | 7 | 0 | 0 | .500 | .533 | .571 | 1.105 | |
|
Pittsburgh Pirates | 22 | 7 | 1 | 6 | .318 | .423 | .682 | 1.105 | |
|
Atlanta Braves | 31 | 11 | 3 | 8 | .355 | .394 | .710 | 1.104 | |
 |
Milwaukee Brewers | 68 | 20 | 9 | 18 | .294 | .368 | .735 | 1.104 | |
|
Philadelphia Phillies | 51 | 18 | 3 | 13 | .353 | .455 | .647 | 1.102 | |
|
Los Angeles Angels | 14 | 7 | 0 | 2 | .500 | .529 | .571 | 1.101 | |
|
New York Yankees | 19 | 5 | 2 | 4 | .263 | .417 | .684 | 1.101 | |
|
Seattle Mariners | 20 | 7 | 1 | 3 | .350 | .500 | .600 | 1.100 | |
  |
Seattle Mariners | 10 | 3 | 1 | 2 | .300 | .300 | .800 | 1.100 | |
|
Minnesota Twins | 19 | 5 | 3 | 3 | .263 | .364 | .737 | 1.100 | |
|
Colorado Rockies | 11 | 4 | 1 | 2 | .364 | .462 | .636 | 1.098 | |
|
Los Angeles Dodgers | 20 | 5 | 3 | 5 | .250 | .348 | .750 | 1.098 | |
|
Detroit Tigers | 17 | 6 | 1 | 4 | .353 | .450 | .647 | 1.097 | |
|
St. Louis Cardinals | 25 | 8 | 2 | 2 | .320 | .414 | .680 | 1.094 | |
 |
New York Mets | 11 | 4 | 1 | 3 | .364 | .364 | .727 | 1.091 | |
|
New York Mets | 22 | 9 | 1 | 2 | .409 | .500 | .591 | 1.091 | |
|
Houston Astros | 22 | 8 | 2 | 5 | .364 | .364 | .727 | 1.091 | |
|
New York Mets | 24 | 10 | 1 | 4 | .417 | .462 | .625 | 1.087 | |
|
Kansas City Royals | 25 | 9 | 2 | 9 | .360 | .407 | .680 | 1.087 |
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.
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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.