MLB Batting Splits 2019
Performance splits by handedness, home/away, and situational categories.
| Player | Team | Split | AB | H | HR | RBI | AVG | OBP | SLG | OPS ▼ |
|---|---|---|---|---|---|---|---|---|---|---|
 |
Baltimore Orioles | 12 | 4 | 2 | 5 | .333 | .429 | .917 | 1.345 | |
 |
Seattle Mariners | 64 | 22 | 8 | 22 | .344 | .500 | .844 | 1.344 | |
 |
San Francisco Giants | 23 | 9 | 2 | 12 | .391 | .517 | .826 | 1.343 | |
 |
Chicago White Sox | 21 | 10 | 1 | 3 | .476 | .577 | .762 | 1.339 | |
 |
Chicago Cubs | 16 | 7 | 1 | 5 | .438 | .526 | .813 | 1.339 | |
|
Toronto Blue Jays | 12 | 6 | 0 | 4 | .500 | .500 | .833 | 1.333 | |
 |
Pittsburgh Pirates | 47 | 18 | 4 | 9 | .383 | .524 | .809 | 1.332 | |
|
Philadelphia Phillies | 28 | 10 | 4 | 5 | .357 | .471 | .857 | 1.328 | |
 |
Los Angeles Angels | 11 | 4 | 2 | 5 | .364 | .417 | .909 | 1.326 | |
 |
Boston Red Sox | 13 | 5 | 1 | 1 | .385 | .556 | .769 | 1.325 | |
 |
Baltimore Orioles | 14 | 6 | 1 | 5 | .429 | .467 | .857 | 1.324 | |
  |
Texas Rangers | 14 | 6 | 2 | 6 | .429 | .467 | .857 | 1.324 | |
 |
Toronto Blue Jays | 68 | 29 | 8 | 28 | .426 | .466 | .853 | 1.319 | |
 |
Oakland Athletics | 11 | 4 | 1 | 1 | .364 | .500 | .818 | 1.318 | |
|
Miami Marlins | 27 | 13 | 2 | 6 | .481 | .500 | .815 | 1.315 | |
|
Detroit Tigers | 13 | 5 | 2 | 3 | .385 | .385 | .923 | 1.308 | |
 |
Arizona Diamondbacks | 13 | 5 | 2 | 3 | .385 | .385 | .923 | 1.308 | |
|
Los Angeles Angels | 13 | 5 | 1 | 5 | .385 | .385 | .923 | 1.308 | |
|
Los Angeles Angels | 12 | 4 | 2 | 2 | .333 | .467 | .833 | 1.300 | |
|
Colorado Rockies | 24 | 8 | 3 | 8 | .333 | .467 | .833 | 1.300 | |
|
Toronto Blue Jays | 20 | 9 | 1 | 4 | .450 | .450 | .850 | 1.300 | |
|
St. Louis Cardinals | 11 | 5 | 1 | 3 | .455 | .571 | .727 | 1.299 | |
 |
Pittsburgh Pirates | 18 | 5 | 3 | 6 | .278 | .409 | .889 | 1.298 | |
|
Toronto Blue Jays | 24 | 9 | 3 | 7 | .375 | .464 | .833 | 1.298 | |
|
New York Yankees | 21 | 8 | 3 | 8 | .381 | .440 | .857 | 1.297 | |
|
San Francisco Giants | 12 | 5 | 1 | 3 | .417 | .462 | .833 | 1.295 | |
|
Colorado Rockies | 12 | 5 | 1 | 1 | .417 | .462 | .833 | 1.295 | |
|
New York Yankees | 18 | 6 | 3 | 7 | .333 | .400 | .889 | 1.289 | |
|
Texas Rangers | 23 | 9 | 2 | 5 | .391 | .462 | .826 | 1.288 | |
|
Pittsburgh Pirates | 30 | 12 | 3 | 7 | .400 | .455 | .833 | 1.288 | |
|
Milwaukee Brewers | 22 | 7 | 4 | 5 | .318 | .423 | .864 | 1.287 | |
 |
Arizona Diamondbacks | 14 | 7 | 0 | 0 | .500 | .500 | .786 | 1.286 | |
|
Milwaukee Brewers | 14 | 6 | 0 | 5 | .429 | .429 | .857 | 1.286 | |
  |
Chicago White Sox | 10 | 3 | 2 | 4 | .300 | .385 | .900 | 1.285 | |
|
St. Louis Cardinals | 24 | 9 | 3 | 5 | .375 | .444 | .833 | 1.278 | |
|
Cleveland Indians | 29 | 13 | 1 | 5 | .448 | .484 | .793 | 1.277 | |
|
Colorado Rockies | 13 | 5 | 2 | 3 | .385 | .429 | .846 | 1.275 | |
|
Chicago White Sox | 13 | 5 | 2 | 3 | .385 | .429 | .846 | 1.275 | |
|
Detroit Tigers | 12 | 4 | 2 | 5 | .333 | .357 | .917 | 1.274 | |
|
Kansas City Royals | 11 | 4 | 2 | 2 | .364 | .364 | .909 | 1.273 | |
|
San Francisco Giants | 10 | 3 | 2 | 4 | .300 | .364 | .900 | 1.264 | |
 |
Los Angeles Angels | 15 | 7 | 1 | 6 | .467 | .529 | .733 | 1.263 | |
|
New York Yankees | 24 | 8 | 4 | 9 | .333 | .429 | .833 | 1.262 | |
|
Chicago White Sox | 31 | 14 | 1 | 7 | .452 | .486 | .774 | 1.260 | |
|
Seattle Mariners | 16 | 6 | 2 | 3 | .375 | .444 | .813 | 1.257 | |
|
Pittsburgh Pirates | 23 | 8 | 3 | 9 | .348 | .429 | .826 | 1.255 | |
 |
Los Angeles Dodgers | 21 | 8 | 2 | 2 | .381 | .536 | .714 | 1.250 | |
 |
St. Louis Cardinals | 27 | 10 | 3 | 6 | .370 | .469 | .778 | 1.247 | |
|
San Francisco Giants | 15 | 5 | 2 | 3 | .333 | .444 | .800 | 1.244 | |
|
Detroit Tigers | 24 | 11 | 0 | 4 | .458 | .536 | .708 | 1.244 |
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.