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 | 75 | 27 | 4 | 10 | .360 | .407 | .627 | 1.034 | |
  |
Chicago White Sox | 30 | 9 | 3 | 10 | .300 | .400 | .633 | 1.033 | |
 |
Toronto Blue Jays | 23 | 8 | 2 | 4 | .348 | .423 | .609 | 1.032 | |
 |
Philadelphia Phillies | 28 | 9 | 3 | 4 | .321 | .387 | .643 | 1.030 | |
|
Washington Nationals | 21 | 9 | 1 | 6 | .429 | .458 | .571 | 1.030 | |
|
Boston Red Sox | 10 | 3 | 1 | 2 | .300 | .429 | .600 | 1.029 | |
 |
Oakland Athletics | 12 | 4 | 0 | 0 | .333 | .529 | .500 | 1.029 | |
 |
Baltimore Orioles | 67 | 27 | 2 | 12 | .403 | .431 | .597 | 1.028 | |
 |
Seattle Mariners | 27 | 6 | 4 | 8 | .222 | .323 | .704 | 1.026 | |
|
Chicago White Sox | 69 | 22 | 6 | 20 | .319 | .373 | .652 | 1.026 | |
 |
San Diego Padres | 20 | 6 | 2 | 5 | .300 | .375 | .650 | 1.025 | |
|
Texas Rangers | 27 | 8 | 2 | 6 | .296 | .321 | .704 | 1.025 | |
|
Los Angeles Angels | 28 | 10 | 2 | 5 | .357 | .379 | .643 | 1.022 | |
 |
Oakland Athletics | 15 | 4 | 2 | 4 | .267 | .353 | .667 | 1.020 | |
|
Texas Rangers | 10 | 3 | 1 | 1 | .300 | .417 | .600 | 1.017 | |
|
Milwaukee Brewers | 73 | 21 | 8 | 20 | .288 | .373 | .644 | 1.017 | |
 |
Cleveland Indians | 20 | 6 | 1 | 4 | .300 | .417 | .600 | 1.017 | |
|
Washington Nationals | 13 | 4 | 1 | 3 | .308 | .400 | .615 | 1.015 | |
 |
Texas Rangers | 27 | 7 | 3 | 7 | .259 | .310 | .704 | 1.014 | |
|
Pittsburgh Pirates | 28 | 9 | 2 | 6 | .321 | .406 | .607 | 1.013 | |
|
Minnesota Twins | 65 | 19 | 6 | 13 | .292 | .397 | .615 | 1.013 | |
|
Cincinnati Reds | 70 | 23 | 4 | 9 | .329 | .440 | .571 | 1.012 | |
|
Houston Astros | 18 | 6 | 1 | 3 | .333 | .400 | .611 | 1.011 | |
|
New York Mets | 62 | 18 | 5 | 9 | .290 | .397 | .613 | 1.010 | |
 |
Chicago Cubs | 73 | 23 | 5 | 16 | .315 | .420 | .589 | 1.009 | |
|
Baltimore Orioles | 14 | 5 | 0 | 2 | .357 | .438 | .571 | 1.009 | |
 |
Atlanta Braves | 53 | 20 | 1 | 9 | .377 | .421 | .585 | 1.006 | |
|
Seattle Mariners | 23 | 9 | 1 | 2 | .391 | .440 | .565 | 1.005 | |
|
Texas Rangers | 63 | 21 | 6 | 13 | .333 | .354 | .651 | 1.005 | |
|
Milwaukee Brewers | 38 | 11 | 3 | 7 | .289 | .372 | .632 | 1.004 | |
|
Cincinnati Reds | 21 | 8 | 0 | 4 | .381 | .480 | .524 | 1.004 | |
|
Colorado Rockies | 74 | 20 | 6 | 17 | .270 | .365 | .635 | 1.000 | |
|
Detroit Tigers | 16 | 5 | 1 | 3 | .313 | .313 | .688 | 1.000 | |
|
Kansas City Royals | 25 | 6 | 4 | 8 | .240 | .240 | .760 | 1.000 | |
|
Houston Astros | 28 | 7 | 4 | 5 | .250 | .250 | .750 | 1.000 | |
|
Toronto Blue Jays | 11 | 5 | 0 | 0 | .455 | .455 | .545 | 1.000 | |
|
Milwaukee Brewers | 10 | 2 | 2 | 3 | .200 | .200 | .800 | 1.000 | |
 |
Boston Red Sox | 15 | 5 | 1 | 3 | .333 | .333 | .667 | 1.000 | |
|
Cincinnati Reds | 52 | 15 | 4 | 10 | .288 | .383 | .615 | .999 | |
|
Cincinnati Reds | 29 | 8 | 3 | 5 | .276 | .344 | .655 | .999 | |
|
Colorado Rockies | 41 | 11 | 3 | 6 | .268 | .412 | .585 | .997 | |
|
Baltimore Orioles | 26 | 8 | 2 | 5 | .308 | .379 | .615 | .995 | |
|
Arizona Diamondbacks | 24 | 6 | 2 | 7 | .250 | .367 | .625 | .992 | |
|
Boston Red Sox | 20 | 6 | 1 | 5 | .300 | .391 | .600 | .991 | |
|
Atlanta Braves | 30 | 8 | 4 | 5 | .267 | .290 | .700 | .990 | |
 |
Washington Nationals | 71 | 20 | 7 | 15 | .282 | .342 | .648 | .990 | |
|
Kansas City Royals | 27 | 10 | 1 | 4 | .370 | .433 | .556 | .989 | |
|
Tampa Bay Rays | 15 | 4 | 1 | 2 | .267 | .389 | .600 | .989 | |
|
Arizona Diamondbacks | 26 | 8 | 3 | 9 | .308 | .333 | .654 | .987 | |
|
Washington Nationals | 24 | 9 | 1 | 2 | .375 | .444 | .542 | .986 |
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.