MLB Batting Splits 2019
Performance splits by handedness, home/away, and situational categories.
| Player | Team | Split | AB | H | HR | RBI | AVG | OBP | SLG | OPS ▼ |
|---|---|---|---|---|---|---|---|---|---|---|
 |
Miami Marlins | 19 | 7 | 1 | 4 | .368 | .455 | .789 | 1.244 | |
 |
Seattle Mariners | 31 | 15 | 1 | 7 | .484 | .529 | .710 | 1.239 | |
 |
Chicago Cubs | 74 | 28 | 9 | 21 | .378 | .435 | .797 | 1.233 | |
 |
Miami Marlins | 25 | 9 | 3 | 5 | .360 | .429 | .800 | 1.229 | |
 |
Minnesota Twins | 20 | 8 | 2 | 6 | .400 | .429 | .800 | 1.229 | |
 |
Milwaukee Brewers | 10 | 2 | 2 | 3 | .200 | .429 | .800 | 1.229 | |
  |
St. Louis Cardinals | 24 | 7 | 4 | 9 | .292 | .393 | .833 | 1.226 | |
 |
Colorado Rockies | 21 | 7 | 3 | 5 | .333 | .417 | .810 | 1.226 | |
 |
Kansas City Royals | 18 | 5 | 3 | 8 | .278 | .391 | .833 | 1.225 | |
|
Baltimore Orioles | 17 | 5 | 3 | 4 | .294 | .400 | .824 | 1.224 | |
 |
Baltimore Orioles | 16 | 6 | 1 | 4 | .375 | .474 | .750 | 1.224 | |
|
Pittsburgh Pirates | 19 | 7 | 1 | 9 | .368 | .480 | .737 | 1.217 | |
|
San Diego Padres | 26 | 11 | 2 | 5 | .423 | .484 | .731 | 1.215 | |
  |
New York Mets | 25 | 8 | 3 | 8 | .320 | .370 | .840 | 1.210 | |
|
Cincinnati Reds | 13 | 4 | 2 | 2 | .308 | .438 | .769 | 1.207 | |
|
Arizona Diamondbacks | 12 | 6 | 0 | 2 | .500 | .538 | .667 | 1.205 | |
|
Washington Nationals | 11 | 3 | 2 | 2 | .273 | .385 | .818 | 1.203 | |
 |
Texas Rangers | 13 | 4 | 2 | 3 | .308 | .357 | .846 | 1.203 | |
|
St. Louis Cardinals | 13 | 4 | 1 | 3 | .308 | .357 | .846 | 1.203 | |
|
Kansas City Royals | 22 | 9 | 1 | 3 | .409 | .519 | .682 | 1.200 | |
|
Texas Rangers | 10 | 4 | 1 | 3 | .400 | .500 | .700 | 1.200 | |
|
Baltimore Orioles | 53 | 17 | 7 | 15 | .321 | .368 | .830 | 1.199 | |
 |
Pittsburgh Pirates | 58 | 21 | 6 | 14 | .362 | .456 | .741 | 1.197 | |
|
San Francisco Giants | 29 | 10 | 4 | 9 | .345 | .367 | .828 | 1.194 | |
|
Pittsburgh Pirates | 71 | 26 | 7 | 23 | .366 | .489 | .704 | 1.193 | |
|
Texas Rangers | 35 | 13 | 4 | 7 | .371 | .450 | .743 | 1.193 | |
|
Chicago Cubs | 24 | 6 | 4 | 5 | .250 | .400 | .792 | 1.192 | |
|
Colorado Rockies | 15 | 4 | 2 | 6 | .267 | .389 | .800 | 1.189 | |
|
Houston Astros | 19 | 7 | 2 | 2 | .368 | .400 | .789 | 1.189 | |
|
Los Angeles Angels | 11 | 5 | 1 | 2 | .455 | .462 | .727 | 1.189 | |
|
Los Angeles Dodgers | 11 | 4 | 1 | 2 | .364 | .462 | .727 | 1.189 | |
|
Chicago Cubs | 12 | 3 | 2 | 4 | .250 | .438 | .750 | 1.188 | |
|
Cincinnati Reds | 36 | 14 | 1 | 9 | .389 | .488 | .694 | 1.183 | |
|
Milwaukee Brewers | 22 | 8 | 2 | 6 | .364 | .500 | .682 | 1.182 | |
|
Minnesota Twins | 13 | 6 | 0 | 2 | .462 | .563 | .615 | 1.178 | |
|
Los Angeles Angels | 65 | 21 | 9 | 17 | .323 | .423 | .754 | 1.177 | |
|
Pittsburgh Pirates | 15 | 5 | 1 | 2 | .333 | .375 | .800 | 1.175 | |
|
Detroit Tigers | 54 | 21 | 5 | 13 | .389 | .433 | .741 | 1.174 | |
|
New York Mets | 74 | 29 | 4 | 15 | .392 | .471 | .703 | 1.173 | |
|
Colorado Rockies | 27 | 11 | 1 | 8 | .407 | .467 | .704 | 1.170 | |
|
Boston Red Sox | 11 | 5 | 0 | 1 | .455 | .625 | .545 | 1.170 | |
 |
Texas Rangers | 12 | 5 | 0 | 0 | .417 | .417 | .750 | 1.167 | |
|
Miami Marlins | 15 | 6 | 1 | 3 | .400 | .500 | .667 | 1.167 | |
|
Washington Nationals | 12 | 6 | 0 | 0 | .500 | .500 | .667 | 1.167 | |
 |
New York Yankees | 18 | 7 | 2 | 4 | .389 | .389 | .778 | 1.167 | |
|
Toronto Blue Jays | 52 | 21 | 4 | 7 | .404 | .492 | .673 | 1.165 | |
 |
Baltimore Orioles | 28 | 12 | 2 | 11 | .429 | .485 | .679 | 1.163 | |
|
San Diego Padres | 18 | 8 | 0 | 0 | .444 | .545 | .611 | 1.157 | |
|
Boston Red Sox | 40 | 16 | 3 | 7 | .400 | .455 | .700 | 1.155 | |
|
Colorado Rockies | 44 | 17 | 1 | 8 | .386 | .426 | .727 | 1.153 |
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.