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 Cubs | 22 | 3 | 0 | 3 | .136 | .344 | .136 | .480 | |
 |
Boston Red Sox | 12 | 2 | 0 | 1 | .167 | .231 | .250 | .481 | |
 |
Minnesota Twins | 14 | 3 | 0 | 0 | .214 | .267 | .214 | .481 | |
 |
Chicago Cubs | 19 | 3 | 0 | 2 | .158 | .273 | .211 | .483 | |
  |
Philadelphia Phillies | 16 | 3 | 0 | 0 | .188 | .235 | .250 | .485 | |
|
Boston Red Sox | 61 | 11 | 1 | 9 | .180 | .239 | .246 | .485 | |
|
Colorado Rockies | 18 | 4 | 0 | 3 | .222 | .263 | .222 | .485 | |
|
St. Louis Cardinals | 12 | 1 | 1 | 2 | .083 | .154 | .333 | .487 | |
 |
New York Yankees | 12 | 1 | 1 | 1 | .083 | .154 | .333 | .487 | |
|
San Francisco Giants | 26 | 3 | 1 | 2 | .115 | .258 | .231 | .489 | |
|
Milwaukee Brewers | 26 | 3 | 1 | 2 | .115 | .258 | .231 | .489 | |
|
Los Angeles Angels | 20 | 3 | 0 | 1 | .150 | .190 | .300 | .490 | |
 |
Arizona Diamondbacks | 26 | 6 | 0 | 0 | .231 | .259 | .231 | .490 | |
|
San Francisco Giants | 24 | 5 | 0 | 0 | .208 | .240 | .250 | .490 | |
|
Pittsburgh Pirates | 22 | 4 | 0 | 1 | .182 | .308 | .182 | .490 | |
|
Arizona Diamondbacks | 11 | 2 | 0 | 0 | .182 | .308 | .182 | .490 | |
|
San Diego Padres | 11 | 2 | 0 | 0 | .182 | .308 | .182 | .490 | |
 |
Oakland Athletics | 22 | 3 | 1 | 3 | .136 | .174 | .318 | .492 | |
|
Miami Marlins | 13 | 1 | 1 | 3 | .077 | .188 | .308 | .495 | |
|
Oakland Athletics | 19 | 4 | 0 | 1 | .211 | .286 | .211 | .496 | |
|
Los Angeles Dodgers | 13 | 2 | 0 | 0 | .154 | .267 | .231 | .497 | |
 |
San Diego Padres | 14 | 3 | 0 | 1 | .214 | .214 | .286 | .500 | |
|
New York Mets | 10 | 2 | 0 | 2 | .200 | .200 | .300 | .500 | |
|
Texas Rangers | 18 | 3 | 1 | 3 | .167 | .167 | .333 | .500 | |
|
Toronto Blue Jays | 14 | 3 | 0 | 2 | .214 | .214 | .286 | .500 | |
|
Chicago Cubs | 12 | 2 | 0 | 0 | .167 | .333 | .167 | .500 | |
|
Washington Nationals | 13 | 1 | 1 | 2 | .077 | .200 | .308 | .508 | |
 |
New York Yankees | 35 | 5 | 2 | 6 | .143 | .167 | .343 | .510 | |
 |
Houston Astros | 20 | 4 | 0 | 2 | .200 | .261 | .250 | .511 | |
|
Toronto Blue Jays | 20 | 4 | 0 | 1 | .200 | .261 | .250 | .511 | |
|
San Diego Padres | 25 | 4 | 1 | 1 | .160 | .192 | .320 | .512 | |
 |
New York Mets | 28 | 5 | 1 | 2 | .179 | .226 | .286 | .512 | |
|
Baltimore Orioles | 35 | 7 | 1 | 3 | .200 | .200 | .314 | .514 | |
|
New York Yankees | 23 | 4 | 0 | 2 | .174 | .296 | .217 | .514 | |
 |
Chicago White Sox | 22 | 3 | 0 | 3 | .136 | .333 | .182 | .515 | |
|
Pittsburgh Pirates | 11 | 2 | 0 | 3 | .182 | .333 | .182 | .515 | |
|
Oakland Athletics | 61 | 9 | 0 | 7 | .148 | .254 | .262 | .516 | |
|
Seattle Mariners | 24 | 4 | 1 | 2 | .167 | .231 | .292 | .522 | |
|
Atlanta Braves | 13 | 2 | 0 | 1 | .154 | .214 | .308 | .522 | |
|
Minnesota Twins | 11 | 2 | 0 | 0 | .182 | .250 | .273 | .523 | |
|
Arizona Diamondbacks | 40 | 7 | 1 | 4 | .175 | .250 | .275 | .525 | |
|
Tampa Bay Rays | 14 | 3 | 0 | 0 | .214 | .313 | .214 | .527 | |
|
Boston Red Sox | 66 | 11 | 3 | 7 | .167 | .214 | .318 | .532 | |
|
San Diego Padres | 10 | 2 | 0 | 1 | .200 | .333 | .200 | .533 | |
|
Oakland Athletics | 15 | 3 | 0 | 1 | .200 | .200 | .333 | .533 | |
|
Arizona Diamondbacks | 10 | 2 | 0 | 0 | .200 | .333 | .200 | .533 | |
|
Minnesota Twins | 15 | 3 | 0 | 0 | .200 | .333 | .200 | .533 | |
|
New York Yankees | 12 | 2 | 0 | 3 | .167 | .286 | .250 | .536 | |
|
Cincinnati Reds | 13 | 2 | 1 | 1 | .154 | .154 | .385 | .538 | |
|
San Diego Padres | 13 | 2 | 1 | 3 | .154 | .154 | .385 | .538 |
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.
Related MLB Tools
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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.