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 | 17 | 8 | 2 | 3 | .471 | .526 | .941 | 1.467 | |
 |
Chicago Cubs | 11 | 5 | 0 | 2 | .455 | .571 | .909 | 1.481 | |
 |
Los Angeles Angels | 10 | 5 | 1 | 1 | .500 | .583 | .900 | 1.483 | |
 |
Kansas City Royals | 12 | 6 | 1 | 4 | .500 | .571 | .917 | 1.488 | |
 |
Tampa Bay Rays | 11 | 4 | 2 | 4 | .364 | .500 | 1.000 | 1.500 | |
|
Chicago White Sox | 13 | 6 | 2 | 4 | .462 | .588 | .923 | 1.511 | |
  |
Baltimore Orioles | 66 | 26 | 13 | 20 | .394 | .467 | 1.045 | 1.512 | |
 |
Toronto Blue Jays | 10 | 4 | 2 | 6 | .400 | .417 | 1.100 | 1.517 | |
|
San Francisco Giants | 13 | 7 | 2 | 4 | .538 | .538 | 1.000 | 1.538 | |
|
Toronto Blue Jays | 28 | 13 | 4 | 14 | .464 | .545 | 1.000 | 1.545 | |
 |
Miami Marlins | 28 | 13 | 5 | 8 | .464 | .516 | 1.036 | 1.552 | |
|
Arizona Diamondbacks | 24 | 10 | 5 | 9 | .417 | .440 | 1.125 | 1.565 | |
 |
Philadelphia Phillies | 10 | 6 | 1 | 1 | .600 | .667 | .900 | 1.567 | |
|
Cincinnati Reds | 15 | 7 | 2 | 4 | .467 | .579 | 1.000 | 1.579 | |
|
San Diego Padres | 12 | 6 | 2 | 4 | .500 | .500 | 1.083 | 1.583 | |
|
Baltimore Orioles | 17 | 7 | 4 | 10 | .412 | .474 | 1.118 | 1.591 | |
 |
Texas Rangers | 12 | 7 | 1 | 1 | .583 | .615 | 1.000 | 1.615 | |
|
Los Angeles Dodgers | 12 | 5 | 3 | 6 | .417 | .462 | 1.167 | 1.628 | |
|
Chicago Cubs | 21 | 9 | 4 | 7 | .429 | .536 | 1.095 | 1.631 | |
|
Minnesota Twins | 14 | 8 | 1 | 4 | .571 | .647 | 1.071 | 1.718 | |
|
Colorado Rockies | 15 | 7 | 3 | 4 | .467 | .600 | 1.267 | 1.867 | |
|
Philadelphia Phillies | 12 | 7 | 2 | 6 | .583 | .643 | 1.333 | 1.976 | |
|
Minnesota Twins | 10 | 6 | 2 | 7 | .600 | .692 | 1.300 | 1.992 | |
 |
New York Mets | 14 | 9 | 4 | 4 | .643 | .667 | 1.500 | 2.167 |
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.