Batter vs Pitcher Matchup
Head-to-head comparison of any MLB batter and pitcher since 1950. Search players below and watch their matchup stats update...
Ownage chart
Matchup Ranks
- SLG1.200 .700
- Hard Hit17% 30%
- Quality AB67% 75%
- SB Success0% 15%
Season stats
| Yr | PA | AB | H | 2B | 3B | HR | RBI | BB | SO | BA | OBP | SLG | OPS |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2022 | 2 | 2 | 2 | 0 | 0 | 1 | 2 | 0 | 0 | 1.000 | 1.000 | 2.500 | 3.500 |
| 2023 | 3 | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | .000 | .333 | .000 | .333 |
| 2024 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1.000 | 1.000 | 1.000 | 2.000 |
| TOTALS | 6 | 5 | 3 | 0 | 0 | 1 | 2 | 1 | 0 | .600 | .667 | 1.200 | 1.867 |
Predicted outcomes
Based on historical data and our prediction model, the probability of various outcomes for a random at-bat (hot streaks aside).
Gets on base (67% OBP)
Makes an out (33%)
- Strikeout: 0%
- Out (in play): 33%
Latest ABs
| Date | Inn | Score | Count | Result | Details | Hard hit? |
|---|---|---|---|---|---|---|
| 8/16/22 - NYN @ ATL | 7 | up 4-0 | (0 - 0) | 1B | line drive single to left | • |
| 10/2/22 - NYN @ ATL | 6 | up 4-3 | (1 - 1) | HR | fly ball home run to deep right | • |
| 4/9/23 - SDN @ ATL | 1 | 0-0 | (2 - 2) | Out | ground ball to first | • |
| 4/9/23 - SDN @ ATL | 3 | down 0-4 | (3 - 0) | BB | - | |
| 4/9/23 - SDN @ ATL | 5 | down 1-8 | (2 - 2) | Out | ground ball to catcher | • |
| 9/28/24 - KCA @ ATL | 1 | 0-0 | (1 - 1) | 1B | bunt ground ball single to center | • |
About Ownage charts
Want a quick look at the seasonal trends of a batter/pitcher matchup? Called Ownage Charts, they're a handy way to see which way the trend is headed. On each chart, the closer the OPS line gets to a player's name, the more ownage and bragging rights. For context, there's a horizontal line that denotes the league-wide OPS of about .725. A minimum of 5 plate appearances in a given season is required to qualify for the chart.
More about our philosophy
Baseball's an individual sport with team goals and nothing's a better example of that than an at-bat. A batter could miss a curve by 2 feet then crush a ball 400 feet the very next time up. Most baseball fans (minus degenerate gamblers) love that unpredictability. On the flip side, models can help predict what may happen and we've developed a variety of algorithms that can be applied to head-to-head matchups. Most rely on a fair amount of data but generally do well when there are at least 15 plate appearances.
Most of the tables above break down the actual head-to-head data in a way that's hopefully more digestable and actionable (i.e., helping you decide to sit or start a SP/batter). The "Predicted" table takes into account a whole bunch of data and tosses it into our at-bat predictive model: previous results, ballpark, weather and more. And, of course, the more historical data the tighter the accuracy. We have plans to release even more granular data, but for now, enjoy and let us know if you have any questions or suggestions.