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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...

Jonah Heim
Yusei Kikuchi
AB7 HR0 OPS.858
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Ownage chart

Jonah Heim
    not enough matchup data to show
Yusei Kikuchi
Note: given a league-wide OPS of ~.725, the closer the line gets to a player, the more ownage. Min of 5 PA in a season to qualify.

Matchup Ranks

  • SLG.429 .700
  • Hard Hit29% 30%
  • Quality AB57% 75%
  • SB Success0% 15%

Season stats

Heim vs Kikuchi: 3 Seasons
YrPAABH2B3BHRRBIBBSOBAOBPSLGOPS
2022110000001.000.000.000.000
2023221000100.500.500.5001.000
2024442000201.500.500.5001.000
TOTALS773000302.429.429.429.858
Note: OPS color trend requires a min. of 5 PA in a season.

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 (35% OBP)

1B: 22% 2B: 4% 3B: 0% HR: 3% BB: 5% HBP: 0%
1b 2b 3b hr bb hbp

Makes an out (66%)

  • Strikeout: 19%
  • Out (in play): 47%

Latest ABs

Heim vs Kikuchi: Last 25 ABs
Date Inn Score Count Result Details Hard hit?
9/11/22 - TOR @ TEX3up 3-0(1 - 2)SOstrike out-
9/13/23 - TEX @ TOR2 0-0(1 - 1)Outground ball to second
9/13/23 - TEX @ TOR4 0-0(0 - 2)1Bground ball single to left
8/7/24 - HOU @ TEX3down 0-2(2 - 1)1Bground ball single to center
8/7/24 - HOU @ TEX5down 0-2(1 - 2)Outline drive to deep center
7/26/24 - TEX @ TOR1up 1-0(0 - 2)SOstrike out-
7/26/24 - TEX @ TOR3down 1-3(0 - 0)1Bline drive single to left

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.

Classic matchups

These are some of our favorite matchups based on length of history, involvement of elite players/HOFs, interesting stat trends and more.