Japanese MLB Players Statcast Dataset (2015-2025)

Introduction

I’ve published a Kaggle dataset containing Statcast data for Japanese MLB players from 2015 to 2025. This dataset provides pitch-by-pitch and batted ball data for analyzing performance trends of Japanese players in Major League Baseball.

Dataset Link: https://www.kaggle.com/datasets/yasunorim/japan-mlb-pitchers-batters-statcast

Dataset Overview

Japan MLB Pitchers Batters Statcast (2015-2025)

• Pitching Data: 25 pitchers, 118,226 pitches (65MB)
• Batting Data: 10 batters, 56,362 batted balls (31MB)
• Player Metadata: 34 players
• Period: 2015-2025 (since Statcast introduction)
• Quality Score: 10.0/10 on Kaggle

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Data Collection Process

1. Getting Player IDs with pybaseball

First, retrieve the MLBAM ID for each player:

from pybaseball import playerid_lookup

# Search by player name
player = playerid_lookup("ohtani", "shohei")
print(player)
# key_mlbam column contains MLBAM ID (660271)

2. Fetching Statcast Data

Use the MLBAM ID to retrieve pitch-by-pitch data:

from pybaseball import statcast_pitcher, statcast_batter

# Pitcher data
df_pitching = statcast_pitcher(
    start_dt="2015-03-01",
    end_dt="2025-02-08",
    player_id=660271  # Shohei Ohtani
)

# Batter data
df_batting = statcast_batter(
    start_dt="2015-03-01",
    end_dt="2025-02-08",
    player_id=660271  # Shohei Ohtani
)

3. Combining Multiple Players

Concatenate data from all players:

import pandas as pd

pitcher_ids = [660271, 506433, 808967, ...]  # All pitcher IDs
batting_ids = [660271, 673548, 807799, ...]  # All batter IDs

# Pitching data
pitching_dfs = []
for pid in pitcher_ids:
    df = statcast_pitcher("2015-03-01", "2025-02-08", pid)
    pitching_dfs.append(df)
df_all_pitching = pd.concat(pitching_dfs, ignore_index=True)

# Batting data
batting_dfs = []
for bid in batting_ids:
    df = statcast_batter("2015-03-01", "2025-02-08", bid)
    batting_dfs.append(df)
df_all_batting = pd.concat(batting_dfs, ignore_index=True)

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Dataset Contents

japanese_mlb_pitching.csv (Pitching Data)

Pitch-by-pitch data with key columns:

Column	Description
pitcher	Pitcher’s MLBAM ID
game_date	Game date
pitch_type	Pitch type (FF, SL, CU, CH, SI, etc.)
release_speed	Release velocity (mph)
release_pos_x, release_pos_y, release_pos_z	Release point coordinates
pfx_x, pfx_z	Movement (feet, gravity-adjusted)
release_spin_rate	Spin rate (rpm)
events	Batted ball outcome (single, strikeout, etc.)
game_type	R=Regular season, S=Spring, F/D/L/W=Postseason

japanese_mlb_batting.csv (Batting Data)

Batted ball data with key columns:

Column	Description
batter	Batter’s MLBAM ID
launch_speed	Exit velocity (mph)
launch_angle	Launch angle (degrees)
hit_distance_sc	Hit distance (feet)
estimated_ba_using_speedangle	Expected batting average (xBA)
estimated_woba_using_speedangle	Expected weighted on-base average (xwOBA)
events	Batted ball outcome
game_type	Game type

players.csv (Player Metadata)

Column	Description
mlbam_id	MLBAM ID
player_name	Player name
position	pitcher / batter

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Usage in Kaggle Notebooks

1. Add Dataset to kernel-metadata.json

{
  "id": "your-username/your-notebook-slug",
  "title": "Your Notebook Title",
  "code_file": "your-notebook.ipynb",
  "language": "python",
  "kernel_type": "notebook",
  "is_private": "false",
  "enable_gpu": "false",
  "enable_tpu": "false",
  "enable_internet": "false",
  "dataset_sources": ["yasunorim/japan-mlb-pitchers-batters-statcast"],
  "competition_sources": [],
  "kernel_sources": [],
  "model_sources": []
}

2. Load Data in Notebook

import pandas as pd

# Pitching data
df_pitching = pd.read_csv('/kaggle/input/japan-mlb-pitchers-batters-statcast/japanese_mlb_pitching.csv')

# Batting data
df_batting = pd.read_csv('/kaggle/input/japan-mlb-pitchers-batters-statcast/japanese_mlb_batting.csv')

# Player metadata
df_players = pd.read_csv('/kaggle/input/japan-mlb-pitchers-batters-statcast/players.csv')

print(f'Pitching data: {len(df_pitching)} records')
print(f'Batting data: {len(df_batting)} records')

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Analysis Examples

SQL Queries with DuckDB

import pandas as pd
import duckdb

df = pd.read_csv("japanese_mlb_pitching.csv")
con = duckdb.connect()

# Yu Darvish (MLBAM ID: 506433) regular season data
df_darvish = con.execute("""
    SELECT game_year, pitch_type, COUNT(*) as count,
           AVG(release_speed) as avg_speed,
           AVG(release_spin_rate) as avg_spin
    FROM df
    WHERE pitcher = 506433 AND game_type = 'R'
    GROUP BY game_year, pitch_type
    ORDER BY game_year, count DESC
""").df()

Batted Ball Visualization

import matplotlib.pyplot as plt

df_batting = pd.read_csv("japanese_mlb_batting.csv")
df_ohtani = df_batting[df_batting["batter"] == 660271]

plt.scatter(df_ohtani["launch_angle"], df_ohtani["launch_speed"], alpha=0.5)
plt.xlabel("Launch Angle (degrees)")
plt.ylabel("Launch Speed (mph)")
plt.title("Batted Ball Profile - Shohei Ohtani")
plt.show()

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Analysis Notebooks Using This Dataset

1. Yu Darvish Pitching Evolution (2021-2025)
2. Shota Imanaga: Rookie to Sophomore Changes (2024-2025)
3. Kodai Senga’s “Ghost Fork” Analysis (2023-2025)
4. Yusei Kikuchi’s Slider Evolution (2019-2025)

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Links

• Dataset: https://www.kaggle.com/datasets/yasunorim/japan-mlb-pitchers-batters-statcast
• GitHub Repository: https://github.com/yasumorishima/kaggle-datasets
• Analysis Notebooks (GitHub): https://github.com/yasumorishima/mlb-statcast-visualization