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YMori

Posted on • Originally published at yasumorishima.github.io on

MLB Bat Tracking Dataset (2024-2025)

#baseball #mlb #kaggle #dataset

Introduction

I've published a Kaggle dataset containing MLB Bat Tracking metrics for the 2024 and 2025 seasons. This dataset provides swing speed, attack angle, and other bat tracking statistics that were introduced by MLB in 2024.

Dataset Link: https://www.kaggle.com/datasets/yasunorim/mlb-bat-tracking-2024-2025

Dataset Overview

MLB Bat Tracking (2024-2025)

  • Batters: 452 batters (226 per season)
  • Columns: 19 columns
  • Seasons: 2024, 2025
  • Source: Baseball Savant (directly accessed, not via pybaseball)

What is Bat Tracking?

Bat Tracking is a new technology introduced by MLB in 2024 that measures swing metrics using high-speed cameras. It provides insights into swing mechanics that were previously unavailable.

Key Metrics

Metric Description
bat_speed Swing speed at contact (mph)
swing_length Length of swing path (feet)
squared_up_rate Rate of optimal bat-ball contact (0-1)
attack_angle Swing plane angle (degrees)
blast_avg Percentage of swings with both high speed and optimal contact

Data Collection

This dataset was collected directly from Baseball Savant using custom scraping, as pybaseball does not yet support bat tracking data.

Data Source: Baseball Savant Bat Tracking Leaderboard

import pandas as pd
import requests
from bs4 import BeautifulSoup

# Example: Fetching 2024 bat tracking data
url = "https://baseballsavant.mlb.com/leaderboard/bat-tracking?year=2024"
response = requests.get(url)
# Parse HTML and extract table data
# (Full implementation available in the dataset repository)
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Dataset Schema

Main Columns

Column Type Description
year int Season year (2024 or 2025)
player_name str Player name
player_id int MLBAM player ID
bat_speed float Average bat speed (mph)
swing_length float Average swing length (feet)
squared_up_rate float Rate of squared-up contact (0-100%)
attack_angle float Average attack angle (degrees)
blast_avg float BLAST score (0-100)
fast_swing_rate float Percentage of fast swings (0-100%)
swords_rate float Percentage of swings-and-misses on pitches in zone (0-100%)

Usage in Kaggle Notebooks

Loading the Dataset 

import pandas as pd

# Load bat tracking data
df = pd.read_csv('/kaggle/input/mlb-bat-tracking-2024-2025/bat_tracking_2024_2025.csv')

print(f"Total records: {len(df)}")
print(f"Seasons: {df['year'].unique()}")
print(f"Average bat speed: {df['bat_speed'].mean():.2f} mph")
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Year-over-Year Comparison 

import matplotlib.pyplot as plt

# Compare bat speed distribution between seasons
df_2024 = df[df['year'] == 2024]
df_2025 = df[df['year'] == 2025]

plt.hist(df_2024['bat_speed'], alpha=0.5, label='2024', bins=20)
plt.hist(df_2025['bat_speed'], alpha=0.5, label='2025', bins=20)
plt.xlabel('Bat Speed (mph)')
plt.ylabel('Frequency')
plt.legend()
plt.title('Bat Speed Distribution: 2024 vs 2025')
plt.show()
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Analysis Examples

Top Performers by Bat Speed 

# Top 10 fastest bat speeds in 2025
top_bat_speed = df[df['year'] == 2025].nlargest(10, 'bat_speed')[
    ['player_name', 'bat_speed', 'squared_up_rate', 'attack_angle']
]
print(top_bat_speed)
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Squared-Up Rate Analysis 

import seaborn as sns

# Relationship between bat speed and squared-up rate
sns.scatterplot(data=df, x='bat_speed', y='squared_up_rate', hue='year', alpha=0.6)
plt.xlabel('Bat Speed (mph)')
plt.ylabel('Squared-Up Rate (%)')
plt.title('Bat Speed vs Squared-Up Rate')
plt.show()
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Insights from Bat Tracking Data

What Makes a Good Hitter?

Bat tracking data reveals that elite hitters typically have:

  • High bat speed (75+ mph)
  • High squared-up rate (30%+)
  • Optimal attack angle (10-20 degrees for most hitters)
  • Short swing length (Efficient swing mechanics)

Comparing Swing Profiles

Different player types show distinct swing profiles:

  • Power hitters: High bat speed, steep attack angle
  • Contact hitters: Lower swing length, high squared-up rate
  • Speed-focused players: Fast swing rate, shallow attack angle

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