I built savant-extras: Date Range Support for Baseball Savant Data

pybaseball only supports full-season leaderboards. savant-extras adds date range support for monthly splits, first/second half comparisons, and more.
python
baseball
opensource
pypi
Author

Yasunori Morishima

Published

February 11, 2026

The Problem

pybaseball is the go-to Python library for accessing Baseball Savant data. It provides dozens of functions for Statcast pitch-level data, leaderboards, fielding metrics, and more.

However, all of pybaseball’s leaderboard functions share a common limitation:

They only support full-season data.

# pybaseball: full season only
from pybaseball import statcast_batter_bat_tracking

df = statcast_batter_bat_tracking(year=2024)  # entire 2024 season
# Can't filter to just April, or first half, or any custom date range

Want to compare a player’s bat speed in April vs. September? Analyze first-half vs. second-half performance? Look at pre-trade vs. post-trade numbers? pybaseball can’t do it.

The Solution: savant-extras

I built savant-extras — a Python library that adds date range support for Baseball Savant’s bat tracking leaderboard.

It’s designed as a complement to pybaseball, not a replacement. Use pybaseball for pitch-level Statcast data, and savant-extras when you need leaderboard data for custom date ranges.

Bat tracking data (Hawk-Eye) is available from the 2024 season onward.

Installation

pip install savant-extras

Usage

1. bat_tracking() — Custom Date Range

from savant_extras import bat_tracking

# April 2024 batter data
df = bat_tracking("2024-04-01", "2024-04-30")
print(df[["name", "avg_bat_speed", "attack_angle", "competitive_swings"]].head(5))

Output:

                 name  avg_bat_speed  attack_angle  competitive_swings
0  Stanton, Giancarlo      80.776081      7.886978                 135
1         Cruz, Oneil      77.538084      9.574513                 127
2    Robert Jr., Luis      77.198599     11.955779                  12
3     Schwarber, Kyle      77.093025     14.716905                 167
4       Chapman, Matt      77.088279      5.304906                 153

You can also get the pitcher perspective:

# Pitcher bat tracking, April-June 2024
df = bat_tracking("2024-04-01", "2024-06-30", player_type="pitcher")
print(df[["name", "avg_bat_speed", "competitive_swings"]].head(5))

Output:

              name  avg_bat_speed  competitive_swings
0      Buttó, José      72.870642                 254
1        Sears, JP      72.681473                 577
2     Snell, Blake      72.646426                 182
3  Walker, Taijuan      72.646226                 298
4    Pérez, Martín      72.557050                 402

2. bat_tracking_monthly() — Season-Long Monthly Splits

Fetches bat tracking data for each month of the season (April–October) and adds a month column.

from savant_extras import bat_tracking_monthly

df = bat_tracking_monthly(2024, min_swings=1)

# Stanton's monthly bat speed trend
stanton = df[df["name"] == "Stanton, Giancarlo"]
print(stanton[["month", "avg_bat_speed", "competitive_swings"]])

Output:

   month  avg_bat_speed  competitive_swings
       4      80.776081                 135
       5      80.548995                 148
       6      81.011443                 137
       7      82.736527                  18
       8      81.483215                 133
       9      82.096844                 143

Stanton’s bat speed increased from 80.5 in May to 82.1 in September — a noticeable in-season improvement.

You can also compute league-wide averages:

print(df.groupby("month")["avg_bat_speed"].mean())

3. bat_tracking_splits() — First Half / Second Half

Splits the season at the All-Star break (approximated as July 13/14).

from savant_extras import bat_tracking_splits

splits = bat_tracking_splits(2024)
first = splits["first_half"]
second = splits["second_half"]

# Stanton's splits
s1 = first[first["name"] == "Stanton, Giancarlo"]
s2 = second[second["name"] == "Stanton, Giancarlo"]

print(f"First half:  {s1['avg_bat_speed'].values[0]:.1f} mph ({s1['competitive_swings'].values[0]:.0f} swings)")
print(f"Second half: {s2['avg_bat_speed'].values[0]:.1f} mph ({s2['competitive_swings'].values[0]:.0f} swings)")

Output:

First half:  80.8 mph (420 swings)
Second half: 81.9 mph (294 swings)

Stanton’s bat speed was 1.1 mph faster in the second half.

Use Cases

Slump / Hot Streak Analysis

Compare bat tracking metrics during a cold stretch vs. a hot month:

hot = bat_tracking("2024-05-01", "2024-05-31")
cold = bat_tracking("2024-08-01", "2024-08-31")

Pre-Trade vs. Post-Trade

Evaluate a player’s performance before and after the trade deadline (July 30):

before = bat_tracking("2024-04-01", "2024-07-29")
after = bat_tracking("2024-07-30", "2024-09-30")

Early Season vs. Late Season

See how the league adjusts over the course of a season:

early = bat_tracking("2024-04-01", "2024-04-30")
late = bat_tracking("2024-09-01", "2024-09-30")

pybaseball vs. savant-extras

pybaseball savant-extras
Full season data ✅ ✅
Monthly splits ❌ ✅
First/second half ❌ ✅
Custom date range ❌ ✅
Pre/post trade ❌ ✅

Technical Notes

  • Uses the same public CSV endpoint as pybaseball (Baseball Savant leaderboard)
  • Issues a warning when querying dates before 2024 (Hawk-Eye data not available)
  • bat_tracking_monthly() includes a 1-second sleep between requests to avoid overwhelming the server
  • Returns an empty DataFrame when the response is HTML or empty