Mound

A CLI and Python toolkit for retrieving, analyzing and visualizing MLB pitch-level data — without needing to know MLB player IDs or the underlying API structures.

> How many splitters did Roki Sasaki throw against the Diamondbacks last night?
> How often has he thrown it relative to his other pitches over his last four starts?
> What does its location look like over that period?

Mound answers questions like these with a few CLI commands or a few lines of Python.

Install

pip install mound

# Parquet export support:
pip install "mound[parquet]"

Or from a local checkout (editable):

git clone https://github.com/stiles/mound.git
cd mound
pip install -e .

Requires Python 3.10+.

Quickstart

CLI

# Find a player and their MLB ID
mound search "Roki Sasaki"

# Retrieve pitches from his last 4 starts
mound pitches "Roki Sasaki" --last 4

# Isolate one pitch type
mound pitches "Roki Sasaki" --last 4 --pitch splitter

# Pitch mix and results by pitch type
mound mix "Roki Sasaki" --last 4
mound results "Roki Sasaki" --last 4 --pitch splitter

# Plot pitch locations against the strike zone
mound zone "Roki Sasaki" --pitch splitter --last 4 --out splitter_zone.png

# Export the underlying data
mound pitches "Roki Sasaki" --last 4 --export roki_last4.csv

Run mound --help or mound <command> --help for the full option list.

Python

from mound import Pitcher

roki = Pitcher("Roki Sasaki")

pitches = roki.pitches(last=4)
splitters = pitches.filter(pitch_type="splitter")

splitters.pitch_mix()
splitters.strike_rate()
splitters.plot_zone(out="splitter_zone.png")

pitches.to_csv("roki_last4.csv")

Pitcher.pitches() and PitchCollection.filter() both accept:

Filtering a PitchCollection always returns another PitchCollection, so any combination of .filter(), .pitch_mix(), .strike_rate(), .plot_zone() and export methods composes freely.

Plots

plot_zone() renders a headline, a dek (pitch count, strike rate, date range) and a source line around the strike-zone chart itself, rather than relying on axis titles or a boxed legend:

All three are auto-generated but overridable:

splitters.plot_zone(
    title="Sasaki leans on the splitter",
    subtitle="134 pitches since the All-Star break",
    source="Source: Baseball Savant",
    kind="heatmap",  # or "scatter" (default)
    out="splitter_zone.png",
)

Pass subtitle="" or source="" to omit either. Passing your own ax (e.g. for a multi-panel figure) skips the dek/source and falls back to a plain left-aligned title, so plot_zone() behaves as a well-mannered subplot.

Pitch location isn't mirrored for batter handedness, so mixing lefties and righties in one panel can blur the picture — pass split_by="stand" to break it into a vs-LHB / vs-RHB pair, each with its own strike zone and pitch count:

splitters.plot_zone(split_by="stand", out="splitter_zone_by_stand.png")

mound zone "Roki Sasaki" --last 4 --pitch splitter --split-by stand --out splitter_zone_by_stand.png

Pitch types

Statcast tags every pitch with a short code. Mound normalizes these into human-readable names and accepts common aliases when filtering, so pitch_type="four-seam", "fastball" and "FF" are all equivalent.

Note on Roki Sasaki's signature pitch: Statcast classifies it inconsistently start-to-start — sometimes as a splitter (FS), sometimes as a forkball (FO), depending on its movement profile in a given game. If a pitch_type="splitter" query looks incomplete, check pitch_type="forkball" too, or filter using both.

Data sources

Mound calls two unofficial, public MLB data services directly:

• MLB Stats API — player search/lookup and game logs, used to resolve a pitcher's identity and discover which games to pull.
• Baseball Savant — the /gf game-feed endpoint, used for pitch-by-pitch Statcast data (location, velocity, pitch type, count, outcome).

Both are unofficial and undocumented; endpoints or response shapes could change without notice. Mound sends a descriptive User-Agent and retries transient failures, but does not currently cache responses, so repeated queries re-fetch data from these services.

Development

pip install -e ".[dev]"
pytest
ruff check .

Tests run entirely against mocked HTTP fixtures in tests/fixtures/ (via the responses library) and don't require network access.

Known limitations

• No caching yet — every call re-fetches from the MLB Stats API / Baseball Savant.
• Pitch classification comes from Statcast's own model and can be inconsistent for pitches with unusual movement (see the Roki Sasaki note above).
• Only pitchers are supported as the primary retrieval unit; there's no batter-vs-pitcher matchup view yet (see ROADMAP.md).
• Historical data availability depends on Statcast/Savant coverage, which is generally reliable from 2015 onward.
• All requests are synchronous and unthrottled beyond basic retry/backoff; heavy bulk retrieval (e.g. a full season) will be slow.

Roadmap

See ROADMAP.md for planned enhancements beyond this prototype.

Changelog

See CHANGELOG.md.