pymotogp 0.1.0

Fast, pythonic access to MotoGP data — inspired by FastF1

PyMotoGP

Fast, pythonic access to MotoGP data — inspired by FastF1.

PyMotoGP gives you session-centric access to qualifying, sprint, and race data from the official PulseLive API and DORNA timing PDFs. Load a session, get a pandas DataFrame of laps, plot pace, run analytics, predict race outcomes.

import motogp

session = motogp.load(2024, 'qatar', 'Q2')
print(session.best_lap.rider_name)         # 'Jorge MARTIN'
df = session.laps.to_dataframe()           # full lap data, sectors, top speeds
session.plot.lap_times()                   # matplotlib figure
session.analysis.gap_to_pole()             # gap analysis

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Install

pip install pymotogp

Requires Python 3.10+. Core dependencies: requests, pandas, pdfplumber, matplotlib.

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Quickstart

Load a session

import motogp

# Year + event + session label. Event matches by name, country, or short code.
session = motogp.load(2024, 'cataluña', 'Q2')

Supported session labels: Q1, Q2, qualifying, FP, practice, sprint, warm-up, race.

Inspect laps

df = session.laps.to_dataframe()
# Columns: rider_name, lap_number, lap_time_ms, sector1_ms ... sector4_ms,
#          top_speed, is_valid, is_cancelled, is_pit, is_best

session.best_lap                  # fastest valid lap
session.riders                    # list of rider names
session.classification            # official position list

Plot

fig = session.plot.lap_times(riders=['Bagnaia', 'Martin'])
fig.savefig('out.png')

session.plot.pace_distribution()      # boxplot per rider
session.plot.sector_comparison()      # grouped bars
session.plot.compare('Bagnaia', 'Martin')  # 2-panel comparison

All plotting methods return a matplotlib.figure.Figure. Cancelled laps, pit laps, and likely outlaps (>1.15× rider median) are filtered by default.

Analyze

session.analysis.theoretical_best()       # sum of best sectors per rider
session.analysis.gain_potential('Bagnaia')   # ms left on the table
session.analysis.gap_to_pole()
session.analysis.sector_strength()
session.analysis.consistency_ranking()

Cross-season history

from motogp import HistoricalAnalyzer

hist = HistoricalAnalyzer()
hist.track_evolution('catalunya')         # pole-time progression by year
hist.rider_form('Bagnaia', year=2024)     # per-round qualifying form
hist.team_pace(year=2024)                 # aggregate by team

Predict race pace (transparent baseline)

from motogp import RacePaceEstimator

qual = motogp.load(2024, 'malaysia', 'Q2')
est = RacePaceEstimator()
est.predict(qual, n_laps=20)

The estimator uses a transparent linear model: race_lap(n) = q_best + race_offset + degradation × (n − 1). All assumptions are exposed as parameters. See "Honest limits" below for accuracy data.

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Data sources

PyMotoGP uses a two-tier resolution strategy:

1. Local cache (instant) — if you have a directory of pre-scraped JSON files, set MOTOGP_SCRAPER_OUTPUT to point at it. Hits return instantly.
2. PulseLive API (live) — falls back to the official MotoGP API: api.motogp.pulselive.com/motogp/v1/. Discovers the session, downloads the official Analysis PDF, parses it with pdfplumber. PDFs are cached at ~/.motogp_pdfs/.

No API key required. Be considerate — the library caches everything.

Environment variables

Variable	Default	Purpose
MOTOGP_SCRAPER_OUTPUT	~/.motogp/scraper_output	Pre-scraped JSON cache
MOTOGP_PDF_CACHE	~/.motogp_pdfs	DORNA Analysis PDF cache

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Honest limits

PyMotoGP includes a backtest pipeline so you can measure model accuracy instead of trusting it blindly.

from motogp.analysis import RacePaceValidator

v = RacePaceValidator()
df = v.validate_season(2024)
v.summarize(df)

2024 season backtest results (20 GP races validated):

Metric	Value
Winner hit rate	30%
Podium overlap (mean of 3)	1.2
Position MAE	3.5 places
Kendall's tau	0.24

Treat RacePaceEstimator.predict() as a directional baseline, not a black-box predictor. The model assumes qualifying pace transfers linearly to race pace with uniform degradation — which is wrong for ~70% of MotoGP races because tire management, race craft, weather, and DNFs aren't captured. Calibrate per-track with est.calibrate_from_race(qual, race) once you have real race data.

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Architecture

motogp/
├── core/           # Session, Lap, Sector, Rider data models
├── api/            # PulseLive client + DORNA PDF parser
├── plots/          # Matplotlib-based session plots
├── analysis/       # SessionAnalyzer, HistoricalAnalyzer,
│                   # RacePaceEstimator, RacePaceValidator

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Roadmap

• ✅ Phase 1 — PulseLive API wrapper, core data models, caching
• ✅ Phase 2 — Real data integration (scraper cache + PulseLive + PDFs)
• ✅ Phase 3 — Session/historical/race-pace analytics + validation
• 🚧 Phase 4 — Sphinx docs, VCR-cassette integration tests, PyPI release

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Contributing

Issues and PRs welcome. Run tests with:

pip install -e ".[dev]"
pytest

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License

MIT

Not affiliated with Dorna Sports or MotoGP™. All data is sourced from publicly available APIs and PDFs for personal and research use.