Tennis Point-by-Point Dataset — ATP, WTA, Challenger, ITF

Free for non-commercial academic research and teaching. Public sample: DOI 10.5281/zenodo.22048731 · Dataset page: livetennisapi.com/data/academic · Full-dataset applications: research@livetennisapi.com

This repository holds the documentation and loaders for the dataset. The data itself lives on Zenodo (public sample) and is granted in full on application — nothing here to clone but knowledge.

What the dataset is (measured 2026-08-22)

• Match index — 173,571 completed professional matches, 2023-01-01 → 2026-08-13 (7-day rolling embargo), across ATP, WTA, Challenger and ITF, singles and doubles.
• Point-by-point score sequences for 170,527 of them (98.2%), in two stated provenance classes:
  • observed — captured live by Live Tennis API's own collection with real UTC timestamps: 22,031 matches / 3,184,514 point states, from 2025-10-12 (full volume from February 2026), every tier down to ITF — where public point-level data has effectively never existed.
  • reconstructed — expanded post-match from a third-party feed, no capture timestamps: covers 2023–2025 and part of 2026. Not in public files; per-request under the programme.
• Players — 32,678 with stable IDs, country, birth date, handedness, and a sackmann_id crosswalk (see below).

Known limitations are stated plainly in the data dictionary and on the dataset page: the observed layer starts late 2025; tapes may start mid-match or end before the final point (each match declares its own coverage); no serve speed/direction or shot-level data; nothing before 2023.

Loading the sample

Python:

import pandas as pd

matches = pd.read_csv("matches.csv.gz")
players = pd.read_csv("players.csv.gz")
points  = pd.read_csv("points_sample_2026-06.csv.gz",
                      parse_dates=["timestamp_utc"])

# games_p1/games_p2 are JSON arrays (games won per set; last element = current set)
import json
points["games_p1"] = points["games_p1"].map(json.loads)
points["games_p2"] = points["games_p2"].map(json.loads)

# One row = one score STATE; a point's outcome is the transition between
# consecutive rows of the same match_id (rows are in capture order).

R:

library(readr)
matches <- read_csv("matches.csv.gz")
points  <- read_csv("points_sample_2026-06.csv.gz")

Joining with Jeff Sackmann's open datasets

For pre-2023 results use Jeff Sackmann's datasets (tennis_atp, tennis_wta, the Match Charting Project) — the standard research corpora. Our players.csv carries a sackmann_id column mapping our stable player IDs to his, so the two ecosystems join cleanly:

joined = players.dropna(subset=["sackmann_id"])
# then join sackmann's atp_players.csv on player_id == sackmann_id

This dataset is a complement for the 2023-onward, lower-tier, and capture-timestamped questions the open archives don't cover — not a replacement for them.

Access & conditions

Email research@livetennisapi.com from an institutional address (students: ORCID or a named supervisor) with one paragraph on your research and the data you need. Target turnaround: 3 business days. Conditions: non-commercial research and teaching only · no redistribution of raw data (published aggregates and findings are fine) · citation required · tell us when you publish.

Citing

Live Tennis API (2026). Tennis Point-by-Point Dataset — ATP, WTA, Challenger, ITF. Zenodo. https://doi.org/10.5281/zenodo.22048731

@dataset{livetennisapi_2026_tennis_pbp,
  author    = {{Live Tennis API}},
  title     = {Tennis Point-by-Point Dataset --- ATP, WTA, Challenger, ITF},
  year      = 2026,
  publisher = {Zenodo},
  doi       = {10.5281/zenodo.22048731},
  url       = {https://doi.org/10.5281/zenodo.22048731}
}

License

Documentation and code snippets in this repository: MIT. The dataset itself: CC BY-NC 4.0 via Zenodo and the application process — this repository grants no data rights.