Rugbypy
Repository for rugby data analytics
Install / Use
/learn @seanyboi/RugbypyREADME
rugbypy
<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->8000+ players, 250+ teams, 6000+ games over the 2022, 2023, 2024 &
2025 seasons - rugbypy is a Python package that aims to make rugby
data more available to aid in the development of rugby analytics.
Documentation can be found here
If you would like to help keep the lights on and support this repo feel free to buy me a coffee!
Requirements
python version 3.11
Install
pip install rugbypy
How to use
Match Stats
You can fetch all matches that have ever occurred with:
from rugbypy.match import *
matches = fetch_all_matches()
matches
Fetching all matches...
| | match_id | home_team | away_team | date | |------|----------|---------------------------------|-----------------------|----------| | 1498 | cae3129b | Exeter Chiefs | Bristol Rugby | 20220101 | | 1499 | 494f4092 | Scarlets | Ospreys | 20220101 | | 1500 | 77e4e537 | Connacht | Munster | 20220101 | | 1501 | fbca6594 | Stade Francais Paris | Perpignan | 20220101 | | 1502 | 7863a641 | Clermont Auvergne | Stade Toulousain | 20220101 | | ... | ... | ... | ... | ... | | 1121 | 2ac5db4a | Shizuoka Bluerevs | Urayasu D Rocks | 20251228 | | 1122 | a68f4527 | Toshiba Brave Lupus Tokyo | Yokohama Canon Eagles | 20251228 | | 1123 | 00adb234 | Mitsubishi Sagamihara Dynaboars | Saitama Wild Knights | 20251228 | | 1117 | e37207b8 | Leicester | Exeter Chiefs | 20251228 | | 4743 | 667ee422 | Beziers | Uso Nevers | 20260403 |
<p>6171 rows × 4 columns</p> </div>You can fetch all the matches that occured on a particular date with:
from rugbypy.match import *
matches = fetch_matches_by_date(date="20251205")
matches
Fetching matches on date: 20251205...
| | match_id | competition_id | home_team_id | home_team | away_team_id | away_team | |----|----|----|----|----|----|----| | 0 | 35e0b16d | ee0c6883 | d7d74d92 | Sale Sharks | 6f08f859 | Glasgow Warriors | | 1 | f29de850 | ee0c6883 | fe2eda63 | Bayonne | f54be954 | Stormers | | 2 | 722ce3c7 | 83d92007 | 0aa2b3cc | Ulster | 2e49b214 | Racing 92 | | 3 | 6afe8441 | 822142db | deee3415 | Beziers | af22c862 | Soyaux Angouleme | | 4 | 719b1db1 | 822142db | 93d1e116 | Carcassonne | 34149c5c | Vannes | | 5 | 3aa1bb81 | 822142db | 98fce2f7 | Biarritz | 791d6c23 | Valence Romans | | 6 | 575235eb | 822142db | 870e9fb0 | Aurillac | d5468acc | Brive | | 7 | 66a1c770 | 822142db | 6992ab09 | Dax | 8b15a375 | Uso Nevers | | 8 | f196da5a | 822142db | 2bac4677 | Agen | 4f43e2ca | Provence Rugby | | 9 | 416f317e | 822142db | 6de7a527 | Mont De Marsan | 53983719 | Oyonnax |
</div>The old method below is deprecated as of v3:
matches = fetch_matches(date="20230101")
Deprecated, please use the fetch_matches_by_date() function instead
Then using that match id you can feed it into the match details function:
from rugbypy.match import *
match_details = fetch_match_details(match_id="35e0b16d")
match_details
Fetching match details for match_id: 35e0b16d...
| | match_id | date | season | competition_id | competition | venue_id | venue | city_played | home_team | away_team | ... | completed | is_tournament | played_on_grass | attendance | home_team_form | away_team_form | kickoff_time | home_score | away_score | referee | |----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----| | 0 | 35e0b16d | 20251205 | 2025 | ee0c6883 | European Rugby Champions Cup | 059580a0 | CorpAcq Stadium | Salford | Sale Sharks | Glasgow Warriors | ... | True | True | True | None | WLWLL | WWWWL | 14:30 | 21 | 26 | None |
<p>1 rows × 22 columns</p> </div>Team Stats
You can fetch all the teams within the rugbypy database from our registry with:
fetch_all_teams()
Fetching all teams available...
| | team_id | team_name | |-----|----------|----------------------| | 0 | 5fa975a5 | Ospreys | | 1 | 79c81be4 | Cardiff Blues | | 2 | 0e10c257 | Scarlets | | 3 | 973ef5cc | Gwent Dragons | | 4 | 93542906 | Newcastle | | ... | ... | ... | | 284 | 0c3eb5f5 | France Women 7s | | 285 | 91fc7224 | Brazil Women 7s | | 286 | 03817b3b | Fiji Women 7s | | 287 | 3baba750 | Canada Women 7s | | 288 | 04349324 | New Zealand Women 7s |
<p>289 rows × 2 columns</p> </div>You can fetch the team stats for a particular with:
from rugbypy.team import *
team_stats = fetch_team_stats(team_id="93542906")
team_stats
Fetching all team stats for team_id: 93542906...
| | team | game_date | team_id | team_vs | team_vs_id | match_id | players | 22m_entries | 22m_conversion | line_breaks | ... | scrums_won | tackles | territory | total_free_kicks_conceded | total_lineouts | tries | turnover_knock_on | turnovers_conceded | yellow_cards | metres_carried | |----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----| | 0 | Newcastle | 20250103 | 93542906 | Harlequins | 566d5199 | 3e3e2af2 | [4e129a8c, 39c63750, 04f27e6a, 8a10bc21, 0c2d9... | 9.0 | 4.22 | 6.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | | 1 | Newcastle | 20250111 | 93542906 | Ospreys | 5fa975a5 | f8d5672a | [39c63750, f9e52f58, ece068ae, bfb19f72, d5a55... | 10.0 | 3.50 | 8.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | | 2 | Newcastle | 20250117 | 93542906 | Montpellier | b6f40da2 | 57acf63c | [39c63750, 04f27e6a, f9e52f58, 0c2d9ef3, ece06... | 12.0 | 2.17 | 9.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | | 3 | Newcastle | 20250126 | 93542906 | Bristol | 47d5fe17 | 10cecec6 | [4e129a8c, 39c63750, 04f27e6a, 8a10bc21, 0c2d9... | 7.0 | 5.00 | 2.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | | 4 | Newcastle | 20250321 | 93542906 | Sale | d7d74d92 | 1eed410b | [4e129a8c, 04f27e6a, b7a90ca8, 8a10bc21, 0c2d9... | 8.0 | 1.88 | 3.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | | 87 | Newcastle | 20241129 | 93542906 | Saracens | 0026b492 | 355072a4 | [1ec5b586, a850f667, 0c2d9ef3, 40ef3013, 20006... | 9.0 | 1.89 | 4.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | | 88 | Newcastle | 20241208 | 93542906 | Pau | 73e02b8e | 04558689 | [1ec5b586, 548a8c29, a850f667, 0c2d9ef3, 20006... | 12.0 | 2.67 | 7.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | | 89 | Newcastle | 20241215 | 93542906 | Gwent Dragons | 973ef5cc | 9f498135 | [1ec5b586, 548a8c29, a850f667, 0c2d9ef3, 20006... | 12.0 | 1.83 | 6.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | | 90 | Newcastle | 20241221 | 93542906 | Bath | aa4f64f5 | e251bdbf | [1ec5b586, 548a8c29, a850f667, 0c2d9ef3, 40ef3... | 12.0 | 3.33 | 6.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | | 91 | Newcastle | 20241228 | 93542906 | Northampton | 895ae027 | c8490107 | [1ec5b586, a850f667, 0c2d9ef3, 40ef3013, 20006... | 12.0 | 5.08 | 12.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
<p>92 rows × 59 columns</p> </div>You can then fetch the team stats for a particular team on a particular date with:
from rugbypy.team import *
team_stats = fetch_team_stats(team_id="93542906", date="20251213")
team_stats
Fetching team stats for team_id: 93542906 on date: 20251213...
| | team | game_date | team_id | team_vs | team_vs_id | match_id | players | 22m_entries | 22m_conversion | line_breaks | ... | scrums_won | tackles | territory | total_free_kicks_conceded | total_lineouts | tries | turnover_knock_on | turnovers_conceded | yellow_cards | metres_carried | |----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----|----| | 18 | Newcastle | 20251213 | 93542906 | Lions | bb49fc47 | 87ab265a | [dbd1161c, 4e129a8c, 8a10bc21, 3b0a4aa4, 0c2d9... | 9.0 | 1.11 | 4.0 | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
<p>1 rows × 59 columns</p> </div>Player Stats
We have the ability to fetch player stats for all the games they have
been involved in. We firstly identify the player_id of a player by
searching our player registry.
from rugbypy.player import *
players = fetch_all_players()
players
| | player_id | player_name | |------|-----------|-------------------| | 0 | 9f0d99ce | Morgan Morris | | 1 | e31a204b | James Ratti | | 2 | b1d2895a | Max Nagy | | 3 | 25bfea05 | Daniel Kasende | | 4 | 24717f78 | Jac Morgan | | ... | ... | ... | | 8439 | b4a9513f | Paul Mullen | | 8440 | edf9d7e1 | Tom Baraer | | 8441 | da8da81b | Nathan Van de Ven | | 8442 | d5c9a047 | Nicolas Gali | | 8443 | 3834ffae | Nicholas Muli |
<p>8444 rows × 2 columns</p> </div>Or we can search for a certain player through our similarity tool:
from rugbypy.player import *
individual_player = fetch_player_id("jac morgan")
individual_player
