rugbypy

Repository for rugby data analytics.

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

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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

6171 rows × 4 columns

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

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

1 rows × 22 columns

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

289 rows × 2 columns

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

92 rows × 59 columns

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

1 rows × 59 columns

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

8444 rows × 2 columns

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
player_id player_name
4 24717f78 Jac Morgan
3518 2c8b48a5 Francesco Braga
5860 e387e07f Franco Lamanna

Once we have their player_id we can fetch their player stats using fetch_player_stats. In this example we fetch Jac Morgan player stats:

from rugbypy.player import *
player_stats = fetch_player_stats(player_id="24717f78")
player_stats
Fetching all player stats for player_id: 24717f78...
player_id name team team_id position carries line_breaks tackles_completed turnovers_lost turnovers_won ... offload penalty_goals points rucks_won runs tackles total_free_kicks_conceded total_lineouts turnover_knock_on turnovers_conceded
0 24717f78 Jac Morgan Ospreys 5fa975a5 blindside_flanker 9.0 0.0 19.0 0.0 0.0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 24717f78 Jac Morgan Ospreys 5fa975a5 blindside_flanker 11.0 0.0 12.0 1.0 1.0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2 24717f78 Jac Morgan Ospreys 5fa975a5 openside_flanker 10.0 0.0 30.0 0.0 2.0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
3 24717f78 Jac Morgan Wales b6319444 flanker 15.0 1.0 NaN NaN NaN ... 1.0 0.0 0.0 12.0 15.0 21.0 0.0 0.0 2.0 3.0
4 24717f78 Jac Morgan Wales b6319444 flanker 8.0 0.0 NaN NaN NaN ... 0.0 0.0 0.0 7.0 8.0 13.0 0.0 0.0 1.0 1.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
67 24717f78 Jac Morgan Wales b6319444 openside_flanker 14.0 0.0 27.0 0.0 1.0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
68 24717f78 Jac Morgan Wales b6319444 openside_flanker 11.0 0.0 28.0 0.0 1.0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
69 24717f78 Jac Morgan Ospreys 5fa975a5 number_eight 13.0 1.0 12.0 0.0 3.0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
70 24717f78 Jac Morgan Ospreys 5fa975a5 openside_flanker 9.0 0.0 11.0 0.0 2.0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
71 24717f78 Jac Morgan Ospreys 5fa975a5 blindside_flanker 14.0 0.0 12.0 0.0 4.0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

72 rows × 54 columns

We can also fetch the player stats on a particular date using fetch_player_stats. In this example we fetch Jac Morgans player stats on 2025-01-01:

from rugbypy.player import *
player_stats = fetch_player_stats(player_id="24717f78", date="20250101")
player_stats
Fetching player stats for player_id: 24717f78 on date: 20250101...
player_id name team team_id position carries line_breaks tackles_completed turnovers_lost turnovers_won ... offload penalty_goals points rucks_won runs tackles total_free_kicks_conceded total_lineouts turnover_knock_on turnovers_conceded
0 24717f78 Jac Morgan Ospreys 5fa975a5 blindside_flanker 9.0 0.0 19.0 0.0 0.0 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

1 rows × 54 columns

Competition data

You can fetch all competition ids to from our competition registry with:

from rugbypy.competition import *
competitions = fetch_all_competitions()
competitions
Fetching all competition...
competition_id competition_name
0 50e6935b World Rugby U20 Championship
1 0fad1a69 Premiership Womens Rugby
2 2f0de05a French Top 14
3 d7c59c8c Japan Rugby League One
4 822142db Pro D2
5 83d92007 European Rugby Challenge Cup
6 467a422f Womens Six Nations
7 0bc7d681 British and Irish Lions Tour
8 c2f89f12 Currie Cup
9 679f8455 Bunnings Npc
10 b5e3c6bd Women's Rugby World Cup
11 bc5d9ec5 Pacific Nations Cup
12 ee0c6883 European Rugby Champions Cup
13 2c3df351 Gallagher Prem
14 23df32a1 International Test Match
15 0ab9177f Major League Rugby
16 622e1591 URBA Top 12
17 814f16bc Mitre 10 Cup
18 877aa127 Super Rugby
19 30e7e1ca Womens Internationals
20 df0639c9 U20 Six Nations
21 a97ec065 Olympic Men's 7s
22 0f8b619e Olympic Women's Rugby Sevens

Venue data

You can fetch all venue ids to from our venue registry with:

from rugbypy.venue import *
competitions = fetch_all_venues()
competitions
Fetching all venues...
venue_id venue_name
0 fb155133 Cardiff Arms Park
1 1f19e651 Parc y Scarlets
2 74f2b7f9 Kingston Park
3 477771ab Kingsholm
4 3933483f Mattioli Woods Welford Road Stadium
... ... ...
345 8c0b3ee4 Estadio Jorge Luis Hirschi
346 8a535068 Estadio Brigadier General Estanislao López
347 b9f8a9dd Massey university
348 cc60410c Bluenergy Stadium
349 8c764117 Chambéry Savoie Stadium

350 rows × 2 columns