rugbypy 3.0.0

Repository for rugby data analytics

rugbypy

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

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