rugbypy

rugbypy is a Python package that aims to make rugby data more available to aid in the development of rugby analytics.

Data is updated daily 5AM UTC! Currently we only have data for 2022, 2023 & 2024

Requirements

python version 3.9

Install

pip install rugbypy

How to use

Match Stats

You can fetch all the matches that occured on a particular date with:

from rugbypy.match import *
matches = fetch_matches(date="20230101")
matches

Fetching matches on date: 20230101...

	match_id	competition_id	home_team_id	home_team	away_team_id	away_team	date
0	595735	267979	25907	Northampton Saints	25901	Harlequins	20230101
1	599464	270557	25965	Cardiff Blues	25968	Ospreys	20230101
2	599465	270557	25966	Scarlets	25967	Dragons	20230101
3	599466	270557	25926	Ulster	25925	Munster	20230101
4	599467	270557	25924	Leinster	25923	Connacht	20230101
5	597648	270559	25917	Clermont Auvergne	25922	Stade Toulousain	20230101

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="595735")
match_details

Fetching match details for match_id:595735...

	match_id	date	season	competition_id	competition	venue_id	venue	city_played	home_team	away_team	home_team_id	away_team_id	completed	is_tournament	played_on_grass	attendance	home_team_form	away_team_form
0	595735	20230101	2023	267979	Premiership Rugby	26070	cinch Stadium at Franklin's Gardens	Northampton	Northampton Saints	Harlequins	25907	25901	True	True	True	None	WLWWL	WTLWL

Team Stats

You can fetch the team stats for every game with:

from rugbypy.team import *
team_stats = fetch_team_stats(team_id="25901")
team_stats

Fetching all team stats for team_id: 25901...

	team	game_date	team_id	team_vs	team_vs_id	match_id	players	clean_breaks	conversion_goals	defenders_beaten	...	scrums_total	scrums_won	tackles	territory	total_free_kicks_conceded	total_lineouts	tries	turnover_knock_on	turnovers_conceded	yellow_cards
0	Harlequins	20220102	25901	Gloucester Rugby	25900	593902	[290946, 298485, 296815, 296184, 295534, 29512...	3.0	2.0	26.0	...	7.0	7.0	94.0	0.63	1.0	16.0	2.0	9.0	15.0	2.0
1	Harlequins	20220108	25901	Exeter Chiefs	116227	593909	[290946, 296815, 296184, 295534, 295121, 29484...	5.0	2.0	16.0	...	11.0	9.0	124.0	0.55	0.0	10.0	2.0	8.0	17.0	0.0
2	Harlequins	20220128	25901	Bath Rugby	25898	593913	[158708, 299436, 298485, 296815, 296791, 29553...	7.0	1.0	12.0	...	8.0	7.0	162.0	0.50	0.0	9.0	3.0	11.0	15.0	1.0
3	Harlequins	20220206	25901	Sale Sharks	25908	593922	[158708, 299436, 299031, 298764, 298487, 29848...	7.0	2.0	20.0	...	5.0	5.0	71.0	0.54	2.0	12.0	2.0	17.0	26.0	0.0
4	Harlequins	20220213	25901	Saracens	25909	593929	[158708, 299436, 298764, 298487, 298485, 29681...	3.0	1.0	23.0	...	4.0	2.0	98.0	0.56	1.0	15.0	1.0	11.0	17.0	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
56	Harlequins	20240421	25901	Sale Sharks	25908	597118	[290946, 257545, 301432, 299436, 299031, 29681...	8.0	3.0	36.0	...	3.0	3.0	119.0	0.45	0.0	21.0	5.0	8.0	15.0	0.0
57	Harlequins	20240427	25901	Northampton Saints	25907	597122	[290946, 257545, 301432, 300430, 299436, 29903...	10.0	4.0	22.0	...	6.0	5.0	125.0	0.45	0.0	12.0	6.0	4.0	6.0	3.0
58	Harlequins	20240505	25901	Stade Toulousain	25922	597203	[290946, 301432, 299436, 299031, 296815, 29679...	5.0	3.0	23.0	...	3.0	2.0	92.0	0.47	0.0	15.0	4.0	8.0	16.0	1.0
59	Harlequins	20240511	25901	Exeter Chiefs	116227	597126	[290946, 301432, 299436, 299031, 296815, 29679...	4.0	3.0	33.0	...	4.0	3.0	133.0	0.40	0.0	9.0	4.0	7.0	11.0	0.0
60	Harlequins	20240518	25901	Bristol Rugby	25899	597132	[290946, 301432, 299436, 299031, 299029, 29681...	6.0	4.0	24.0	...	4.0	3.0	129.0	0.45	1.0	16.0	4.0	6.0	12.0	0.0

61 rows × 42 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="25901", date="20230108")
team_stats

Fetching team stats for team_id: 25901 on date: 20230108...

	team	game_date	team_id	team_vs	team_vs_id	match_id	players	clean_breaks	conversion_goals	defenders_beaten	...	scrums_total	scrums_won	tackles	territory	total_free_kicks_conceded	total_lineouts	tries	turnover_knock_on	turnovers_conceded	yellow_cards
28	Harlequins	20230108	25901	Sale Sharks	25908	595741	[158708, 299436, 299031, 298485, 295117, 29477...	3.0	0.0	24.0	...	7.0	5.0	125.0	0.41	0.0	11.0	2.0	8.0	17.0	0.0

1 rows × 42 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 manifest file.

from rugbypy.player import *
player_manifest = fetch_all_players()
player_manifest.head()

	player_id	player_name
0	262799	AJ MacGinty
1	295135	Bevan Rodd
2	294815	Tom Roebuck
3	294814	Gus Warr
4	294810	Sam Dugdale

Or we can search for a certain player through our similarity tool:

from rugbypy.player import *
individual_player = fetch_player(name="johnny sexton")
individual_player

	player_id	player_name
1345	16004	Johnny Sexton
2156	291349	Ayden Johnstone
2795	149315	Anthony Watson

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

from rugbypy.player import *
player_stats = fetch_player_stats(player_id="16004")
player_stats

Fetching all player stats for player_id: 16004...

	player_id	game_date	name	team	team_id	competition_id	competition	match_id	team_vs	team_vs_id	...	rucks_won	runs	tackles	total_free_kicks_conceded	total_lineouts	tries	try_assists	turnover_knock_on	turnovers_conceded	yellow_cards
0	16004	20220205	Johnny Sexton	Ireland	3	180659	Six Nations	595080	Wales	4	...	4.0	9.0	7.0	0.0	0.0	0.0	1.0	1.0	2.0	0.0
1	16004	20220227	Johnny Sexton	Ireland	3	180659	Six Nations	595088	Italy	20	...	1.0	3.0	1.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0
2	16004	20220312	Johnny Sexton	Ireland	3	180659	Six Nations	595091	England	1	...	4.0	12.0	7.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
3	16004	20220319	Johnny Sexton	Ireland	3	180659	Six Nations	595093	Scotland	2	...	3.0	3.0	10.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0
4	16004	20220610	Johnny Sexton	Leinster	25924	270557	United Rugby Championship	594483	Bulls	25953	...	0.0	3.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
5	16004	20221216	Johnny Sexton	Leinster	25924	271937	European Rugby Champions Cup	597405	Gloucester Rugby	25900	...	0.0	0.0	7.0	0.0	0.0	0.0	0.0	1.0	1.0	0.0
6	16004	20230204	Johnny Sexton	Ireland	3	180659	Six Nations	596205	Wales	4	...	3.0	8.0	7.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0
7	16004	20230211	Johnny Sexton	Ireland	3	180659	Six Nations	596208	France	9	...	1.0	5.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
8	16004	20230312	Johnny Sexton	Ireland	3	180659	Six Nations	596216	Scotland	2	...	2.0	5.0	9.0	0.0	0.0	0.0	0.0	0.0	2.0	0.0
9	16004	20230318	Johnny Sexton	Ireland	3	180659	Six Nations	596219	England	1	...	6.0	9.0	5.0	0.0	0.0	0.0	0.0	1.0	2.0	0.0
10	16004	20230909	Johnny Sexton	Ireland	3	164205	Rugby World Cup	596156	Romania	12	...	0.0	5.0	4.0	0.0	0.0	2.0	0.0	0.0	0.0	0.0
11	16004	20230916	Johnny Sexton	Ireland	3	164205	Rugby World Cup	596166	Tonga	16	...	1.0	1.0	2.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0
12	16004	20230923	Johnny Sexton	Ireland	3	164205	Rugby World Cup	596175	South Africa	5	...	1.0	3.0	11.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0
13	16004	20231007	Johnny Sexton	Ireland	3	164205	Rugby World Cup	596190	Scotland	2	...	0.0	5.0	7.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0
14	16004	20231014	Johnny Sexton	Ireland	3	164205	Rugby World Cup	596195	New Zealand	8	...	0.0	1.0	11.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0

15 rows × 41 columns

We can also fetch the player stats on a particular date using fetch_player_stats. In this example we fetch Johnny Sextons player stats on 2023-09-16:

from rugbypy.player import *
player_stats = fetch_player_stats(player_id="16004", date="20230916")
player_stats

Fetching player stats for player_id: 16004 on date: 20230916...

	player_id	game_date	name	team	team_id	competition_id	competition	match_id	team_vs	team_vs_id	...	rucks_won	runs	tackles	total_free_kicks_conceded	total_lineouts	tries	try_assists	turnover_knock_on	turnovers_conceded	yellow_cards
11	16004	20230916	Johnny Sexton	Ireland	3	164205	Rugby World Cup	596166	Tonga	16	...	1.0	1.0	2.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0

1 rows × 41 columns