rugbypy

Repository for rugby data analytics.

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

PyPI - Downloads

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