This repository contains Python scripts that scrape tennis data from the ATP World Tour website, as of October 2017. Note that if the site layout is subsequently redesigned, then these scripts will no longer work.
This work is licensed under a Creative Commons Attribution 4.0 International License.
- A. Scraping tournament data by year
- B. Scraping match scores for each tournament
- C. Scraping match stats for each match
A1. The tournaments.py script ^
The following Python script:
scrapes the following data:
tourney_year_id
tourney_order
tourney_type
tourney_name
tourney_id
tourney_slug
tourney_location
tourney_date
year
tourney_month
tourney_day
tourney_singles_draw
tourney_doubles_draw
tourney_conditions
tourney_surface
tourney_fin_commit_raw
currency
tourney_fin_commit
tourney_url_suffix
singles_winner_name
singles_winner_url
singles_winner_player_slug
singles_winner_player_id
doubles_winner_1_name
doubles_winner_1_url
doubles_winner_1_player_slug
doubles_winner_1_player_id
doubles_winner_2_name
doubles_winner_2_url
doubles_winner_2_player_slug
doubles_winner_2_player_id
from pages like the following:
The CSV file for all tournament data from 1877-2016 is found in:
A2. Example usage ^
Example command line usage and output is as follows:
$ time python3 tournaments.py 2010 2019
Year Tournaments
---- -----------
2010 66
2011 66
2012 67
2013 65
2014 65
2015 66
2016 67
2017 69
2018 69
2019 68
real 0m14.230s
user 0m7.306s
sys 0m0.104s
B1. The match_scores.py script ^
The following Python script:
scrapes the following data:
tourney_year_id
tourney_order
tourney_name
tourney_slug
tourney_url_suffix
start_date
start_year
start_month
start_day
end_date
end_year
end_month
end_day
currency
prize_money
match_index
tourney_round_name
round_order
match_order
winner_name
winner_player_id
winner_slug
loser_name
loser_player_id
loser_slug
winner_seed
loser_seed
match_score_tiebreaks
winner_sets_won
loser_sets_won
winner_games_won
loser_games_won
winner_tiebreaks_won
loser_tiebreaks_won
match_id
match_stats_url_suffix
from pages like the following:
The CSV files for all match scores data from 1877-2016 is found in:
B2. Example usage ^
Example command line usage and output is as follows:
$ time python3 match_scores.py 1967 1968
Scraping match info for 4 tournaments...
Year Order Tournament Matches
---- ----- ---------- -------
1967 1 australian-championships 58
1967 2 french-championships 123
1967 3 wimbledon 127
1967 4 us-championships 127
Scraping match info for 15 tournaments...
Year Order Tournament Matches
---- ----- ---------- -------
1968 1 australian-chps 61
1968 2 bournemouth 31
1968 3 rome 103
1968 4 roland-garros 126
1968 5 barcelona 57
1968 6 london 53
1968 7 wimbledon 127
1968 8 cincinnati 11
1968 9 dublin 31
1968 10 gstaad 31
1968 11 hamburg 47
1968 12 toronto 0
1968 13 us-open 95
1968 14 los-angeles 68
1968 15 buenos-aires 36
real 0m21.037s
user 0m8.330s
sys 0m0.211s
C1. The match_stats.py script ^
The following Python script:
scrapes the following data:
match_id
tourney_slug
match_stats_url_suffix
match_time
match_duration
winner_slug
winner_serve_rating
winner_aces
winner_double_faults
winner_first_serves_in
winner_first_serves_total
winner_first_serve_points_won
winner_first_serve_points_total
winner_second_serve_points_won
winner_second_serve_points_total
winner_break_points_saved
winner_break_points_serve_total
winner_service_games_played
winner_return_rating
winner_first_serve_return_won
winner_first_serve_return_total
winner_second_serve_return_won
winner_second_serve_return_total
winner_break_points_converted
winner_break_points_return_total
winner_return_games_played
winner_service_points_won
winner_service_points_total
winner_return_points_won
winner_return_points_total
winner_total_points_won
winner_total_points_total
loser_slug
loser_serve_rating
loser_aces
loser_double_faults
loser_first_serves_in
loser_first_serves_total
loser_first_serve_points_won
loser_first_serve_points_total
loser_second_serve_points_won
loser_second_serve_points_total
loser_break_points_saved
loser_break_points_serve_total
loser_service_games_played
loser_return_rating
loser_first_serve_return_won
loser_first_serve_return_total
loser_second_serve_return_won
loser_second_serve_return_total
loser_break_points_converted
loser_break_points_return_total
loser_return_games_played
loser_service_points_won
loser_service_points_total
loser_return_points_won
loser_return_points_total
loser_total_points_won
loser_total_points_total
from pages like the following:
The CSV files for all match stats data from 1991-2016 is found in:
[Note: the ATP did not start keeping match stats data until 1991.]
C2. Example usage ^
Example command line usage and output is as follows:
$ time python3 match_stats.py 1991
Collecting match stats data for 83 tournaments:
Index Tourney slug Matches
----- ------------ -------
0 adelaide 30/30 (100%)
1 wellington 30/30 (100%)
2 sydney 31/31 (100%)
3 auckland 29/29 (100%)
4 australian-open 127/127 (100%)
5 milan 29/31 (94%)
6 san-francisco 30/31 (97%)
7 guaruja 30/31 (97%)
8 philadelphia 47/47 (100%)
9 brussels 31/31 (100%)
10 stuttgart 31/31 (100%)
11 memphis 47/47 (100%)
12 rotterdam 31/31 (100%)
13 chicago 30/30 (100%)
14 indian-wells 54/54 (100%)
15 copenhagen 31/31 (100%)
16 miami 95/95 (100%)
17 estoril 31/31 (100%)
18 hong-kong 31/31 (100%)
19 orlando 31/31 (100%)
20 tokyo 53/54 (98%)
21 barcelona 54/54 (100%)
22 nice 31/31 (100%)
23 seoul 31/31 (100%)
24 monte-carlo 54/54 (100%)
25 singapore 31/31 (100%)
26 madrid 31/31 (100%)
27 munich 31/31 (100%)
28 tampa 29/30 (97%)
29 hamburg 55/55 (100%)
30 charlotte 1/30 (3%)
31 rome 59/59 (100%)
32 umag 29/31 (94%)
33 dusseldorf Match structure/stats URL problem
34 bologna 30/30 (100%)
35 roland-garros 47/47 (100%)
36 london 53/53 (100%)
37 florence 30/31 (97%)
38 rosmalen 31/31 (100%)
39 genova 31/31 (100%)
40 manchester 31/31 (100%)
41 wimbledon 121/121 (100%)
42 gstaad 30/30 (100%)
43 bastad 31/31 (100%)
44 newport 29/31 (94%)
45 stuttgart 47/47 (100%)
46 washington 55/55 (100%)
47 montreal 55/55 (100%)
48 hilversum 30/30 (100%)
49 kitzbuhel 47/47 (100%)
50 los-angeles 31/31 (100%)
51 san-marino 29/30 (97%)
52 cincinnati 55/55 (100%)
53 prague 30/31 (97%)
54 indianapolis 55/55 (100%)
55 new-haven 54/54 (100%)
56 long-island 31/31 (100%)
57 schenectady 30/31 (97%)
58 us-open 125/125 (100%)
59 bordeaux 31/31 (100%)
60 brasilia 46/47 (98%)
61 geneva 31/31 (100%)
62 basel 31/31 (100%)
63 palermo 31/31 (100%)
64 brisbane 30/31 (97%)
65 sydney 47/47 (100%)
66 toulouse 31/31 (100%)
67 athens 30/31 (97%)
68 tokyo 45/47 (96%)
69 berlin 31/31 (100%)
70 tel-aviv 30/31 (97%)
71 lyon 31/31 (100%)
72 vienna 31/31 (100%)
73 stockholm 47/47 (100%)
74 guaruja 31/31 (100%)
75 paris 46/46 (100%)
76 buzios 31/31 (100%)
77 birmingham 31/31 (100%)
78 moscow 30/30 (100%)
79 sao-paulo 29/29 (100%)
80 atp-tour-world-championship 1/1 (100%)
81 atp-tour-world-doubles-championship Match structure/stats URL problem
82 munich Match structure/stats URL problem
real 36m11.241s
user 2m13.432s
sys 0m4.073s