Collect ATP Tennis Statistics via Web Scraping

Web Scraping – ATP Tennis Statistics → Excel

I am looking for a freelancer experienced in Python / web scraping / data extraction to collect and combine ATP tennis statistics into a clean Excel file.

The project concerns approximately 450–500 ATP men’s singles main-draw matches from these 9 tournaments in 2026:

* Montreal
* Washington
* Los Cabos
* Miami
* Indian Wells
* Dubai
* Acapulco
* Delray Beach
* Doha

Only ATP men’s singles main-draw matches are required.

1. TennisTourData – Aces

For every match, I need to enter both players into this TennisTourData comparison interface:

https://tennistourdata.com/atp-aces/?ttd_mode=compare&ttd_surface=hard&ttd_extra_surface=none&ttd_draw=main&ttd_stat_sort=metric&ttd_player=&ttd_compare_a=Merida+Aguilar+D.&ttd_compare_b=Tien+L.&ttd_type=all&ttd_year=last52&ttd_min_matches=&ttd_view=overall&ttd_page=1

Settings must be:

* Tour: All
* Year: Last 52 weeks
* Surface: Hard
* Draw: Main

Player names must be entered accurately, including the first initial, especially when players have the same surname (e.g. the Cerundolo brothers).

Required statistics for each player:

* Matches
* Aces
* Aces/Match
* Serves
* Ace %

Aces/Match should be calculated.

2. TennisTourData – Aces Against

Use this interface:

https://tennistourdata.com/atp-return/?ttd_mode=compare&ttd_surface=hard&ttd_extra_surface=clay&ttd_draw=main&ttd_stat_sort=count&ttd_player=&ttd_compare_a=Merida+Aguilar+D.&ttd_compare_b=Tien+L.&ttd_type=all&ttd_year=last52&ttd_min_matches=&ttd_view=overall&ttd_page=1

Same settings:

* Tour: All
* Year: Last 52 weeks
* Surface: Hard
* Draw: Main

Required:

* Aces Against
* Ace Against / Match
* Serves Against
* Ace Against %

Ace Against / Match should be calculated.

3. Actual match Aces

For every individual match, I need the actual number of Aces hit by each player in that match.

This can be obtained from a reliable tennis statistics/results website such as Flashscore, TennisTemple, or another reliable source.

Excel structure

One row = one match.

Columns:

1. Player 1
2. Player 2
3. Match Aces – Player 1
4. Match Aces – Player 2
5. Matches – Player 1
6. Aces – Player 1
7. Aces/Match – Player 1
8. Serves – Player 1
9. Ace % – Player 1
10. Matches – Player 2
11. Aces – Player 2
12. Aces/Match – Player 2
13. Serves – Player 2
14. Ace % – Player 2
15. Aces Against – Player 1
16. Ace Against / Match – Player 1
17. Serves Against – Player 1
18. Ace Against % – Player 1
19. Aces Against – Player 2
20. Ace Against / Match – Player 2
21. Serves Against – Player 2
22. Ace Against % – Player 2

Please use Excel formulas for the calculated fields where appropriate.

Important

The most important requirement is data accuracy and correct player matching.

The final dataset must correctly link:

Match → Players → TennisTourData statistics → Aces Against statistics → Actual match Aces.

Please pay particular attention to players with similar names or the same surname.

I would also appreciate a second Sources sheet containing the URLs used to obtain the data, if possible.

Test before the full project

Before completing the entire dataset, I would like the freelancer to demonstrate the process on 5–10 matches.

The test should include all the requested fields and demonstrate that the data is correctly matched.

Budget

I have a small budget, so please provide your best fixed-price offer.

In your proposal, please specify:

* Your experience with Python/web scraping
* How you plan to scrape TennisTourData
* Which source you would use for the actual match Aces
* Your fixed price
* Estimated delivery time
* Whether you provide the Python code