TennisExplorer Scraper

hipersoft/tennisexplorer-scraper

Scrape tennis match results and upcoming fixtures from TennisExplorer: ATP, WTA, Challenger and ITF singles and doubles with set scores, tiebreaks, winners, betting odds, H2H, rankings, surface and round. Any date range. Export CSV, JSON or Excel.

Run this scraper →See the API call

Total users

Monthly users

Total runs

Per match

$0.006

Last rebuilt

Oct 26, 2026

Overview

TennisExplorer Scraper exports tennis match results and upcoming fixtures from TennisExplorer for any date or date range. It covers ATP, WTA, Challenger and ITF singles and doubles. Each match row includes set-by-set scores with tiebreaks, the winner, closing betting odds, tournament, country and start time. You can also turn on match details to add round, surface, player rankings, age, height, playing hand, head-to-head record and odds from every bookmaker, both opening and current. Export to CSV, Excel, JSON or XML for betting models, backtesting, tennis analytics and fantasy sports.

What does TennisExplorer Scraper do?

TennisExplorer Scraper turns TennisExplorer's daily results and match pages into a clean, flat tennis dataset:

🎾 Match results for any day, week, month or season, covering every ATP, WTA, Challenger and ITF match listed.
📅 Upcoming matches with scheduled start times, pre-match odds and how many times the players have met.
🧮 Set scores with tiebreaks, sets won and the winner, ready for modelling.
💰 Betting odds: the main home/away odds on every row, plus per-bookmaker opening and current odds with match details.
🧑 Player details: full name, singles and doubles ranking, birthdate, height, weight, playing hand and year turned pro.
🔁 Head-to-head record and round and surface for every match.
🔎 Filters by tournament name and player name.

What tennis data can you scrape?

Match data	With match details
Match ID and link	Round (final, semifinal, quarterfinal…)
Date, local time and UTC start time	Surface (hard, clay, grass, indoors)
Match type (men's/women's singles/doubles)	Full player names
Tournament name, link and country	Singles and doubles rankings
Player 1 and player 2, profile links and seeds	Birthdate, height, weight
Winner	Plays (right/left) and turned pro
Sets won by each player	Head-to-head wins for each player
Set-by-set games and tiebreak points	Odds from every bookmaker: opening and current
Score string, e.g. `7-6(4), 6-3`	Highlights video link
Home/away betting odds
Status (finished, scheduled…) and H2H matches count

Use cases for tennis data

Betting models and backtesting: years of results with closing and opening odds for ROI, value-bet and market-movement studies.
Tennis analytics: serve win rates by surface, upset frequency by ranking gap, tiebreak performance and more.
Fantasy tennis and tipping: upcoming schedules with odds and head-to-head context.
Sports media and apps: feed daily results and fixtures into websites, newsletters and bots.
Machine learning datasets: clean labeled match outcomes with player features.
Odds comparison: compare bookmaker prices for every match.

How to scrape tennis results from TennisExplorer

Click Try for free to open TennisExplorer Scraper in Apify Console.
Choose Match results or Upcoming matches.
Select the match types (men's/women's, singles/doubles) and a start date and optional end date.
Optionally filter by tournament or player, and switch on Include match details.
Click Start, then download your tennis dataset as CSV, Excel or JSON.

Input

Example input

{
  "mode": "results",
  "matchTypes": ["atp-single", "wta-single"],
  "startDate": "2026-09-01",
  "endDate": "2026-09-30",
  "tournamentFilter": "",
  "playerFilter": "",
  "includeMatchDetails": true,
  "maxMatches": 0
}

Field	Description
What to scrape	`results` (finished matches) or `upcoming` (scheduled matches).
Match types	Men's singles, women's singles, men's doubles, women's doubles. ATP/WTA lists include Challenger and ITF events.
Start date / End date	The date range. Leave the end date empty for a single day.
Tournament contains	Only matches from tournaments whose name contains this text.
Player contains	Only matches involving a player whose name contains this text.
Include match details	Adds round, surface, rankings, player bio, head-to-head record and per-bookmaker odds.
Max matches	Stop after this many matches (0 = no limit).
Proxy configuration	Optional, not needed for normal use.

Scrape a whole tournament or season

Set a date range covering the event and put its name in Tournament contains, for example Wimbledon with dates from 2026-06-29 to 2026-07-12. To get a whole season, set January 1 to December 31.

Get tomorrow's matches every day

Choose Upcoming matches and leave the dates empty, then create an Apify schedule to run it every morning.

Output

Match result example (with match details)

{
  "matchId": "3338963",
  "matchUrl": "https://www.tennisexplorer.com/match-detail/?id=3338963",
  "matchType": "atp-single",
  "date": "2026-10-04",
  "time": "13:10",
  "startTimeUtc": "2026-10-04T11:10:00.000Z",
  "status": "finished",
  "tournament": "Beijing",
  "tournamentCountry": "CN",
  "player1": "Djokovic N.",
  "player1Seed": "6",
  "player2": "Zverev A.",
  "player2Seed": "1",
  "winner": "Djokovic N.",
  "setsPlayer1": 2,
  "setsPlayer2": 1,
  "score": "4-6, 6-4, 6-4",
  "sets": [
    { "set": 1, "player1": 4, "player2": 6, "tiebreak": null },
    { "set": 2, "player1": 6, "player2": 4, "tiebreak": null },
    { "set": 3, "player1": 6, "player2": 4, "tiebreak": null }
  ],
  "oddsPlayer1": 3.16,
  "oddsPlayer2": 1.35,
  "round": "quarterfinal",
  "surface": "hard",
  "player1Details": { "fullName": "Djokovic Novak", "ranking": 11, "birthdate": "22. 5. 1987", "heightCm": 187, "weightKg": 80, "plays": "right", "turnedPro": "2003" },
  "player2Details": { "fullName": "Zverev Alexander", "ranking": 2, "birthdate": "20. 4. 1997", "heightCm": 198, "weightKg": 90, "plays": "right" },
  "h2hPlayer1Wins": 11,
  "h2hPlayer2Wins": 7,
  "bookmakerOdds": [
    { "bookmaker": "10Bet", "player1": 3.1, "player2": 1.33, "player1Opening": 2.63, "player2Opening": 1.45 }
  ]
}

Output schema

Field	Type	Description
`matchId`	string	TennisExplorer match ID.
`matchUrl`	string (URL)	Match page.
`matchType`	string	`atp-single`, `wta-single`, `atp-double` or `wta-double`.
`date`	string	Match date (YYYY-MM-DD).
`time`	string	Local start time (Central European time).
`startTimeUtc`	string (ISO date)	Start time in UTC.
`status`	string	`finished`, `incomplete`, `not-played`, `scheduled` or `live-or-finished`.
`tournament`	string	Tournament name.
`tournamentUrl`	string (URL)	Tournament page.
`tournamentCountry`	string	Host country code.
`player1`, `player2`	string	Player names (doubles teams as "A / B").
`player1Url`, `player2Url`	string (URL)	Player profile pages.
`player1Seed`, `player2Seed`	string	Seed or entry status (e.g. `1`, `Q`, `WC`).
`winner`	string	Winner's name.
`setsPlayer1`, `setsPlayer2`	integer	Sets won.
`score`	string	Score string with tiebreaks, e.g. `7-6(4), 6-3`.
`sets`	array	Per-set games for each player and tiebreak points.
`oddsPlayer1`, `oddsPlayer2`	number	Main decimal betting odds.
`h2hMatches`	integer	Previous meetings (upcoming matches).
`round`	string	Round (with match details).
`surface`	string	Court surface (with match details).
`player1Details`, `player2Details`	object	Full name, rankings, birthdate, height, weight, plays, turned pro.
`h2hPlayer1Wins`, `h2hPlayer2Wins`	integer	Head-to-head record.
`bookmakerOdds`	array	Per-bookmaker current and opening odds.
`highlightsUrl`	string (URL)	Match highlights video, if available.
`scrapedAt`	string (ISO date)	When the row was collected.

How much does it cost to scrape TennisExplorer?

TennisExplorer Scraper uses pay-per-match pricing: a small fixed price for each match row. Higher Apify subscription plans get an automatic discount. See the Pricing tab for the exact price per match. A typical day of ATP and WTA singles has about 300 to 600 matches.

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FAQ

Which tournaments are covered? Everything TennisExplorer lists: Grand Slams, ATP and WTA tour events, Challengers, WTA 125 and ITF tournaments.

How far back can I go? TennisExplorer has results going back many years. Set any start and end date.

Are the odds opening or closing odds? The main oddsPlayer1/oddsPlayer2 columns are TennisExplorer's headline odds for the match. With match details on, bookmakerOdds gives each bookmaker's current and opening odds.

What time zone are the times in? time is shown as on TennisExplorer (Central European time), and startTimeUtc gives the same moment in UTC.

Can I get doubles matches? Yes. Select men's or women's doubles in Match types.

How do I get only one player's matches? Enter their surname in Player contains, for example Sinner, and set the date range.

What formats can I export? CSV, Excel, JSON, XML, HTML and RSS, or read the results through the Apify API.

Can I integrate TennisExplorer Scraper with other apps? TennisExplorer Scraper can be connected with almost any cloud service or web app thanks to integrations on the Apify platform. It works with Make, Zapier, Slack, Airbyte, GitHub, Google Drive and many more, plus the Apify API, JS/Python clients and MCP. Or use webhooks to trigger an action whenever a run finishes.

Is it legal to scrape TennisExplorer? The scraper only collects publicly available sports results. You are responsible for using the data in line with TennisExplorer's terms.

Notes

Original clean-room implementation. Returns only public data; you are responsible for compliance with TennisExplorer's terms. Not affiliated with TennisExplorer.

Input

Field	Type	Description
`mode` What to scrape	string · results, upcoming	Finished match results or upcoming fixtures (with pre-match odds and head-to-head count). Default: `"results"`
`matchTypes` Match types	array	Which tours to include. ATP and WTA lists also cover Challenger and ITF events. Default: `["atp-single", "wta-single"]`
`startDate` Start date	string	First day to scrape (YYYY-MM-DD). Defaults to yesterday for results and today for upcoming matches.
`endDate` End date	string	Last day to scrape (YYYY-MM-DD). Leave empty to scrape only the start date. Long ranges such as a whole season are supported.
`tournamentFilter` Tournament contains	string	Only keep matches whose tournament name contains this text, e.g. Beijing, Wimbledon, Challenger.
`playerFilter` Player contains	string	Only keep matches where a player name contains this text, e.g. Djokovic.
`includeMatchDetails` Include match details	boolean	Also open each match page to add round, surface, full player names, rankings, age, height, weight, playing hand, head-to-head record, highlights and odds from every bookmaker (current and opening). Slower. Default: `false`
`maxMatches` Max matches	integer	Stop after this many matches. 0 means no limit. Default: `0`
`proxyConfiguration` Proxy configuration	object	Not required. Use a proxy only for very large date ranges if you run into rate limits. Default: `{"useApifyProxy": false}`

API

curl -X POST "https://api.apify.com/v2/acts/hipersoft~tennisexplorer-scraper/run-sync-get-dataset-items?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
  "mode": "results",
  "matchTypes": [
    "atp-single",
    "wta-single"
  ]
}'

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")
run = client.actor("hipersoft/tennisexplorer-scraper").call(run_input={
    "mode": "results",
    "matchTypes": [
        "atp-single",
        "wta-single"
    ]
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });
const run = await client.actor('hipersoft/tennisexplorer-scraper').call({
  "mode": "results",
  "matchTypes": [
    "atp-single",
    "wta-single"
  ]
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);

Pricing

Event	Price	Paid plans from
Match One tennis match row in the dataset.	$0.006	$0.003

You pay only for what the run returns. Set a maximum cost per run and it stops cleanly at that limit.

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