Tennisexplorer Scraper — matches, odds, rankings | $3.99/1k

What does Tennisexplorer Scraper do?

Tennisexplorer Scraper extracts structured tennis data from tennisexplorer.com — one row per match, for any day back to 2000. Each row carries the tournament (with country, tier and singles/doubles draw), both players with the site's stable player IDs, set-by-set scores with tiebreaks parsed properly, sets won, the winner, and both players' decimal odds. The same run can also return upcoming fixtures with head-to-head records, the ATP and WTA ranking tables, and full player profiles.

How to use this actor

• 👉 Register for a free Apify account — no credit card required.
• 🎉 Just click Sign up free on Apify → and complete a quick signup.
• 💰 A free Apify account includes $5 in monthly credits — enough to test this actor.
• ⏳ Scrape during the free trial, with no commitment or upfront payment required.

Key features

• 📦 Compact mode — core fields only — date, time, tournament, both players, score, winner and both prices — when you want a lean table instead of the full row.
• 🧹 Empty-field stripping — drop null, empty-string, and empty-array fields from each record before push. Smaller payloads for AI agents and dashboards that already handle missing fields gracefully.
• ♻️ Incremental mode — recurring runs emit and charge only for matches that are new or whose score, odds or status changed since the last run. The first run builds the baseline; a daily run on the same stateKey after that costs a fraction of a full re-scrape.
• 📤 Export anywhere — Download the dataset as JSON, CSV, or Excel from the Apify Console, or stream live via the Apify API and integrations (Make, Zapier, Google Sheets, n8n, …).
• 🔌 MCP connectors — push matches straight into Notion via Apify's MCP connectors — no glue code between the run and your database.

What data can you extract from tennisexplorer.com?

Each result includes Core match fields (listingId, title, recordType, rank, rankChange, previousRank, points, and player, and more) and detail fields when enrichment is enabled (detailFetched). In standard mode, all fields are always present — unavailable data points are returned as null, never omitted. In compact mode, only core fields are returned.

Enable detail enrichment in the input to fetch each record's detail page — extra fields the search results omit.

Input

The main inputs are a search keyword and a result limit. Additional filters and options are available in the input schema.

Key parameters:

• query — Paste ANY tennisexplorer.com URL and it is routed automatically — a match, a player, a ranking table (incl. a year archive), a results day or an upcoming day.
  
  The day to collect matches for, as YYYY-MM-DD (e.g. 2026-08-10). today and yesterday also work. A JSON array scrapes several in one run.
  
  Other surfaces, same syntax:
  • upcoming:2026-08-14 — fixtures not yet played, with pre-match odds where the market is open (odds appear close to the match; a day two out usually has none yet).
  • ranking:atp-men or ranking:wta-women — the ranking table, 60 players per page.
  • search:alcaraz — find a player's id by name. Returns id, full name and country. Use this to get the id right: Carlos Alcaraz is alcaraz-5ab70, not alcaraz.
  • detail:3289773 — one match in full: round and surface (which appear nowhere else), both players' ranking at match time plus birthdate, height, weight and playing hand, the head-to-head record with every previous meeting, and per-bookmaker odds across four markets with the opening price and every line move.
  • ranking:atp-men:2025 — the ranking table as it stood in an earlier year.
  • player:sinner-8b8e8 — one player's profile (country, birth date, height/weight, plays, current and career-best rank in singles and doubles). Take the id from a match row's player1Id or a ranking row's playerId — do not guess it: player:alcaraz is Alcaraz Susana L. of Mexico, not Carlos Alcaraz.
  
  Rows carry recordType (match, ranking or player) so you can tell them apart.
• dateFrom — First day of a date range, YYYY-MM-DD. Collects every day from here through Date To inclusive — the same as listing each day in the Results Day field.
• dateTo — Last day of the range, YYYY-MM-DD. Give only one of the two and that single day is collected.
• matchIds — Match ids to collect in full detail — round and surface, both players' ranking at match time, the head-to-head record with every previous meeting, and per-bookmaker odds with the opening price and every line move. A pasted match-detail URL works too.
• tour — Limit a results or upcoming day to one tour. All tours also includes doubles and team events; the singles options are exactly the site’s own ATP/WTA filters. Measured on 2026-08-12: 383 matches for all tours, 171 ATP singles, 121 WTA singles. Ignored by the ranking, player, search and match-detail modes. (default: "all")
• startUrls — Any tennisexplorer.com URLs. Each is classified automatically into the surface it belongs to, the same way a pasted URL in the Results Day field is. (default: [])
• maxResults — Maximum total matches across all days (0 = every match of every requested day). A busy day holds roughly 500 matches. (default: 100)
• compact — Core fields only — date, time, tournament, both players, score, winner, both odds and the match URL. Drops the per-set breakdown and the scrape bookkeeping. (default: false)
• excludeEmptyFields — Drop null, empty-string, and empty-array fields from each record before push. Smaller payloads for AI agents and dashboards. (default: false)
• incrementalMode — Compare against previous run state and report what is new or changed — a live match whose score or odds moved comes back as UPDATED. stateKey is optional; it defaults to a value derived from your search inputs so different days never share state. (default: false)
• stateKey — Optional. Stable identifier for the tracked set of matches. Leave empty to auto-generate from the search inputs.
• skipReposts — When incremental, skip records whose content matches an entry that dropped out of a prior run (cross-run duplicate detection). (default: false)
• ...and 15 more parameters

Input examples

One day of results — Any date as YYYY-MM-DD, or the aliases today / yesterday.

→ Every match played that day — roughly 500 on a busy day — with scores, tiebreaks, winner and both players' odds.

{
  "query": "2026-08-09"
}

Several days in one run — Pass a JSON array of days. Allocate more memory for long ranges.

→ Every match across all listed days, deduplicated.

{
  "query": "[\"2026-08-07\",\"2026-08-08\",\"2026-08-09\"]",
  "maxResults": 0
}

Only what changed since the last run — Incremental mode keeps state between runs under a stateKey, so a repeat run emits only new or updated matches. A result that has not moved is filtered out.

→ On the first run, every match for the day. On later runs, only matches that are new or whose score, odds or status changed.

{
  "query": "today",
  "incrementalMode": true,
  "stateKey": "tennis-daily"
}

Upcoming fixtures with odds and head-to-head — Odds appear close to the match — a day two out usually has none yet.

→ Scheduled matches with start time, pre-match odds where the market is open, and each player's career wins over the opponent.

{
  "query": "upcoming:2026-08-14"
}

ATP or WTA rankings — 60 players per page; request further pages for more.

→ Rank, points, places moved since the last publication, and the derived previous rank.

{
  "query": "ranking:atp-men"
}

A player profile — Take the id from a match row's player1Id or a ranking row's playerId — do not guess it. /player/alcaraz/ is Alcaraz Susana L. of Mexico, not Carlos Alcaraz.

→ Country, birth date, height and weight where published, playing hand, and current plus career-best rank in singles and doubles.

{
  "query": "player:sinner-8b8e8"
}

Output

Each run produces a dataset of structured match records. Results can be downloaded as JSON, CSV, or Excel from the Dataset tab in Apify Console.

Example match record

{
  "listingId": "93e7fcf67ab428c603c231b54e12c37736127dc05d721d3707a4e7c1bc89d07a",
  "title": "Tien L. v Tirante T.",
  "recordType": "match",
  "tournament": "Montreal",
  "date": "2026-08-09",
  "time": "20:15",
  "player1": "Tien L.",
  "player2": "Tirante T.",
  "player1Seed": 12,
  "player1Id": "tien",
  "player2Id": "tirante",
  "player1Url": "https://www.tennisexplorer.com/player/tien/",
  "player2Url": "https://www.tennisexplorer.com/player/tirante/",
  "player1Ids": [
    "tien"
  ],
  "player2Ids": [
    "tirante"
  ],
  "isDoubles": false,
  "setsPlayer1": [
    "6",
    "6"
  ],
  "setsPlayer2": [
    "4",
    "4"
  ],
  "resultPlayer1": "2",
  "resultPlayer2": "0",
  "score": "6-4, 6-4",
  "winner": "Tien L.",
  "oddsPlayer1": "1.43",
  "oddsPlayer2": "2.83",
  "matchUrl": "https://www.tennisexplorer.com/match-detail/?id=3289783",
  "status": "completed",
  "startsAt": "2026-08-09T20:15:00+01:00",
  "oddsPlayer1Decimal": 1.43,
  "oddsPlayer2Decimal": 2.83,
  "tournamentSlug": "montreal",
  "tournamentSeason": 2026,
  "tournamentTour": "atp-men",
  "tournamentCountry": "ca",
  "tournamentCategory": "men2",
  "tournamentDrawType": "singles",
  "tournamentGender": "men",
  "tournamentUrl": "https://www.tennisexplorer.com/montreal/2026/atp-men/",
  "portalUrl": "https://www.tennisexplorer.com/match-detail/?id=3289783",
  "scrapedAt": "2026-08-12T16:29:56.707Z",
  "searchQuery": "2026-08-09",
  "contentQuality": "full",
  "detailFetched": false,
  "source": "tennisexplorer.com",
  "contentHash": "35344a8f90b0157345630d0b242daeaf64d9cde6a423512652a974a79701f479",
  "changeType": "NEW",
  "isRepost": false
}

Incremental fields

When incremental mode is on, each record also carries:

• changeType — one of NEW, UPDATED, UNCHANGED, REAPPEARED, EXPIRED.
• isRepost, repostOfId, repostDetectedAt — populated when a new listing matches the tracked content of a previously expired one. Set skipReposts: true to drop detected reposts from the output.

How to scrape tennisexplorer.com

1. Go to Tennisexplorer Scraper in Apify Console.
2. Enter a search keyword.
3. Set maxResults to control how many results you need.
4. Enable includeDetails if you need the extra detail-page fields.
5. Click Start and wait for the run to finish.
6. Export the dataset as JSON, CSV, or Excel.

Use cases

• Extract match data from tennisexplorer.com for market research and competitive analysis.
• Monitor new and changed matches on scheduled runs without processing the full dataset every time.
• Feed structured data into AI agents, MCP tools, and automated pipelines using compact mode.
• Export clean, structured data to dashboards, spreadsheets, or data warehouses.

How much does it cost to scrape tennisexplorer.com?

Tennisexplorer Scraper uses pay-per-event pricing. You pay a small fee when the run starts and then for each result that is actually produced.

• Run start: $0.005 per run
• Per result: $0.00399 per match record

Example costs:

• 10 results: $0.045
• 25 results: $0.1
• 100 results: $0.4
• 200 results: $0.8
• 500 results: $2

Example: recurring monitoring savings

These examples compare full re-scrapes with incremental runs at different churn rates. Churn is the share of matches that are new or whose tracked content changed since the previous run. Actual churn depends on your query breadth, source activity, and polling frequency — the scenarios below are examples, not predictions.

Example setup: 250 matches per run, daily polling (30 runs/month). Costs scale linearly with the number of matches.

Churn rate	Full re-scrape run cost	Incremental run cost	Savings vs full re-scrape	Monthly cost after baseline
5% — stable niche query	$1.00	$0.05	$0.95 (95%)	$1.65
15% — moderate broad query	$1.00	$0.15	$0.85 (85%)	$4.64
30% — high-volume aggregator	$1.00	$0.30	$0.70 (70%)	$9.13

Full re-scrape monthly cost at the same cadence: $30.07. First month with incremental costs $2.59 / $5.49 / $9.83 for the 5% / 15% / 30% scenarios because the first run builds baseline state at full cost before incremental savings apply.

Platform usage is included in the per-result fee shown above.

FAQ

How many results can I get from tennisexplorer.com?

The number of results depends on the search query and available matches on tennisexplorer.com. Use the maxResults parameter to control how many results are returned per run.

Does Tennisexplorer Scraper support recurring monitoring?

Yes. Enable incremental mode to only receive new or changed matches on subsequent runs. This is ideal for scheduled monitoring where you want to track changes over time without re-processing the full dataset.

Can I integrate Tennisexplorer Scraper with other apps?

Yes. Tennisexplorer Scraper works with Apify's integrations to connect with tools like Zapier, Make, Google Sheets, Slack, and more. You can also use webhooks to trigger actions when a run completes.

Can I use Tennisexplorer Scraper with the Apify API?

Yes. You can start runs, manage inputs, and retrieve results programmatically through the Apify API. Client libraries are available for JavaScript, Python, and other languages.

Can I use Tennisexplorer Scraper through an MCP Server?

Yes. Apify provides an MCP Server that lets AI assistants and agents call this actor directly. Use compact mode, a single descriptionFormat, and excludeEmptyFields to keep payloads manageable for LLM context windows.

Is it legal to scrape tennisexplorer.com?

This actor extracts publicly available data from tennisexplorer.com. Web scraping of public information is generally considered legal, but you should always review the target site's terms of service and ensure your use case complies with applicable laws and regulations, including GDPR where relevant.

Your feedback

If you have questions, need a feature, or found a bug, please open an issue on the actor's page in Apify Console. Your feedback helps us improve.

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Getting started with Apify

New to Apify? Create a free account with $5 credit — no credit card required.

1. Sign up — $5 platform credit included
2. Open this actor and configure your input
3. Click Start — export results as JSON, CSV, or Excel

Need more later? See Apify pricing.

Disclaimer

This actor accesses only publicly available data on tennisexplorer.com. You are responsible for how you use the extracted data — in particular any personal information such as names, phone numbers, or email addresses — and for complying with Tennisexplorer's terms of use, applicable data-protection law (including the GDPR where it applies), and the anti-spam rules of your jurisdiction.

This actor is not affiliated with, endorsed by, or connected to Tennisexplorer.

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