Tennis Point-by-Point Scraper — Rally & Match Stats 🎾

🎾 The only scraper that returns point by point tennis data — every serve, rally, and break point, plus the full tennis match statistics table

Built for analysts, traders, scouts, and developers who need real tennis data, not headlines

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📋 Overview

Serve, rally, break, repeat. Every other tennis scraper hands you a scoreline. This one hands you the match: the player directory, match metadata, every point of every game in play order, and the complete match- and set-level statistics table — as clean, structured rows you can analyze in seconds.

Why analysts and developers choose us:

• 🥇 Rally-level data nobody else sells: no other tennis scraper on the Store returns point-by-point sequences at any price
• ⚡ Two clicks to data: find a player, then pull their matches — no API key, no login
• 🎯 Point-by-point depth: every serve, rally, and break point, in order, ready for modeling
• 🛡️ Controlled bulk retrieval: explicit match limits, per-match failure isolation, and resumable two-stage jobs
• 💎 Full statistics table: serve, return, break points, plus performance and shot-quality ratings, at match and set level
• 🎾 Singles-only mode: filter out doubles before parsing — and never pay for the rows you filtered
• 🌐 Proxies included: nothing to configure, nothing extra to pay — we route every request through our own residential network
• ✨ Export anywhere: JSON, CSV, or Excel, or straight from the API

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✨ Features

• 🔎 Player Directory Search: find any player by name and get a stable ID
• 🎾 Point-by-Point Data: sets → games → points, in play order
• 📊 Match & Set Statistics: serve/return/break-point rows plus shot-quality ratings
• 🏟️ Rich Match Metadata: tournament, location, date, and both players
• 👥 Multi-Player Runs: scrape many players at once — shared matches are fetched only once
• 🧭 Two-Stage Actor Operations: discover unique match IDs first, then scrape bounded detail batches
• 🔌 Stable Retrieval Contract: production clients can plan, deduplicate, and resume outside the Actor
• 🎛️ Toggle the Payload: include or skip points/statistics per run
• 📁 Clean Structured Output: one tidy dataset per run, ready for analysis
• 🔑 No API Key Required: just add input and run

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🎬 Quick Start

Scrape a player's matches — points, rallies and statistics — in one call.

curl -X POST "https://api.apify.com/v2/acts/sian.agency~tennis-point-by-point-scraper/runs?token=YOUR_TOKEN" \
-H 'Content-Type: application/json' \
-d '{"operation": "scrape-matches", "playerIds": ["feca401d-e9c2-4638-93b0-878fdb9aea74"], "maxMatchesPerPlayer": 5}'

Don't have a player ID yet? Look one up first:

curl -X POST "https://api.apify.com/v2/acts/sian.agency~tennis-point-by-point-scraper/runs?token=YOUR_TOKEN" \
-H 'Content-Type: application/json' \
-d '{"operation": "discover-players", "search": "Dinev"}'

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🚀 Getting Started (3 Simple Steps)

Step 1: Hit Start

scrape-matches is the default operation and a player is already prefilled, so a first run returns real matches with full point-by-point data for a few cents. No setup at all.

Step 2: Point it at your own players

Switch to discover-players, search a name, and copy the playerId from the results into playerIds. Then switch back to scrape-matches.

⚠️ Leaving search empty returns the entire directory — about 2,600 players, billed per row. It is capped at maxPlayers (default 100), but keep a name in there unless you deliberately want a big slice.

Step 3: Export your data

Download the dataset as JSON, CSV, or Excel — or pull it straight from the Apify API.

That's it! In minutes, you'll have:

• A searchable directory of players with stable IDs
• Full match records with point-by-point data
• Match and set statistics ready for analysis

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📥 Input Configuration

Field	Type	Required	Description
operation	string	Yes	discover-players, discover-matches, scrape-matches, or scrape-match-ids
search	string	No	Filter the player directory by name or ID (discover only)
playerIds	array	No	Player IDs for discover-matches or scrape-matches
matchRequests	array	No	Up to 250 {matchId, requestedPlayerIds} objects for scrape-match-ids
maxMatchesPerPlayer	integer	No	Matches per player; default 5, 0 = all
maxTotalMatches	integer	No	Hard ceiling on billed matches per run after deduplication; default 20
includePoints	boolean	No	Include nested point-by-point data (default true)
includeStats	boolean	No	Include match and set statistics (default true)
matchFormat	string	No	all (default) or singles; singles mode excludes doubles before point/stat parsing

Find players:

{
  "operation": "discover-players",
  "search": "Dinev"
}

Scrape matches:

{
  "operation": "scrape-matches",
  "playerIds": ["feca401d-e9c2-4638-93b0-878fdb9aea74"],
  "maxMatchesPerPlayer": 10,
  "maxTotalMatches": 50,
  "matchFormat": "singles",
  "includePoints": true,
  "includeStats": true
}

Build a globally deduplicated match plan:

{
  "operation": "discover-matches",
  "playerIds": ["feca401d-e9c2-4638-93b0-878fdb9aea74"],
  "maxMatchesPerPlayer": 25,
  "maxTotalMatches": 5000
}

Scrape explicit match IDs:

{
  "operation": "scrape-match-ids",
  "matchRequests": [
    {
      "matchId": "c292a56a-5fbe-464e-a768-0fc26f483be2",
      "requestedPlayerIds": ["feca401d-e9c2-4638-93b0-878fdb9aea74"]
    }
  ],
  "matchFormat": "singles"
}

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📤 Output

Results are saved to the Apify dataset. Runs return player, match-reference, match, or match-exclusion records. In singles mode, doubles produce lightweight exclusions after metadata classification and before point/stat parsing. Unknown formats are fully scraped to avoid accidental data loss. Structured per-item failures are stored in the run's FAILURES key-value record.

Field	Type	Description
recordType	string	player, match-reference, match, or match-exclusion
playerId / fullName	string	Player ID and name (player records)
requestedPlayerIds	array	Requested players that led to a match reference or record
matchId / title	string	Match ID and title (match records)
matchFormat	string	singles, doubles, or unknown
exclusionReason	string	Why a match-exclusion record was not fully parsed
tournament / location	string	Event name and place
completedAt	string	When the match finished
pointCount	number	Total points in the match
playerIds	array	Both players resolved from point data
sets	array	Nested sets → games → points
stats	array	Match- and set-level statistics rows

Example (match record — real output, trimmed):

{
  "recordType": "match",
  "matchId": "313b5434-cffc-4938-804c-e6edeec28e8e",
  "title": "Ivanov vs Dinev | 2026#3007#QS008 | Tennis Results",
  "tournament": "LEXUS NOTTINGHAM CHALLENGER 3",
  "matchFormat": "singles",
  "pointCount": 146,
  "playerIds": ["7626aa33-...", "feca401d-..."],
  "sets": [
    { "setNumber": 1, "games": [{ "gameNumber": 1, "points": [
      {
        "pointNumber": 1,
        "setNumber": 1,
        "gameNumber": 1,
        "winner": "teamA",
        "teamServing": "teamA",
        "playerServing": "7626aa33-7f05-436e-a24a-282a666310f8",
        "playerReceiving": "feca401d-e9c2-4638-93b0-878fdb9aea74",
        "rally": [
          { "sequence": 1, "type": "SERVE" },
          { "sequence": 2, "type": "ACE" },
          { "sequence": 3, "type": "POINT_COMPLETE" }
        ],
        "pointBreakdown": {
          "outcome": "winner", "serve": "ACE", "totalShots": "1",
          "winningShotType": "serve", "errorType": null, "situation": "serving"
        },
        "ace": true, "isBreakPoint": false, "isDeuce": false,
        "streak": { "owner": "teamA", "count": 1 },
        "time": "2026-07-06T13:27:53.474Z"
      }
    ] }] }
  ],
  "stats": [
    { "scope": "match", "category": "Points", "metric": "Total Points Won", "teamAValue": "81", "teamBValue": "65" },
    { "scope": "set", "setNumber": "1", "category": "Service", "metric": "Break Points Saved", "teamAValue": "4", "teamBValue": "3" }
  ]
}

That single match carried 146 points across 3 sets and 84 statistic rows — one billed record.

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💼 Use Cases & Examples

1. Tennis Analytics & Modeling

Data scientists building serve/return and momentum models. Input: Player IDs of the players you follow. Output: Point-by-point sequences + statistics. Use: Train win-probability and momentum models on real rally data.

2. Betting & Trading Research

Traders backtesting tennis markets. Input: A roster of players. Output: Historical match results with detailed in-match stats. Use: Validate edges and study live-trading spots.

3. Player Scouting & Coaching

Coaches and scouts preparing for opponents. Input: A player's ID. Output: Full match history with serve/return breakdowns. Use: Build opponent scouting reports and head-to-head views.

4. Sports Media & Content

Journalists and creators covering tournaments. Input: Search a player by name. Output: Match metadata + stats. Use: Fact-check stories and generate match recaps.

5. Data Products & Feeds

Developers powering apps and dashboards. Input: Scheduled runs over a player list. Output: Clean JSON match records. Use: Feed a live tennis stats product without maintaining a scraper.

6. Fantasy & Community Tools

Fantasy platforms and stat communities. Input: A directory of players. Output: Match and point data. Use: Power fantasy scoring and leaderboards.

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🔗 Integration Examples

JavaScript/Node.js

import { ApifyClient } from 'apify-client';
const client = new ApifyClient({ token: 'YOUR_TOKEN' });

const run = await client.actor('sian.agency/tennis-point-by-point-scraper').call({
  operation: 'scrape-matches',
  playerIds: ['feca401d-e9c2-4638-93b0-878fdb9aea74'],
  maxMatchesPerPlayer: 10
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items[0]);

Python

from apify_client import ApifyClient
client = ApifyClient('YOUR_TOKEN')

run = client.actor('sian.agency/tennis-point-by-point-scraper').call(
    run_input={
        'operation': 'scrape-matches',
        'playerIds': ['feca401d-e9c2-4638-93b0-878fdb9aea74'],
        'maxMatchesPerPlayer': 10,
    }
)

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

cURL

curl -X POST 'https://api.apify.com/v2/acts/sian.agency~tennis-point-by-point-scraper/runs?token=YOUR_TOKEN' \
-H 'Content-Type: application/json' \
-d '{"operation": "discover-players", "search": "Dinev"}'

Automation Workflows (N8N / Zapier / Make)

1. Trigger: Schedule or webhook
2. HTTP Request: Call the actor API
3. Process: Handle JSON results
4. Action: Save, notify, or transform

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📊 Performance & Pricing

You pay per record, and only for records you actually receive:

Event	What it is
Match record	One complete match — metadata, every point of every game, and the full match & set statistics table
Player record	One player-directory entry (ID, name, results link)

A match record is an entire match, not a scoreline: a typical singles match carries 100–200+ points plus dozens of statistic rows in a single billed row. Doubles excluded by matchFormat: "singles" and plan rows from discover-matches are never charged — you only pay for data you asked for and received.

Proxies are on us. There is nothing to configure and nothing extra to pay: every request goes through our own residential proxy network, included in the per-record price. You will not find a proxy field in the input — you never need one.

Your first run is cheap by design. The defaults scrape a small sample (5 matches per player, 20 total) so you can inspect the data shape for pennies before scaling up. There is no Free/Paid cap — raise maxTotalMatches up to 10,000 whenever you are ready. It is a hard ceiling: a run can never bill more matches than that number. Apify compute and storage are billed by Apify under the account that starts the run.

🏗️ Large-Scale Retrieval

For jobs bigger than one run, use the two-stage contract. Call discover-matches with your full player roster to get a globally deduplicated match plan — every unique match ID, each fetched only once no matter how many of your players played in it. Then submit those IDs to scrape-match-ids in bounded batches of up to 250.

Because the plan is stable and deduplicated, your own client can checkpoint it, resume after an interruption, and re-run only the match IDs it is still missing.

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❓ Frequently Asked Questions

Q: How do I get a player's matches? A: Run discover-players first to get the player's ID, then pass that ID to scrape-matches.

Q: How many matches can I scrape? A: Defaults are deliberately small — 5 per player, 20 per run — so a first run costs pennies. Raise maxTotalMatches up to 10,000 for a planned job, or split larger ones with discover-matches → scrape-match-ids.

Q: Am I charged for doubles I filtered out? A: No. In singles mode, doubles are classified from match metadata and skipped before any point or statistics parsing, and those exclusion rows are not billed.

Q: Can I get every point of a match? A: Yes — with includePoints enabled, each match returns its sets, games, and points in order.

Q: What statistics are included? A: Serve, return, and break-point stats plus performance and shot-quality ratings, at both match and set level.

Q: What output formats are available? A: JSON, JSONL, CSV, and Excel — export directly from the Apify dataset.

Q: Do I need an account or API key for the source? A: No. Just add your input and run the Actor.

Q: Do I need to set up a proxy? A: No — and there is no proxy field to fill in. Every request is routed through our own residential proxy network at no extra cost to you.

Q: Which players and tournaments are covered? A: The player directory carries thousands of players, and match coverage is strongest on ATP Challenger and ITF-level events. Not every player in the directory has matches on file — in a 31-player sample, 19 had match listings (up to 20 matches each). Run discover-players and then discover-matches to see exactly what is available for your roster before committing to a large scrape; discover-matches plan rows are not charged.

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🐛 Troubleshooting

No matches returned for a player

• Double-check the player ID from discover-players
• The player genuinely may have no matches on file — the directory lists more players than have match records. Use discover-matches (unbilled) to check a roster before scraping it.

Run stopped at the total-match limit

• Increase maxTotalMatches deliberately after reviewing the expected number of billed match records

Empty player search

• Broaden your search term, or leave it empty to return the full directory

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⚖️ Is it legal to scrape data?

Our actors are ethical and do not extract any private user data, such as email addresses, gender, or location. They only extract what the user has chosen to share publicly. We therefore believe that our actors, when used for ethical purposes by Apify users, are safe.

However, you should be aware that your results could contain personal data. Personal data is protected by the GDPR in the European Union and by other regulations around the world. You should not scrape personal data unless you have a legitimate reason to do so. If you're unsure whether your reason is legitimate, consult your lawyers.

You can also read Apify's blog post on the legality of web scraping.

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🤝 Support

Join our active support community

• For issues or questions, open an issue in the actor's repository
• Check SIÁN Agency Store for more automation tools
• 📧 apify@sian-agency.online

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