Tennis Odds Scraper | Results & Closing Odds, $3/1K

What is Tennis Odds Scraper?

Get tennis results and upcoming matches with opening and closing odds from 10-15 bookmakers, scores, round, surface and implied probabilities for $3.00 per 1,000 matches, no login.

For bettors, modellers and odds analysts. ATP, WTA, Challenger and ITF singles and doubles, stable match IDs across runs, and a free only-new mode for hourly or daily feeds.

▶ Watch the 75-second demo

Source	TennisExplorer public match lists and match pages, 400-700 matches a day
Modes	Finished results (days back or a date range) or upcoming matches
Filters	Tour level, singles/doubles, tournaments, players, only priced matches
Output	47 fields per match; JSON, CSV, Excel, API
Automate	Only new since last run, Slack/webhook digest, schedules, MCP
Price	$3.00 per 1,000 matches ($2.50 per 1,000 matches on paid Apify plans)
Login needed	No

Daily monitor: only what's new, every morning

Set it once, get only the new matches every morning. With Only new since last run on, the first run returns everything that matches; each later run skips matches that earlier runs already returned, so you pay only for new ones.

{
  "mode": "results",
  "daysBack": 1,
  "categories": [
    "atp",
    "wta"
  ],
  "matchType": "singles",
  "onlyWithOdds": true,
  "incremental": true,
  "stateKey": "atp-wta-results"
}

1. Paste this input into the Input tab (JSON view) and click Save as a new task; it appears under Saved tasks.
2. Open the task from Saved tasks and click Schedule.
3. Choose Daily, pick the hour, and save.

Cost example: ATP and WTA singles, about 60 newly finished matches a day ≈ $5.55/month on the free plan ($4.65 on paid plans): one run a day for 30 days, start fee included. The first run returns everything that matches, so it costs more.

Add a Slack incoming-webhook URL in slackWebhookUrl (or any URL in webhookUrl) to get a short digest of each run's new matches; nothing is sent when a run finds nothing.

Why Tennis Odds Scraper users pick this one

• Odds per bookmaker. Opening and closing prices from 10-15 bookmakers, each with its time.
• Results joined to odds. Score by set, winner, retirements and walkovers in the same row.
• 62% under the leading Actor. $3.00 per 1,000 matches against $8.
• Market probabilities. Implied win chances with the bookmaker margin removed.
• A feed, not a dump. Free only-new mode with a Slack or webhook digest.

Get started in 3 steps

1. Pick 'Results' (finished matches: days back or a date range) or 'Upcoming' (today plus N days); match pages with per-bookmaker opening and closing odds are on by default.
2. Optionally narrow it to ATP/WTA/Challenger/ITF levels, singles or doubles, tournaments or players, and turn on 'Only new since last run' for a feed.
3. Click Start, then download JSON, CSV or Excel, or schedule it hourly or daily and send new matches to Slack, Google Sheets, Zapier, Make or n8n.

▶ Try it with the example input

The form opens with an example input already filled in.

How much does it cost to scrape tennis odds?

Pay per event: you pay only for what the run returns, plus a small start fee. Bronze, Silver, Gold and higher Apify plans pay less per result.

Matches	Free plan	Paid plans (Bronze, Silver, Gold)
100	$0.31	$0.26
1,000	$3.01	$2.51
10,000	$30.01	$25.01

Totals include the $0.005 start fee per run, rounded to the cent. Apify's free plan includes $5 of credit a month, enough for about 1,600 matches.

Matches outside your filters, already returned with 'Only new since last run', or beyond 'Max matches' are not charged.

Billing events

Event	Free plan	Paid plans (Bronze, Silver, Gold)
match	$3.00 per 1,000 matches	$2.50 per 1,000 matches
apify-actor-start	$0.005 per run	$0.005 per run

Tennis Odds Scraper vs alternatives

	This Actor	crawlstone/tennis-scraper	extractify-labs/flashscore-tennis-matches
Price per 1,000 matches, free plan	$3	$8	$1
Price per 1,000, paid plans	$2.50	$6 to $7.36	$1
Start fee per run	$0.005	none	$0.00005
Bookmaker odds	10-15 bookmakers per match, opening and closing with times	One odds pair (SofaScore), live matches	None
Implied probabilities, margin removed	Yes	No	No
Results by date range	Yes, up to 92 days per run	By date (tournaments mode)	Recent days (day offsets)
Round, surface, rankings	Yes (match page)	Yes (tournament/player modes)	Yes
Only new since last run + Slack/webhook	Yes, free	No	No
Live scores, point-by-point	No	Yes	Live game score

Prices checked regularly on each Actor's Store page.

Benchmarks: how we compare

Run	Matches returned	With per-bookmaker odds	Run time
Prefilled form: results, 20 matches	20	19	11 s
Upcoming matches, 10 matches	10	10	8 s
Results, 3 days, match pages on, 1,000 cap	1,000 (1,003 page loads, 0 failed)	925	326 s
Upcoming ATP + WTA, only new, first run	48	not recorded	24 s
Same input, run again	0 new (48 skipped, not charged)	-	3 s
Worst empty run: 92-day range, player that matches nothing	0	-	114 s
Results, 2 days, all levels, match pages off (local)	1,175, every match_id unique	0 (1,041 with a price from the day list)	not timed

The first six rows are single runs on the Apify cloud at 256 MB memory. The last row is one local run used to check duplicates across two full days of all levels with match pages switched off. Each row is one run, so treat times as a guide, not a guarantee.

What data you get

Tennis Odds Scraper turns TennisExplorer's public daily match lists and match pages into one clean row per match: ATP, WTA, Challenger, ITF and UTR singles and doubles, with tournament, surface, round, players with seeds and rankings, start time in UTC, the result set by set (retirements and walkovers marked), and the Home/Away odds of every listed bookmaker with the opening and closing price and when each was set, plus averages, best prices and implied win probabilities with the bookmaker margin removed. Pull finished results for any date range, or upcoming matches with the latest prices, and turn on 'Only new since last run' for an hourly or daily feed with a Slack or webhook digest.

• match_id (stable across runs), date and start_time (UTC), status, tournament, category, match_type, round, surface
• Both players: short and full name, seed, singles ranking; winner, sets, score (e.g. 7-6(3) 6-4), set_scores
• Average opening and closing odds, best closing odds, implied_prob_p1 / p2 (margin removed), overround
• bookmaker_odds: opening and closing price per bookmaker with the time each was set

player1	player2	score	closing_avg_p1	closing_avg_p2	implied_prob_p1	tournament	round	surface	bookmakers_count
Fils A.	Machac T.	6-3 7-6(4)	1.553	2.427	0.6098	Paris	2. round	indoors	15
Struff J.	Tirante T.	6-7(5) 6-3 7-6(3)	1.42	2.838	0.6665	Paris	2. round	indoors	15
Alcaraz C.	Michelsen A.	7-6(4) 6-3	1.167	5.023	0.8115	Paris	2. round	indoors	15
Berrettini M.	Humbert U.	7-6(3) 7-5	2.033	1.769	0.4653	Paris	2. round	indoors	15
Borges N.	Khachanov K.	6-3 6-4	2.696	1.457	0.3508	Paris	2. round	indoors	15
Fritz T.	Molcan A.	6-7(6) 7-5 7-6(5)	1.229	4.151	0.7716	Paris	2. round	indoors	15

One full match record, exactly as a real run returned it (long text and lists shortened here):

{
  "match_id": "te-3368391",
  "source_match_id": "3368391",
  "match_url": "https://www.tennisexplorer.com/match-detail/?id=3368391",
  "title": "Fils A. vs Machac T.",
  "date": "2026-11-04",
  "start_time": "2026-11-04T20:20:00Z",
  "tournament": "Paris",
  "tournament_url": "https://www.tennisexplorer.com/paris/2026/atp-men/",
  "tournament_country": "FR",
  "category": "atp",
  "gender": "men",
  "match_type": "singles",
  "round": "2. round",
  "surface": "indoors",
  "status": "finished",
  "player1": "Fils A.",
  "player1_full_name": "Fils Arthur",
  "player1_url": "https://www.tennisexplorer.com/player/fils/",
  "player1_seed": "6",
  "player1_ranking": 7,
  "player2": "Machac T.",
  "player2_full_name": "Machac Tomas",
  "player2_url": "https://www.tennisexplorer.com/player/machac/",
  "player2_seed": null,
  "player2_ranking": 69,
  "winner": "player1",
  "winner_name": "Fils A.",
  "sets_p1": 2,
  "sets_p2": 0,
  "score": "6-3 7-6(4)",
  "set_scores": [
    {
      "p1": 6,
      "p2": 3,
      "tiebreak": null
    },
    {
      "p1": 7,
      "p2": 6,
      "tiebreak": 4
    }
  ],
  "odds_p1": 1.55,
  "odds_p2": 2.43,
  "implied_prob_p1": 0.6098,
  "implied_prob_p2": 0.3902,
  "overround": 0.0559,
  "opening_avg_p1": 1.429,
  "opening_avg_p2": 2.74,
  "closing_avg_p1": 1.553,
  "closing_avg_p2": 2.427,
  "closing_best_p1": 1.59,
  "closing_best_p2": 2.5,
  "bookmakers_count": 15,
  "bookmaker_odds": [
    {
      "bookmaker": "10Bet",
      "opening_p1": 1.42,
      "opening_p2": 2.7,
      "opening_at": "2026-11-02T18:37:00Z",
      "closing_p1": 1.53,
      "closing_p2": 2.4,
      "closing_at": "2026-11-04T14:23:00Z",
      "changes": 20
    },
    {
      "bookmaker": "1xBet",
      "opening_p1": 1.43,
      "opening_p2": 2.92,
      "opening_at": "2026-11-02T18:26:00Z",
      "closing_p1": 1.59,
      "closing_p2": 2.4,
      "closing_at": "2026-11-04T20:07:00Z",
      "changes": 30
    },
    {
      "bookmaker": "bet-at-home",
      "opening_p1": 1.4,
      "opening_p2": 2.85,
      "opening_at": "2026-11-02T18:38:00Z",
      "closing_p1": 1.55,
      "closing_p2": 2.45,
      "closing_at": "2026-11-04T19:52:00Z",
      "changes": 30
    },
    "... [12 more]"
  ],
  "odds_source": "bookmakers",
  "source_url": "https://www.tennisexplorer.com/match-detail/?id=3368391",
  "scraped_at": "2026-11-05T00:02:04Z"
}

Use cases

• Bettors and tipsters measuring closing-line value: compare the price you took with the closing average
• Modellers backtesting Elo or serve/return models against market-implied probabilities
• Odds and trading analysts tracking line movement from opening to close across 10-15 bookmakers
• Media, apps and newsletters publishing daily results and tomorrow's order of play with prices

Use-case recipes

Closing-line value check for bettors

For bettors and tipsters who want to compare the price they took with the market close. Run Results mode for yesterday and today at ATP and WTA level, singles, priced matches only. Join your bet log on player names or match_id, then compare your price with closing_avg_p1 / closing_avg_p2 and closing_best_p1 / closing_best_p2; the per-bookmaker closing price is in bookmaker_odds.

{
  "mode": "results",
  "daysBack": 1,
  "categories": [
    "atp",
    "wta"
  ],
  "matchType": "singles",
  "onlyWithOdds": true,
  "maxResults": 200
}

Tomorrow's order of play with prices

For newsletters, apps and tipping sites. Run Upcoming mode for today and tomorrow, priced matches only, with 'Only new since last run' on and schedule it every few hours. Each run returns only newly scheduled matches; publish start_time (UTC), tournament, round, player1 and player2 with odds_p1, odds_p2 and implied_prob_p1. Add a Slack webhook to get a digest of the top 10.

{
  "mode": "upcoming",
  "daysAhead": 1,
  "categories": [
    "atp",
    "wta",
    "challenger"
  ],
  "onlyWithOdds": true,
  "incremental": true,
  "maxResults": 150
}

Backtest a model against the market

For modellers testing Elo or serve/return models. Pull a month of finished ATP singles with odds, then compare your model's win chance with implied_prob_p1 (closing averages, margin removed) and score it on winner. Raise maxResults once the sample looks right, and run one range of up to 92 days per run for older periods. Rankings are the players' current ones, not as of the match, so do not use them as model inputs for old matches.

{
  "mode": "results",
  "daysBack": 30,
  "categories": [
    "atp"
  ],
  "matchType": "singles",
  "onlyWithOdds": true,
  "maxResults": 300
}

Player watchlist feed without match pages

For fans, agents and analysts following a few players. List them in players (surname matching), turn match pages off for a fast run that reads one day list per day, turn on 'Only new since last run' and schedule it hourly. Each new result arrives with score, set_scores, winner_name and the day-list price in odds_p1 / odds_p2; set slackWebhookUrl or webhookUrl to forward it.

{
  "mode": "results",
  "daysBack": 1,
  "players": [
    "Jannik Sinner",
    "Swiatek"
  ],
  "bookmakerOdds": false,
  "incremental": true,
  "maxResults": 50
}

Input

Field	Type	What it does	Default
mode (What to get)	string	'results' = finished matches with scores and closing odds for past days; 'upcoming' = matches not started yet with the...	"results"
maxResults (Max matches)	integer	Stop after this many matches.	500
daysBack (Results: days back)	integer	Results mode: finished matches from today (UTC) and the N days before.	1
daysAhead (Upcoming: days ahead)	integer	Upcoming mode: matches scheduled today (UTC) and on the next N days.	1
categories (Tours and levels)	array	Keep only these levels.
matchType (Singles or doubles)	string	Example: singles.	"all"

All 16 fields, with examples: see the Input tab.

Switching from crawlstone/tennis-scraper

Already running crawlstone/tennis-scraper? It is built around live matches and SofaScore IDs; this Actor is built around finished and upcoming matches with bookmaker odds. Map its input fields like this:

crawlstone/tennis-scraper input	This Actor's input	Notes
mode	mode	Theirs has liveMatches, tournaments, point-by-point, players and rankings modes. Ours has results (finished matches) and upcoming. There is no live mode: matches in progress are skipped until they finish.
maxMatches	maxResults	Theirs caps live matches only and 0 means all. Ours caps every run, from 1 to 50,000, default 500.
matchType	matchType	Theirs is a list; ours is one value: all, singles or doubles.
tournamentLevel	categories	Levels: atp, wta, challenger, itf-men, itf-women, utr, other. Empty = all.
gender	categories	Pick the men's levels (atp, challenger, itf-men) or the women's (wta, itf-women). Each row also carries a gender field.
date	daysBack, or dateFrom + dateTo	Theirs finds tournaments on one date. Ours returns the matches of today and N days back, or of a date range of up to 92 days.
tournamentId	tournaments	Words from the tournament name, e.g. Paris, not numeric IDs.
playerId	players	Player names, matched on the surname. You get that player's matches, not a player profile.
matchId (point-by-point)	no equivalent	No point-by-point or live scores.
ranking	no equivalent	No ranking tables. Each match row carries both players' current singles ranking from the match page.

• Match IDs change: ours are TennisExplorer match numbers (te-3368391), the same in every run and mode, so joins between upcoming and results runs use match_id.
• One row per match holds the odds of every listed bookmaker in bookmaker_odds (opening and closing price with times), plus averages, best prices and implied probabilities. The comparison table above lists one odds pair for theirs.
• Pricing model: a flat price per match returned plus a small start fee per run; matches outside your filters, beyond Max matches, or already returned in only-new mode are not charged.
• Need live scores or point-by-point? Keep theirs for that and use this Actor for results, upcoming matches and odds.

Use with AI agents (Apify MCP server, Claude, ChatGPT, n8n AI agent)

Tennis Odds Scraper is a ready-made tool for AI agents. Through the Apify MCP server an agent reads this Actor's input schema, fills in the input from your request, starts the run and reads the matches back. You pay the same per-result price as a run started by hand, from your own Apify account.

Example prompts

• "Get yesterday's finished ATP and WTA singles matches with the closing odds of every bookmaker and the implied win probabilities, as a table"
• "List today's and tomorrow's ATP and Challenger matches that bookmakers price, with start time in UTC, round, surface and the best odds for each player"
• "Pull every finished ATP singles match of the last 30 days with opening and closing average odds and tell me how often the closing favourite won"

Claude (claude.ai and Claude Desktop)

Open Settings > Connectors, choose Add custom connector, paste https://mcp.apify.com/?tools=aurelix/tennis-odds-scraper and sign in to Apify when asked. Then ask Claude one of the prompts above.

Claude Code, Cursor, VS Code and other MCP clients

$claude mcp add --transport http apify "https://mcp.apify.com/?tools=aurelix/tennis-odds-scraper"

Clients configured with JSON take the same URL:

{
  "mcpServers": {
    "apify": {
      "url": "https://mcp.apify.com/?tools=aurelix/tennis-odds-scraper"
    }
  }
}

A client without Apify sign-in sends the header Authorization: Bearer <YOUR_APIFY_TOKEN>; to run the server locally instead, use npx -y @apify/actors-mcp-server --actors aurelix/tennis-odds-scraper with APIFY_TOKEN set.

ChatGPT

Where your ChatGPT plan offers custom connectors (developer mode), add https://mcp.apify.com/?tools=aurelix/tennis-odds-scraper as a connector, sign in to Apify, and ask for the data in plain words.

n8n AI agent

Give an AI Agent node an MCP Client Tool with the endpoint https://mcp.apify.com/?tools=aurelix/tennis-odds-scraper and a Bearer credential holding your Apify token. The agent can then call Tennis Odds Scraper whenever a workflow step needs matches; for a fixed, non-agent workflow use Apify's n8n node with Run Actor instead.

Use it from AI agents (MCP), the API or no-code

Copy this to your AI assistant

Use the Apify Actor aurelix/tennis-odds-scraper (Tennis Odds Scraper) through Apify's MCP server or API. Main input: mode (What to get); maxResults (Max matches). Example input: {"mode": "results", "dateFrom": "2026-10-01", "dateTo": "2026-10-31", "categories": ["atp", "wta"], "matchType": "singles", "onlyWithOdds": true, "maxResults": 5000}. Each result is one match with fields such as match_id, source_match_id, match_url, title, date, start_time, tournament, tournament_url. It costs $3.00 per 1,000 matches on Apify's free plan plus a $0.005 start fee per run, and needs no login.

MCP server URL for this Actor: https://mcp.apify.com/?tools=aurelix/tennis-odds-scraper (setup for each client in the section above).

API

Start a run and read the dataset with Apify's REST API or the Python and JavaScript clients:

from apify_client import ApifyClient

client = ApifyClient("<YOUR_APIFY_TOKEN>")
run = client.actor("aurelix/tennis-odds-scraper").call(run_input={'mode': 'results', 'dateFrom': '2026-10-01', 'dateTo': '2026-10-31', 'categories': ['atp', 'wta'], 'matchType': 'singles', 'onlyWithOdds': True, 'maxResults': 5000})
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

No-code: schedule it in 3 clicks

Send the results to Google Sheets, Zapier, Make, n8n, Slack or any webhook URL.

1. Fill in the input, then click Save as a new task.
2. In the task, click Schedule and pick daily or weekly (Apify Schedules).
3. Turn on Only new since last run so each run returns only what is new, and paste a Slack or webhook URL into Slack webhook URL / Webhook URL to get a digest of the new items (count plus the top 10) after every run.

Prefer n8n or Make? Ask in the Issues tab and we will share a ready-made workflow for it.

FAQ

Can I scrape tennis odds without login?

Yes. It never logs in, so you need no TennisExplorer account, password or cookies; only a free Apify account to run it.

Is there a tennis odds API?

You can use this Actor as one. Start it with Apify's REST API or the Python and JavaScript clients and read the results as JSON (see the code above), or call it from an AI agent through MCP.

How much does it cost to scrape tennis odds?

About $3.01 for 1,000 matches on Apify's free plan and $2.51 on paid plans, start fee included. The free $5 monthly credit covers about 1,600 matches.

Can I export tennis odds data to Excel or Google Sheets?

Yes. Download the results as Excel, CSV, JSON, XML or HTML from the run's Storage tab, or send them to Google Sheets with Apify's Google Sheets integration or the API.

Is it legal to scrape tennis odds?

It reads only public data any logged-out visitor can see: TennisExplorer's public match lists and match pages, on paths its robots.txt allows, at a polite pace with a declared bot user-agent. It never logs in, never solves CAPTCHAs and never visits bookmaker websites. Match results and odds are facts about public sporting events; no personal data beyond players' public names, seeds and rankings. To have a record removed, open an issue on this Actor's Issues tab; if TennisExplorer formally asks us to stop, this Actor will stop. You are responsible for using the data lawfully, incl. gambling and data-reuse laws where you are; this is not legal advice.

Which bookmakers are included?

Those TennisExplorer lists for the match, typically 10 to 15 at ATP/WTA and Challenger level (bet365, Pinnacle, Betfair, William Hill, Unibet, bwin and more). Many ITF and UTR matches have few or none; 'Only matches with odds' skips those.

What exactly are opening and closing odds?

Per bookmaker, opening is the oldest price in its change history on the match page and closing its last price before the start (current price for upcoming matches), each with its UTC time.

How do I get a live-ish feed?

Turn on 'Only new since last run' and schedule Results mode (days back 1) hourly: each run returns only matches finished since the last one. Add a Slack or webhook URL for a digest.

What happens if TennisExplorer slows down or a day does not load?

After 6 match pages in a row fail, the run stops opening match pages and returns the remaining matches from the day list only: score, players and the day-list price, without round, surface, rankings or per-bookmaker odds. After 3 day lists in a row fail, the run stops. Matches already read are kept, the status message says how many came without a match page and which days did not load, and you pay only for matches written to the dataset.

What am I charged for a run that returns nothing?

Only the small per-run start fee. Matches are charged one by one as they are written; a run with no matching matches, or a repeat run in only-new mode with nothing new, writes nothing and pays no match charges.

How does 'Only new since last run' decide what is new?

It keeps a memory of returned matches per mode and filter set (or per 'State key' if you name one). In Results mode each finished match is returned once. In Upcoming mode each scheduled match is returned once, so later odds moves on that match are not sent again; run Results mode later for the closing odds. Skipped matches are not charged, and matches unseen for 120 days drop out of the memory.

Why are round, surface, full names or rankings empty on some rows?

They come from the match page. They are empty when 'Bookmaker odds' (match pages) is switched off, or when that match page did not load. Rankings are singles rankings only, and they are the players' current rankings, not as of the match date.

What do odds_p1, closing_avg_p1 and odds_source mean?

odds_p1 / odds_p2 are the prices shown on the day list. closing_avg and opening_avg are the means of each bookmaker's last and first price; closing_best is the highest closing price across bookmakers. implied_prob_p1 / p2 come from the closing averages when the match page was read, otherwise from the day-list prices, with the margin (overround) removed. odds_source is 'bookmakers', 'list', or empty when no price was shown.

How far back can one run go?

Results mode reads today and up to 92 days back, or a date range of at most 92 days. A longer range is refused with a clear message; split it into several runs. A 'To date' in the future is cut to today.

Which day does a match belong to, and are times UTC?

Yes. Pages are read with the site's public time-zone setting at UTC, so date is the UTC day of the list and start_time is UTC. Odds history times are converted from the site's Prague time to UTC.

How does the players filter match names?

On the last word of what you type (the surname), ignoring case and accents, against both players. 'Jannik Sinner' and 'Sinner' both match any player named Sinner on the list.

How many matches does a day have, and how long does a run take?

A busy day lists 400 to 700 matches across all levels, about 60-100 of them at ATP/WTA level. With match pages on, a run reads about 3 pages per second: 1,000 matches took 326 seconds in our test run. With match pages off it reads one page per day.

Limits (stated honestly)

• One source: TennisExplorer. Matches, bookmakers and odds are as it shows them; Home/Away (match winner) odds only, no handicaps, totals or in-play odds.
• No live scores; matches in progress are skipped until they finish.
• Round, surface, full names and rankings need match pages (on by default); rankings are current, not as of the match date.
• UTC days; a date range covers at most 92 days per run; about 3 match pages per second (1,000 matches in 5-8 minutes).

Works great with

• Then: Google Trends by Region: put player full names or tournament names into its keywords input to compare search interest around a match

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• Google Maps Phone Scraper: Local business lists from Google Maps: phones, websites, ratings, hours, coordinates; optional website emails and socials.
• Recently Funded Companies API: US companies that just filed an SEC Form D: amount raised, industry, state, executives, confirmed website.
• Tech Stack API: CMS, e-commerce platform, analytics, ad pixels, chat, CDN and email provider for each domain you enter.

Ad intelligence

• Competitor Watch: One report per competitor with only what is new: Meta and TikTok ads, page changes, Shopify products and prices, jobs.
• Facebook Ad Library Search & API: Facebook and Instagram business ads from the Meta Ad Library: copy, creatives, landing pages, dates, optional EU reach.

Canada and government data

• Canada Building Permits: Building permits and development applications from 7 Canadian cities: address, type, status, dates, value, contractor company.

AI, content and SEO

• Google Trends by Region: Google Trends interest over time, by region and rising queries for any number of keywords, plus trending searches.
• Google Autocomplete API: Expands seed keywords into hundreds of real Google and YouTube autocomplete phrases, tagged by search intent.
• PDF to Markdown OCR & Table Extraction: Turns linked PDF and Word files into Markdown per page, with tables as JSON rows, RAG chunks and OCR.
• Broken Link Crawler & SEO Audit: Audits a whole site: one row per page with SEO checks and broken links, plus a ranked issues summary.
• Website to Markdown API: Crawls a site and returns one row of clean main-content Markdown per page, with title, headings and links.

Recent changes

• 0.1: Faster long ranges: the next day lists are fetched ahead while the current day is processed, within the same polite request rate.

The full history is on the Changelog tab.

Tested daily

An automated health check runs this Actor every day on a fixed input and checks that at least 8 matches come back with match_id, match_url, date, tournament, player1, player2 filled in; a failed check alerts the maintainer straight away.

Your feedback

Missing a field, found a bug or need another filter? Open an issue on the Issues tab; we aim to answer within a day. If this Actor saved you time, a short review helps other people find it.