Tennis Scraper — Flashscore, SofaScore & TennisExplorer in One Actor

Turn three of the biggest tennis sites into one clean, structured dataset. Flashscore returns a whole day of matches in a single request. SofaScore carries the depth — 50+ per-match statistics, point-by-point, live scores, player bios and ATP/WTA rank history. TennisExplorer adds the thing neither of them sells: per-bookmaker odds history across four markets. Pick a source, pick a mode, or paste any URL. Built for betting models, tennis analytics, and sports media that need more than final scores.

Sources

Source	Best at	Cost per day of fixtures
Flashscore	Breadth — every tournament, every match, scores and set-by-set	1 request
SofaScore	Depth — statistics, point-by-point, odds line movement, rankings, player bios	~88 requests (bounded by sofaMaxTournaments)
TennisExplorer	Per-bookmaker odds history: opening price, every timestamped move, 4 markets	1 request per day

Set source to flashscore, sofascore, tennisexplorer, or all.

How it works

✨ Why use this scraper?

• Line movement nobody else sells — match details capture every bookmaker's opening price, every timestamped odds change with its delta, the current price and trend, across Home/Away, Over/Under, Asian Handicap and Correct Score markets.
• Three sites, one schema — Flashscore, SofaScore and TennisExplorer normalise into the same rows, so you can compare or merge them without writing glue code.
• Seven modes in one actor — fixtures, results, match details, player profiles, head-to-head, rankings and player search. No need to buy and wire up five different scrapers.
• No proxy needed — all three sources answer from a plain datacenter IP, so there is no residential bandwidth cost to pass on.
• Paste any URL — every tennisexplorer.com page type is auto-classified: match links, player pages, /mutual/ head-to-heads, ranking tables, day listings. Mix them freely in one run.
• Date ranges, both tours, singles and doubles — scrape a whole week of results in one run, filtered to ATP/WTA, singles/doubles, or everything.
• Historical depth — weekly ranking snapshots going back years, per-season win/loss balances by surface, career titles and injury history per player.
• Clean, model-ready rows — ISO dates, numeric odds, tiebreak points split from games, one billed row per match/player/ranking entry. No padding rows, ever.

🎯 Use cases

Who	What they do with it
Betting modelers & syndicates	Backtest markets with opening→closing line movement per bookmaker; track steam moves; join odds to results by matchId.
Tennis analytics teams	Build win-probability and surface-adjusted models from per-season balances, H2H records and set-level scores.
Sports media & content sites	Auto-generate match previews (H2H, form, odds) and recaps (scores, movers in the rankings).
Fantasy & prediction apps	Daily fixtures with odds as the slate; results feed for scoring; player bios for profiles.
Data products & researchers	Historical ranking snapshots and results archives as time series, exported to JSON/CSV/Excel.

📥 Supported inputs

Input	Example
Fixtures / day listings	https://www.tennisexplorer.com/matches/, /next/?year=2026&month=08&day=14
Results by day	https://www.tennisexplorer.com/results/?type=atp-single&year=2026&month=08&day=11
Match detail URLs or bare ids	https://www.tennisexplorer.com/match-detail/?id=3290771 or 3290771
Player profiles (slug or URL)	djokovic or https://www.tennisexplorer.com/player/djokovic/
Head-to-head pairs	djokovic vs alcaraz-5ab70, or https://www.tennisexplorer.com/mutual/djokovic/alcaraz-5ab70/
Rankings (with history)	https://www.tennisexplorer.com/ranking/atp-men/?page=2&date=2026-07-27
Player search	any name fragment, e.g. alcaraz

Not supported: pages behind a TennisExplorer login, live-score push updates (each run is a snapshot), and non-tennisexplorer.com domains.

🔄 How a run works

1. Choose a mode (or paste URLs — URLs win when both are present).
2. The actor expands your input into page fetches: date ranges become day pages, ranking runs walk ?page=N, ids become detail URLs.
3. Pages are fetched in parallel with browser-grade TLS (impit) and a three-stack fallback race for resilience.
4. Each page is parsed and normalised: paired match rows, odds tables with nested history, player boxes, ranking tables.
5. Rows are pushed to the dataset — one row per match, player, head-to-head pair, or ranking entry — up to your maxItems.

⚙️ Input parameters

Field	Type	Default	Notes
mode	select	fixtures	fixtures · results · matchDetails · players · h2h · rankings · searchPlayers
startUrls	array	[]	Any tennisexplorer.com URLs; overrides mode when present
tourType	select	all	all · atp-single · wta-single · atp-double · wta-double
dateFrom / dateTo	string	today / same day	YYYY-MM-DD; inclusive range, capped at 62 days
matchIds	array	[]	Numeric ids or /match-detail/ URLs
includeOddsHistory	boolean	true	Full timestamped movement per bookmaker on matchDetails rows
includeRecentForm	boolean	true	Last matches + mutual meetings on matchDetails rows
playerSlugs	array	[]	Slugs or full player URLs
h2hPairs	array	[]	a vs b, a,b or /mutual/ URLs
searchQuery	string	—	For searchPlayers
rankingTour	select	atp-men	atp-men · wta-women
rankingDate	string	latest	Historical Monday snapshot, YYYY-MM-DD
maxItems	integer	200	Hard cap on billed rows — your spend guard
maxConcurrency	integer	10	Parallel page fetches
proxy	object	none	Site works over a direct connection; configure only if you want one

📊 Output overview

Every mode writes one row per entity to the default dataset. Match rows (fixtures/results) carry both sides with players, seeds, per-set scores (tiebreaks split out), the two odds, the winner and a matchId you can feed straight into matchDetails. Match-detail rows add both players' bios, the structured final score, surface win/loss comparison, recent form, and the odds block: for each bookmaker and each of the four markets, the current price, trend, opening price and (optionally) the full timestamped history. Player rows carry the bio, current/highest rankings, per-season and per-surface balances, titles and injuries. Ranking rows are one player each, stamped with the snapshot date. Every row includes sourceUrl and scrapedAt.

📦 Output sample

A trimmed, real match-detail row (Montreal quarterfinal, scraped 2026-08-12):

{
    "type": "matchDetail",
    "matchId": "3290771",
    "matchUrl": "https://www.tennisexplorer.com/match-detail/?id=3290771",
    "title": "Jodar - Fils",
    "date": "2026-08-11",
    "time": "20:20",
    "tournament": "Montreal",
    "round": "quarterfinal",
    "surface": "hard",
    "tour": "atp",
    "home": { "name": "Jodar Rafael", "slug": "jodar", "ranking": 15, "birthdate": "2006-09-17", "plays": "right" },
    "away": { "name": "Fils Arthur", "slug": "fils", "ranking": 24, "birthdate": "2004-06-12", "plays": "right" },
    "result": {
        "raw": "2 : 0(7-65, 6-3)",
        "setsHome": 2,
        "setsAway": 0,
        "sets": [
            { "home": 7, "away": 6, "awayTiebreak": 5 },
            { "home": 6, "away": 3 }
        ]
    },
    "surfaceComparison": {
        "hard": { "home": { "wins": 21, "losses": 9 }, "away": { "wins": 30, "losses": 15 } },
        "clay": { "home": { "wins": 19, "losses": 4 }, "away": { "wins": 9, "losses": 2 } }
    },
    "odds": {
        "homeAway": [
            {
                "bookmaker": "10Bet",
                "line": null,
                "side1": {
                    "odds": 1.65,
                    "trend": "down",
                    "opening": 1.9,
                    "history": [
                        { "at": "11.08. 20:04", "odds": 1.65, "delta": -0.02 },
                        { "at": "09.08. 08:37", "odds": 1.9, "delta": null }
                    ]
                },
                "side2": { "odds": 2.2, "trend": "up", "opening": 1.85 }
            }
        ],
        "overUnder": [{ "bookmaker": "10Bet", "line": "2.5", "side1": { "odds": 2.2 }, "side2": { "odds": 1.61 } }],
        "asianHandicap": [],
        "correctScore": []
    },
    "recentForm": {
        "home": [
            {
                "tournament": "Montreal",
                "date": "2026-08-11",
                "matchup": "Jodar - Fils",
                "outcome": "win",
                "score": "2:0",
                "matchUrl": "https://www.tennisexplorer.com/match-detail/?id=3290771"
            }
        ]
    },
    "sourceUrl": "https://www.tennisexplorer.com/match-detail/?id=3290771",
    "scrapedAt": "2026-08-12T21:31:04.660Z"
}

(overUnder had 92 rows, asianHandicap 125 and correctScore 40 on this match — trimmed here. Arrays are [], never missing.)

🗂 Key output fields

Match rows (type: "match" — fixtures & results)

Field	Meaning
matchId, matchUrl	Feed these into matchDetails for the deep record
status	scheduled · live · finished
tournament, tour, discipline	e.g. Montreal, atp, singles
home / away	players[] (name, url, slug — two entries for doubles), seed, setsWon, setScores[] ({games, tiebreak?}), odds, h2hWins (fixtures)
winner	home · away · null
date, time	Day (mode runs) and local match time

Match-detail rows (type: "matchDetail") — adds round, surface, per-player bios (ranking, birthdate, heightCm, weightKg, plays, turnedPro), structured result.sets, surfaceComparison, recentForm (home/away/mutual), and odds with homeAway / overUnder / asianHandicap / correctScore — each row per bookmaker with line, side1/side2 = {odds, trend, opening, history[]}.

Player rows (type: "player") — name, country, heightCm, weightKg, age, birthdate, plays, rankSingles/rankDoubles ({current, highest}), careerBalance + seasonBalance[] (wins/losses total and per surface), titles[], titleCounts[], injuries[], recentTournaments[].

H2H rows (type: "h2h") — players[], official record (e.g. 5 : 6), bio comparison, mutualMatches[] with year, tournament, surface, round, winner/loser and set scores.

Ranking rows (type: "rankingEntry") — tour, snapshotDate, rank, move, player, country, points.

Search rows (type: "playerSearchResult") — query, tour, rank, player (with the slug you need for the other modes).

❓ FAQ

How do I get odds history for a match I found in fixtures? Take the matchId from the fixtures row and run matchDetails with it. Chaining fixtures → matchDetails each morning gives you a daily line-movement archive.

What do odds-history timestamps look like? As the site prints them: "11.08. 20:04" (day.month. hour:minute, no year). The opening price is extracted for you, so open→close deltas don't require parsing the history at all.

Can I get last year's rankings? Yes — set rankingDate to any Monday (e.g. 2025-06-02). TennisExplorer keeps weekly snapshots going back years; the row's snapshotDate confirms which week you got.

Does it cover doubles and Challenger/ITF events? Doubles: yes — use tourType: atp-double/wta-double; each side then carries two players. Coverage mirrors the site, which includes Challengers, ITF and exhibition events alongside the main tours.

Do I need a proxy? No. The site is served fine over a direct connection, which keeps your cost at compute + results only. If you prefer a proxy anyway, configure one in the input and it's honoured.

Why did a run return fewer rows than maxItems? maxItems is a cap, not a target — a quiet Tuesday simply has fewer matches. Failed pages never produce placeholder rows; gaps are reported in the run's status message instead.

💬 Support

Found a bug or missing a field? Open an issue on the actor's Issues tab in Apify Console — issues are answered within 1–2 business days. Feature requests are welcome: if TennisExplorer shows it, it can usually be added.

🛠 Additional services

Need a custom pipeline (scheduled line-movement archive, merged multi-source tennis feed, direct-to-database delivery) or a scraper for another sports/odds site? Contact me through the actor page — custom builds and SLAs available.

🔎 Explore more scrapers

More actors by the same developer: memo23 on Apify Store — flight prices, job boards, review platforms, real-estate portals and more, all pay-per-result.

🤖 For AI Agents & LLM Apps

This actor is MCP-friendly: modes map cleanly to tool calls (fixtures → "what's on today", matchDetails → "odds history for match X", h2h → "how do these two compare"). Outputs are stable, typed JSON with no HTML fragments, safe to feed straight into an LLM context or a function-calling loop. Use searchPlayers first to resolve names → slugs, then chain players/h2h. Keep maxItems low per call to control cost; every row is self-contained.

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⚠️ Disclaimer

This Actor is an independent tool and is not affiliated with, endorsed by, or sponsored by TennisExplorer (tennisexplorer.com) or any of its operators, nor by the ATP, WTA, ITF or any tournament, nor by any bookmaker whose odds appear in the data. All trademarks mentioned are the property of their respective owners.

The scraper accesses only publicly available pages — no authenticated endpoints, no paywalled content, and no live-score push feeds. Odds data is informational; nothing in this actor's output constitutes betting advice. Users are responsible for ensuring their use complies with TennisExplorer's Terms of Service, applicable data-protection law (GDPR, CCPA, etc.), local gambling regulations, and any contractual obligations of their own organisation.

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