Access structured ATP and WTA rankings data through a developer-friendly REST JSON API. Build ranking pages, player profiles, analytics dashboards, betting tools, fantasy products and tennis media platforms with current rankings, ranking points, movement, player context and historical ranking snapshots.
{
"tour": "ATP",
"ranking_type": "singles",
"updated": "2026-05-09",
"rankings": [
{
"player_id": "sinner-jannik",
"rank": 1,
"previous_rank": 1,
"movement": 0,
"player": "Jannik Sinner",
"country": "ITA",
"points": 9735
},
{
"player_id": "alcaraz-carlos",
"rank": 2,
"previous_rank": 3,
"movement": 1,
"player": "Carlos Alcaraz",
"country": "ESP",
"points": 8855
}
]
}
ATP and WTA Rankings Data for Developers
Tennis rankings are one of the most important datasets in professional tennis. They provide a structured view of player strength, tournament entry status, seedings, ranking movement and long-term player development.
The Tennis Rankings API gives developers access to ATP and WTA rankings data that can be used inside live score apps, player profile pages, sports media websites, fantasy tennis products, betting research tools, AI models and analytics platforms.
Instead of manually collecting ranking tables or scraping tennis websites, developers can retrieve structured JSON data through REST endpoints and connect rankings with players, fixtures, head-to-head records, tournaments and historical results.
Tennis Rankings API Features
ATP Rankings
Access men’s professional tennis rankings, player positions, ranking points and related player information.
WTA Rankings
Retrieve women’s professional tennis rankings for ranking pages, player profiles and sports data products.
Ranking Movement
Track how players move over time and identify rising players, declining players and major ranking changes.
Ranking History
Use historical ranking context for analytics, prediction models, player development studies and editorial features.
Player Profiles
Combine rankings with player profiles, nationality, match results, H2H records and tournament performance.
REST JSON API
Integrate rankings data into web, mobile and backend systems using clean API responses.
What Tennis Ranking Fields Matter?
A ranking API is most useful when it provides enough structure to power ranking tables, player pages, analytics models and weekly movement reports. Developers should look for stable player IDs, ranking dates, points and movement fields rather than only a text table.
| Data Area | Example Fields | Why It Matters |
|---|---|---|
| Ranking identity | tour, ranking_type, updated_at, week | Shows which ranking list is being displayed and when it was last updated. |
| Player identity | player_id, player name, country, age where available | Connects ranking rows to player profiles and match records. |
| Position | rank, previous_rank, movement | Supports ranking tables, movement charts and editorial updates. |
| Points | ranking points, points defended, points gained where available | Helps explain gaps between players and ranking scenarios. |
| History | ranking snapshots, career high, year-end rank | Useful for player development analysis and historical pages. |
| Connected context | recent results, upcoming tournaments, seedings | Turns a ranking table into a useful tennis product. |
Why Rankings Matter in Tennis Products
Rankings are more than a leaderboard. They influence tournament seedings, player reputation, matchup expectations, qualification paths, media narratives and betting analysis.
A ranking page can show who is number one. A better tennis product connects rankings with context: recent form, surface performance, ranking movement, player age, tournament schedule, injury returns and historical career progression.
For developers building tennis applications, rankings data is often the anchor dataset that links together player profiles, live scores, tournament pages and prediction workflows.
Ranking Data Workflows
Rankings can support many product workflows when connected to player, match and tournament data.
| Workflow | Ranking Data Needed | Product Output |
|---|---|---|
| Current rankings page | Rank, points, player, country, updated date | ATP or WTA leaderboard with links to player pages. |
| Ranking movement report | Current rank, previous rank, movement | Weekly risers, fallers and ranking-change summaries. |
| Player profile | Current rank, career high, ranking history | Player context with current and historical level. |
| Match preview | Both players’ ranks, points and movement | Ranking-based matchup context before a match. |
| Prediction model | Rank at match date, ranking trend, ranking points | Feature inputs for forecasting and player-strength models. |
| Fantasy valuation | Rank, ranking movement, recent results | Player evaluation for fantasy sports and games. |
Example ATP Rankings Table
Jannik Sinner
Carlos Alcaraz
Daniil Medvedev
Alexander Zverev
Example ranking data should be treated as illustrative. Production applications should always display the latest ranking data returned by the API, with a visible ranking date or “last updated” label.
Example Rankings API Request
Rankings data is available through RapidAPI using REST endpoints and structured JSON responses. Developers can request ranking data and connect it to other tennis endpoints for richer product experiences.
curl --request GET \ --url https://tennis-api-atp-wta-itf.p.rapidapi.com/tennis/v2/rankings \ --header 'X-RapidAPI-Key: YOUR_API_KEY' \ --header 'X-RapidAPI-Host: tennis-api-atp-wta-itf.p.rapidapi.com'
{
"tour": "WTA",
"ranking_type": "singles",
"updated": "2026-05-09",
"rankings": [
{
"player_id": "swiatek-iga",
"rank": 1,
"previous_rank": 1,
"movement": 0,
"player": "Iga Swiatek",
"country": "POL",
"points": 10485
}
]
}
Use Cases for Rankings Data
Sports Apps
Display ATP and WTA rankings inside live score apps, mobile apps and tennis platforms.
Player Profile Pages
Show current rank, ranking movement, ranking points and career context on player pages.
Fantasy Tennis Products
Use rankings data for player valuation, contest design, scoring logic and fantasy tennis insights.
Sportsbooks and Betting Tools
Combine rankings with odds, recent form, surface performance and H2H records for pre-match analysis.
Media Platforms
Create rankings pages, weekly ranking updates, player movement articles and tournament preview content.
AI and Prediction Models
Use ranking history and ranking movement as structured features in machine learning and forecasting systems.
Ranking Data for Analytics and AI
Ranking data is a useful baseline for tennis modelling, but it becomes more powerful when combined with other datasets. A player ranked number 20 may be improving quickly, recovering from injury, dominant on a specific surface or facing an opponent with a favourable matchup history.
Analytics teams commonly combine rankings with:
Recent Form
Compare ranking position against recent wins, losses and tournament runs.
Surface Records
Separate player performance by clay, grass, hard court and indoor conditions.
Head-to-Head Data
Evaluate whether ranking difference matches historical matchup performance.
Historical Results
Measure whether ranking movement is supported by long-term match performance.
Odds and Market Data
Compare rankings with market expectation and implied probabilities.
Tournament Context
Understand how rankings affect seeding, draw difficulty and qualification paths.
Ranking History and Player Development
Ranking history is especially useful for understanding a player’s career trajectory. Current rank tells you where the player stands today. Historical rankings show how quickly the player rose, whether the current level is stable, and how they performed across seasons.
| Ranking History Use Case | Data Needed | Why It Helps |
|---|---|---|
| Career progression | Weekly or periodic ranking snapshots | Shows rise, peak, recovery and decline phases. |
| Breakout detection | Ranking movement and recent results | Identifies players improving faster than public perception. |
| Prediction modelling | Rank at match date, trend, points | Prevents using current rankings for past matches. |
| Editorial features | Career-high ranking, year-end rank, ranking milestones | Creates more useful player stories and comparison pages. |
| Betting research | Ranking trend, odds context, match outcomes | Compares market expectation with player development. |
Implementation Tips for Rankings Data
Show the Ranking Date
Always display when a ranking list was updated so users know whether the table is current.
Use Player IDs
Connect ranking rows to stable player IDs rather than relying only on names.
Cache Appropriately
Rankings do not need the same refresh frequency as live scores. Cache according to update cycle and API terms.
Separate ATP and WTA
Keep tour, ranking type and date clearly labelled to avoid mixing men’s and women’s rankings.
Store Snapshots
Historical snapshots make ranking movement, trend charts and model features much more useful.
Link Related Data
Ranking pages should link to player profiles, recent matches, tournament pages and H2H comparisons.
SEO Opportunities for Tennis Rankings Pages
Tennis rankings pages can attract ongoing search demand, but they need more than a plain table. Strong ranking pages provide freshness, context, player links and useful explanations.
| Page Element | Recommended Content | Why It Helps |
|---|---|---|
| Current ranking table | Rank, player, country, points, movement | Answers the main ranking query quickly. |
| Update date | Latest ranking update date | Builds trust and avoids stale-content concerns. |
| Movement section | Biggest risers and fallers | Adds editorial value beyond a static table. |
| Player links | Links to player profiles and recent results | Improves navigation and internal linking. |
| Context notes | Tournament results, injuries, returns, ranking milestones | Creates useful content for users and search engines. |
| Structured data | ItemList, BreadcrumbList and WebPage where appropriate | Helps search engines understand page structure when used accurately. |
Avoid thin rankings pages that only display copied data. Add player context, update dates, movement explanations and internal links to make the page genuinely useful.
Frequently Asked Questions
Does the API include ATP and WTA rankings?
Yes. The API includes rankings data for ATP and WTA professional tennis tours.
Does the rankings API return JSON?
Yes. The Tennis Rankings API uses REST endpoints and returns structured JSON responses.
Can rankings data be combined with player stats?
Yes. Rankings can be combined with player profiles, fixtures, H2H records, historical results and match statistics.
Is historical rankings data available?
Historical rankings and player performance data can be used for analytics, modelling and player development analysis.
How do developers access the API?
Access is available through RapidAPI using API key authentication.
Can I use rankings data for SEO pages?
Yes. Rankings data can support ranking pages, player pages and editorial content, especially when combined with useful context such as movement, player form and tournament implications.
How often should rankings data be refreshed?
Rankings should be refreshed according to the relevant tour update cycle and your product requirements. They generally do not need the same refresh frequency as live scores.
Can rankings data be used in prediction models?
Yes. Ranking position, ranking points, movement and rank at match date are common inputs for tennis prediction and analytics models.
Start Using the Tennis Rankings API
Access ATP and WTA rankings data through a fast, reliable and developer-friendly tennis data API.