Access structured tennis betting odds, live prices, opening odds, closing lines, market movement, implied probabilities and historical pricing data for ATP, WTA and professional tennis events through a developer-friendly REST JSON API built for sportsbooks, analytics platforms, betting tools and prediction systems.
Update: Live tennis odds are now available through our low-latency Tennis WebSocket service for plans starting at $99/month.
{
"match_id": "madrid-2026-qf-01",
"match": "Carlos Alcaraz vs Jannik Sinner",
"market": "match_winner",
"format": "decimal",
"opening_odds": {
"alcaraz": 1.85,
"sinner": 1.95
},
"current_odds": {
"alcaraz": 1.72,
"sinner": 2.10
},
"movement": {
"alcaraz": "-0.13",
"sinner": "+0.15"
},
"updated_at": "2026-05-02T14:22:00Z"
}
Professional Tennis Betting Odds Data
Tennis odds data gives developers and analysts a market-based view of each match. Rankings show official player position. Results show what happened. Odds show what the market expected before and during the match.
The Tennis Odds API provides structured betting market data for ATP, WTA and professional tennis use cases, including live odds, market movement, historical pricing, player comparison context and match-level betting information through REST endpoints and JSON responses.
Odds data is especially valuable when combined with rankings, recent form, surface performance, head-to-head records, live scores and historical match results. Used correctly, it can help products explain probability, market expectation and price movement rather than only showing a raw number.
Tennis Odds API Features
Live Betting Odds
Retrieve ATP and WTA tennis betting odds and current market pricing for match analysis, dashboards and betting products.
Market Movement
Track line movement and price changes before and during matches to understand shifting market expectations.
Historical Odds
Analyse historical tennis prices, opening lines, closing prices and long-term market behaviour where available.
Player Comparison
Combine odds with H2H records, rankings, surface performance and recent form for richer pre-match analysis.
REST JSON API
Use fast JSON responses designed for betting tools, dashboards, prediction systems and sports applications.
ATP and WTA Coverage
Build products around professional tennis betting markets across men’s and women’s tours.
What Tennis Odds Fields Matter?
Odds data should be stored with enough structure to support analysis, display, timestamps and historical comparison. A single price is useful, but a production odds product usually needs market, source, time and movement context.
| Data Area | Example Fields | Why It Matters |
|---|---|---|
| Match identity | match_id, player IDs, tournament, start time | Connects odds to live scores, H2H pages and historical results. |
| Market identity | market, selection, bookmaker/source, odds format | Prevents confusion between match winner, totals, spreads and other markets. |
| Price timing | opening_odds, current_odds, closing_odds, updated_at | Enables market movement and closing-line analysis. |
| Movement | absolute change, percentage change, direction | Shows whether the market shortened, drifted or stayed stable. |
| Probability | implied_probability, overround, margin-adjusted probability | Useful for analytics, predictions and model benchmarking. |
| Status | pre-match, live, suspended, closed, settled | Helps users understand whether a market is current, in-play or historical. |
Why Odds Data Matters in Tennis
A tennis result without odds context can be misleading. Beating a top player as a heavy underdog is different from winning as a 1.10 favourite. Odds allow applications to measure player performance against expectation rather than treating every win or loss equally.
Tennis odds can help answer questions such as:
Was the player expected to win?
Compare final results with implied probabilities from the betting market.
Did the market move?
Track whether prices shortened or drifted before a match started.
Was there closing-line value?
Compare model prices or early prices against later market prices.
Are certain players mispriced?
Study whether markets consistently underprice or overprice specific player profiles.
How does surface affect pricing?
Analyse whether market expectation changes across clay, grass, hard court and indoor events.
How do odds compare with rankings?
Identify matches where the betting market disagrees with ranking-based expectations.
Opening Odds, Closing Odds and Market Movement
Serious tennis odds products should distinguish between opening prices, current prices and closing prices. These prices answer different questions.
| Odds Type | Meaning | Best Use Case |
|---|---|---|
| Opening odds | The early market price when a match is first listed | Studying early market expectation and initial bookmaker pricing. |
| Current odds | The latest available price at a given timestamp | Live dashboards, odds comparison and market monitoring. |
| Closing odds | The final pre-match price near match start | Model benchmarking, closing-line value and backtesting. |
| Live odds | In-play price during the match | Trading tools, live implied probability charts and in-play dashboards. |
| Historical odds | Archived prices from previous matches | Research, model training, strategy testing and player analysis. |
Market movement becomes especially useful when connected to injuries, schedule changes, surface conditions, recent form, ranking changes or news-driven market reaction.
Converting Tennis Odds into Implied Probability
Decimal odds can be converted into implied probability using a simple formula:
Implied probability = 1 / decimal odds
For example, decimal odds of 2.00 imply a 50% probability before accounting for bookmaker margin.
1 / 2.00 = 0.50 = 50%
In real betting markets, both players’ raw implied probabilities often add up to more than 100%. The difference is the bookmaker margin or overround. Serious analytics products should account for this before comparing model probabilities with market probabilities.
Example Tennis Odds Markets
Alcaraz vs Sinner
Zverev vs Medvedev
Swiatek vs Pegula
Shelton vs Fils
Example prices are illustrative. Production applications should always display the latest odds and timestamps returned by the API.
Example Odds API Request
Retrieve tennis betting odds through REST API endpoints using RapidAPI. Odds data can be combined with live scores, rankings, H2H records and historical results to build richer betting and analytics products.
curl --request GET \ --url https://tennis-api-atp-wta-itf.p.rapidapi.com/tennis/v2/odds \ --header 'X-RapidAPI-Key: YOUR_API_KEY' \ --header 'X-RapidAPI-Host: tennis-api-atp-wta-itf.p.rapidapi.com'
{
"match_id": "rg-final-001",
"match": "Djokovic vs Nadal",
"market": "Match Winner",
"format": "decimal",
"odds": {
"djokovic": 1.67,
"nadal": 2.25
},
"updated_at": "LIVE"
}
Built for Sportsbooks, Betting Tools and Analytics Platforms
Sportsbooks
Integrate ATP and WTA betting market data into sportsbook platforms, trading dashboards and internal tools.
Odds Comparison
Track price changes, compare implied probabilities and display odds movement for tennis match pages.
Prediction Models
Use odds data alongside rankings, H2H records, surface performance and historical results for predictive systems.
Sports Analytics
Analyse betting market efficiency, player pricing trends and performance against expectation.
Live Match Centres
Display betting odds alongside live scores, rankings, player form and match status.
Trading Platforms
Use market movement feeds for live trading, risk monitoring and price analysis workflows.
Odds Data for Prediction Models and Backtesting
Historical odds are valuable for model evaluation because they provide a market benchmark. A tennis prediction model should not only be judged by how often it picks winners; it should also be compared against implied probability and closing prices where available.
| Workflow | Odds Data Needed | Purpose |
|---|---|---|
| Convert prices | Decimal odds | Create implied probability features. |
| Compare with model | Model probability and market probability | Identify where the model disagrees with the market. |
| Backtest strategy | Historical odds and match outcomes | Test whether an edge existed at available prices. |
| Measure closing-line value | Early odds and closing odds | Evaluate whether a model identified value before market movement. |
| Segment results | Tour, surface, tournament, ranking band | Understand where model performance is stronger or weaker. |
Using Odds Data Responsibly
Betting odds are powerful data, but they should be presented clearly and responsibly. If your product displays odds, users should understand the market, timestamp, price format and whether the data is live, pre-match, opening, closing or historical.
Developer teams should consider:
Timestamp Every Price
Odds can change quickly, so display when the price was last updated.
Separate Live and Pre-Match Odds
In-play markets behave differently from pre-match markets and should be labelled clearly.
Convert to Implied Probability Carefully
Decimal odds can be converted into implied probability, but margin and overround should be considered in serious analysis.
Combine with Tennis Context
Odds become more useful when shown with rankings, form, surface records, injuries where known and H2H history.
Avoid Misleading Guarantees
Prediction and odds products should avoid implying certainty. Tennis outcomes remain uncertain.
Respect Local Regulations
Betting-related products should account for jurisdiction, user eligibility and responsible gambling requirements.
Implementation Tips for Tennis Odds Products
Store Timestamps
Odds without timestamps are difficult to interpret. Store update time, capture time and match start time where available.
Separate Market Types
Keep match winner, totals, spreads and live markets clearly separated in your database and UI.
Cache Historical Prices
Historical odds can often be cached longer than live markets depending on API terms and product requirements.
Show Data Freshness
Users should know whether an odds price is live, delayed, closed or historical.
Connect Match Context
Odds pages are stronger when linked to rankings, H2H records, recent form, surface records and live scores.
Handle Missing Markets
Some matches or lower-level events may have limited markets. Design graceful empty states and labels.
Frequently Asked Questions
Does the API provide live ATP and WTA odds?
Yes. The API supports ATP, WTA, Grand Slams and Olympics tennis betting odds and market information for tennis odds use cases.
Can I track market movement?
Yes. Odds movement and pricing changes can be tracked through API responses where available.
Does the API support historical odds analysis?
Historical tennis pricing and market analysis can be used for betting models, trading research and analytics workflows.
What format does the API return?
The Tennis Odds API uses REST endpoints and returns structured JSON responses.
Who uses tennis betting odds APIs?
Sportsbooks, betting platforms, analytics companies, odds comparison products, trading tools and AI prediction systems commonly use tennis odds APIs.
Can odds data be used in prediction models?
Yes. Odds can be converted into implied probabilities and used as features or benchmarks in tennis prediction models, especially when combined with rankings, form, surface and historical results.
What is implied probability?
Implied probability is the probability suggested by the odds. For decimal odds, the basic calculation is 1 divided by the decimal price, before adjusting for bookmaker margin.
What is closing-line value?
Closing-line value compares an earlier price or model price with the closing market price. It is often used to evaluate whether a model found value before the market moved.
Start Using the Tennis Odds API
Access ATP and WTA tennis betting odds, market movement and pricing data through a professional developer-friendly Tennis API.