Live tennis win probabilities, built on real data
A differentiated Markov chain model using surface-specific serve stats, pressure score-state adjustments, fatigue, and momentum. Free API. Updates every 30 seconds.
Get your free API keyExample response
GET /v1/live?key=YOUR_KEY
[
{
"match_id": "atp-alcaraz-zverev-2026-06-05",
"player1": "Carlos Alcaraz",
"player2": "Alexander Zverev",
"surface": "clay",
"tournament": "Roland Garros",
"score": { "sets": [[6,3],[4,5]], "games": [4,5], "best_of": 5 },
"p1_win_prob": 0.4231,
"p2_win_prob": 0.5769,
"is_live": true
}
]
What makes this different
- Surface-specific: Separate serve % for clay, grass, hard
- Pressure-adjusted: Point win rate varies at 0-40 vs 40-0
- Fatigue-aware: Discounts for back-to-back matches
- Momentum term: Tracks in-match point run rates