Calibrated AI top picks for ATP and WTA matches across all four Grand Slams, Masters 1000s, and tour-level events on every surface. Updated daily with form, surface specialty, head-to-head, and ranking trends folded into every prediction. We cap our confidence on most picks because tennis upsets happen โ even the best player on tour loses to lower-ranked opponents on the wrong surface or off bad form. Free, no login required.
โก 20 Tennis predictions today
Written by the Predictify Sports AI team ยท Last updated 8 May 2026
Monday, May 11 ยท 8 matches
Jannik Sinner vs Alexei Popyrin
Frances Tiafoe vs Andrea Pellegrino
Pablo Llamas Ruiz vs Daniil Medvedev
Mattia Bellucci vs Martin Landaluce
Andrey Rublev vs Alejandro Davidovich Fokina
Brandon Nakashima vs Nikoloz Basilashvili
Mariano Navone vs Hamad Medjedovic
Flavio Cobolli vs Thiago Agustin Tirante
Monday, May 11 ยท 12 matches
Victoria Bosio vs Elena Bocchi
Zhibek Kulambayeva vs Nicole Fossa Huergo
Susan Bandecchi vs Sapfo Sakellaridi
Hanne Vandewinkel vs Whitney Osuigwe
Marta Lombardini vs Jessica Bouzas Maneiro
Barbora Krejcikova vs Victoria Jimenez Kasintseva
Federica Urgesi vs Maja Chwalinska
Lois Boisson vs Dominika Salkova
Alycia Parks vs Mary Stoiana
Lisa Pigato vs Lucia Bronzetti
Gabriela Ce vs Deborah Chiesa
Kaja Juvan vs Moyuka Uchijima

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Clay, hard court, and grass produce different tennis. Clay rewards patience, defensive depth, and heavy topspin โ Alcaraz, Sinner, and Swiatek dominate Roland Garros. Hard courts (Indian Wells, Miami, US Open, Australian Open) reward aggressive baseline play and big serves. Grass โ only Wimbledon at the top level โ favors fast service motions and net play; rally lengths are the shortest of any surface. Our prediction prompt includes each player's recent surface-specific form pulled from search grounding, plus their career-long surface tendencies.
Men's Grand Slam matches are best of 5; everything else (ATP 1000s/500s, all WTA matches, even Davis Cup since 2019) is best of 3. Best of 5 favors physical players who recover well between matches and rewards players who can grind through long matches โ upsets are rarer because variance is washed out over more sets. Best of 3 has higher variance: one bad service game in a tight set can decide the match. Our predictions account for the format when generating confidence scores.
H2H records often reveal style mismatches that rankings miss. A player ranked #20 might consistently beat a top-10 opponent because their backhand exploits a weakness. Recent form windows matter too: a player who just won a tournament enters with momentum and confidence; one returning from injury or a long break often takes a round or two to find rhythm. Our AI uses search grounding to fetch both H2H history and recent results before generating each match prediction.
Match winner is the headline market, but tennis offers many alternatives. Set handicap (e.g. -1.5 sets favorite) provides better value when one player is heavily favored. Total games (over/under, typically 21.5โ24.5 lines) lets you bet on whether the match will be tight or one-sided. Exact set score (2-0, 2-1, 3-0, 3-1, 3-2) carries longer odds with focused upside. Retirement risk matters โ players returning from injury have higher walkover rates. Our match pages cover the main markets where odds are available.
We donโt run a black box. Hereโs exactly what feeds into every Predictify tennis pick:
Every ATP and WTA match across the main tour and 250-level events appears in our database within hours of the draw being published. We cover all four Grand Slams (Australian Open, French Open, Wimbledon, US Open), all nine ATP Masters 1000s, the WTA 1000s, and selected ATP 500 / WTA 500 events on hard, clay, grass, and indoor courts.
For every player in every match, we store the current ATP/WTA ranking, recent form (wins/losses on the current surface in the last 8โ12 weeks), career head-to-head record, surface-specific win rate, age and tour experience, and recent injury or withdrawal flags. Twelve to fifteen signals per player, plus the matchup-specific dynamics (lefty vs righty, big-server vs returner, baseline vs net).
Googleโs Gemini model, with web search grounding for late news (player withdrawals, retirements, on-court warm-up reports), evaluates each match against our structured player data. The model reads both player profiles and the surface context, then produces a winner probability.
Tennis isnโt horse racing โ best-of-three or best-of-five formats produce more deterministic outcomes than 12-runner handicaps, so confidence numbers run higher. But we still cap them honestly. A clear ATP top-10 favourite over an unranked qualifier on their best surface might warrant 75โ80% confidence. A first-round Masters 1000 between two equally-ranked clay-courters might be 52โ48 โ and we say so.
Every ATP and WTA match we predict gets graded automatically once results land. If we predicted Sinner and Sinner won, thatโs a hit. If Sinner lost in three sets to a qualifier, thatโs a miss โ recorded honestly. All hits and misses are public on our accuracy page.
We track every single tennis prediction we publish. Hits, misses, sample sizes, surface-specific calibration โ all visible at /accuracy.
Tennis is one of our higher-volume sports โ we typically grade 50โ100 ATP and WTA matches per week during peak tour months. That means we accumulate meaningful sample size faster than in low-volume sports. Once we hit several hundred graded matches per surface, the calibration data starts carrying real signal: how well our 70%+ confidence picks actually perform, whether weโre stronger on hard courts than clay, and so on.
We wonโt claim 75% accuracy on 30 matches. Weโll show you the real number with the sample size attached and let you read it for yourself.
The official world ranking on the menโs (ATP) or womenโs (WTA) tour. A 52-week rolling points total. Useful as a baseline for player quality but doesnโt capture surface specialty or recent form.
The court type: hard (most events), clay (French Open, spring European tour), grass (Wimbledon and brief grass swing), indoor hard (year-end events, Davis Cup ties). Some players are clear surface specialists; others are surface-agnostic.
Career record between two specific players. Strong when theyโve played 5+ times โ anything below that is a small sample. We weigh surface-specific H2H more than overall H2H.
How often a player wins their service games. Big-servers like Hubert Hurkacz hold 90%+. Return-oriented players like Diego Schwartzman hold lower but break more often. Style matchups matter โ a big-server vs another big-server tends to produce tiebreaks; a returner vs a returner produces more breaks of serve.
Inverse of hold โ how often a player breaks their opponentโs serve. The differential between a playerโs hold% and their opponentโs break% is roughly predictive of who controls the match.
A player getting 65%+ of first serves in and winning 75%+ of those points is dominant on serve. When that drops below 60% / 70%, theyโre vulnerable.
Tennis is a young personโs sport for women (peak 19โ25) and a longer arc for men (peak 24โ30). A 33-year-old isnโt washed up but is more vulnerable in best-of-five formats and on physical surfaces like clay.
Best-of-three (most ATP/WTA events) is more upset-prone than best-of-five (Grand Slam main draw, men only). The longer format gives the better player more time to assert.
Most casual bettors and old-school models lean on world rankings. The market often misprices clay-court specialists at the French Open or grass-court players at Wimbledon. Look for cases where the lower-ranked player has a clear surface advantage and recent form on that surface.
The match-winner line gets the most attention from sportsbooks. Markets like total games (over/under), set betting (2โ0 vs 2โ1), and handicap games sometimes carry softer prices, particularly for matches involving big-servers. Big servers vs big servers correlate with high-game-total outcomes.
First-round Grand Slam matches between a top seed and a qualifier often see a tight first set as the favourite warms up. The pre-match price on the favourite tightens further as soon as they win the first set, but the in-play price on a 2โ0 sweep often offers value if the favourite has won the opener clearly.
Some players are flag-bearers for mid-match retirements. Backing them in best-of-five is a hidden risk. Check recent form for in-match retirements before staking heavily.
Most tennis bettors bet flat across surfaces. Your edge probably isnโt uniform โ you might be sharp on clay and weak on grass, or strong on womenโs matches and average on menโs. Keep a spreadsheet and find your real edge.
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