Today's AI WTA Tennis Predictions

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Last updated: 2026-07-07

Wimbledon

grand slam | Quarterfinals | grass
Wimbledon, GBR

Marta Kostyuk

VS

Jasmine Paolini

Win Probability

Kostyuk
81.3%
Paolini
18.7%

AI Predictions

23.3 Games
3 Aces
5 D.Faults

Most likely set score: 2-0 (52%)

P(Tiebreak): 26.0%

View Form, ELO Trends & Match Analysis

Wimbledon

grand slam | Quarterfinals | grass
Wimbledon, GBR

Linda Noskova

VS

Elise Mertens

Win Probability

Noskova
58.0%
Mertens
42.0%

AI Predictions

24.2 Games
9 Aces
9 D.Faults

Most likely set score: 2-0 (34%)

P(Tiebreak): 32.0%

View Form, ELO Trends & Match Analysis

Wimbledon

grand slam | Quarterfinals | grass
Wimbledon, GBR

Jessica Pegula

VS

Coco Gauff

Win Probability

Pegula
52.7%
Gauff
47.3%

AI Predictions

20.7 Games
5 Aces
7 D.Faults

Most likely set score: 2-0 (33%)

P(Tiebreak): 32.0%

View Form, ELO Trends & Match Analysis

Wimbledon

grand slam | Quarterfinals | grass
Wimbledon, GBR

Naomi Osaka

VS

Karolina Muchova

Win Probability

Osaka
45.9%
Muchova
54.1%

AI Predictions

21.6 Games
8 Aces
5 D.Faults
View Form, ELO Trends & Match Analysis

AI-Powered WTA Tennis Predictions

Our proprietary deep learning models are trained on more than 30,000 WTA matches (2016-present data coverage) and leverage over 500 features to generate accurate predictions. Unlike simple win/loss predictions, we generate full probability distributions for:

Match Winner
Total Games
Games Spread
Aces Predictions
Double Faults
Tiebreak Likelihood
Exact Set Score

Our models incorporate rolling averages by surface type (hard, clay, grass) and tournament category (Grand Slams, WTA 1000, WTA 500/250), along with our proprietary Form Index and Surface Index that quantify player momentum and surface-specific strengths.

Each feature carries different weight depending on the prediction type. For aces predictions, the surface is highly influential (grass courts produce far more aces than clay), plus each player's serve index and her recent serve performance in the last matches. Learn more about our Tennis Indices

WTA Power Rankings

See how every WTA player ranks across our rating systems: ELO, Glicko-2, surface-specific ratings (hard, clay, grass), and Form Index. Search any player to view all her rankings at a glance.

View Power Rankings

Historical WTA Tennis Analysis

Explore how key WTA match statistics have evolved across 9 seasons (2017-2025) and 31,614 matches. Because women's tennis is best-of-3 at every level, including the Grand Slams, these trends stay remarkably stable across tiers and inform our AI models.

31,614
Matches Analyzed
9
Seasons

WTA Tour (Best-of-3)

21.5
Avg Games
5.2
Avg Aces
7.6
Avg DFs

Grand Slams (Best-of-3)

21.5
Avg Games
5.6
Avg Aces
7.0
Avg DFs
WTA World No. 1 Ranking Timeline: Serena Williams to Sabalenka (2014-Present)
WTA world No. 1 ranking timeline (2014-present): Serena Williams held No. 1 into 2017, trading with Angelique Kerber. Karolina Pliskova and Garbine Muguruza held it briefly in 2017. Simona Halep and Caroline Wozniacki held it across 2017-2019. Naomi Osaka and Ashleigh Barty alternated in 2019, with Barty holding into 2022. Iga Swiatek and Aryna Sabalenka have traded No. 1 from 2022 to today.

WTA Match Statistics by Surface (Hard, Clay, Grass) Over Time

Average Total Games per WTA Tour Match by Surface (Hard, Clay, Grass)
WTA Tour average total games per best-of-3 match by surface from 2017 to 2025. Grass court matches average around 22 to 23 games, hard court around 21 to 22, and clay around 21 to 22.
Average Aces per WTA Tour Match by Surface (Hard, Clay, Grass)
WTA Tour average aces per match by surface from 2017 to 2025. Grass court matches produce the most aces (about 7 to 8.5), followed by hard court (about 5.5 to 6.5) and clay (about 4).
Average Double Faults per WTA Tour Match by Surface (Hard, Clay, Grass)
WTA Tour average double faults per match by surface from 2017 to 2025. Hard court averages about 7.5 double faults, clay about 7 to 9.5, and grass about 7 to 9.
Average Total Games per WTA Grand Slam Match by Surface (Hard, Clay, Grass)
WTA Grand Slam average total games per best-of-3 match by surface from 2017 to 2025. Hard, clay and grass all cluster near 21 to 22 games.
Average Aces per WTA Grand Slam Match by Surface (Hard, Clay, Grass)
WTA Grand Slam average aces per match by surface from 2017 to 2025. Hard court produces the most aces (about 6 to 6.8), grass is similar (about 5.5 to 6.6), and clay the fewest (about 3 to 4.7).
Average Double Faults per WTA Grand Slam Match by Surface (Hard, Clay, Grass)
WTA Grand Slam average double faults per match by surface from 2017 to 2025. Hard court averages about 7 double faults, clay about 6.5, and grass about 6.9.

WTA Tournament Coverage

We provide AI predictions for all major WTA Tour events, covering the following WTA calendar:

Grand Slams

  • Australian Open
  • Roland Garros
  • Wimbledon
  • US Open

WTA 1000

  • Doha
  • Dubai
  • Indian Wells
  • Miami
  • Madrid
  • Rome
  • Canadian Open
  • Cincinnati
  • Wuhan
  • Beijing

WTA 500

  • Adelaide
  • Abu Dhabi
  • Stuttgart
  • Charleston
  • Berlin
  • Eastbourne
  • Washington
  • Monterrey
  • Ningbo
  • Tokyo

WTA 250

  • Auckland
  • Hobart
  • Singapore
  • Bogota
  • Rabat
  • 's-Hertogenbosch
  • Nottingham
  • Prague
  • + more tournaments

Prediction Transparency

We believe in full transparency. All our historical predictions and their outcomes are publicly available. Track our prediction accuracy and model performance over time:

Current Season Performance Model Calibration

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Frequently Asked Questions

Women's tennis is contested over best-of-3 sets at every level of the tour, including all four Grand Slams. Because the format never changes, our exact set-score model needs only four classes — 2-0, 2-1, 1-2, 0-2 — with no best-of-5 branch, and match totals cluster tightly around 21 to 22 games whether it is a WTA 250 or a Slam. That uniformity keeps our games, tiebreak and set-score distributions consistent across the whole calendar.

Our WTA models learn from more than 31,000 tour-level matches spanning nine complete seasons (2017-2025); the 22,188 with a complete statistical record form the labelled modelling set the seven models train on. Because every match shares the same best-of-3 format, the models see a dense, homogeneous sample of women's tennis rather than a mix of formats, which sharpens the total-games, aces and double-faults distributions. All of our historical predictions and outcomes are published on the WTA performance tracking page.

The women's serve profile is distinct: across our data WTA matches average about 5.2 aces and 7.6 double faults, so double faults typically outnumber aces — the reverse of the men's game. Our aces and double-faults models (Poisson and negative-binomial) are fitted on WTA data only, so they learn women's serving distributions directly. Grass lifts ace counts while clay tends to push double faults higher, and the models track those surface swings.

The women's tour is graded by tier: the four Grand Slams at the top, then the ten WTA 1000 events, the WTA 500s and the WTA 250s. We generate predictions across every tier. Tournament category is itself a model feature, because a WTA 1000 draw is deeper than a 250 and that changes how competitive an early-round match is expected to be.

Hard, clay and grass each leave their own signature on the women's tour. Grass produces the most aces and the shortest matches, clay lengthens rallies and nudges double faults up, and hard court sits in between. We compute surface-specific ratings and rolling form separately for each surface, so a clay specialist is rated differently on grass — a distinction the raw WTA ranking never makes.

We run an independent rating pipeline over WTA results only. Elo updates after every WTA match — opponent strength and result move a player's number immediately — while Glicko-2 additionally tracks how uncertain that rating is after a lay-off or a busy run. Both are computed purely from women's results, along with surface-specific variants, and they feed our neural networks alongside the Form and Surface indices, reacting to current form far faster than 52-week ranking points. Learn more about our Tennis ELO Rating System

The Form Index captures a player's recent momentum — how she has been performing over her latest matches relative to expectation — while the Surface Index measures how much stronger or weaker she is on a given surface than her own baseline. Both are derived entirely from WTA match history and are among the higher-weighted inputs to our models, especially for total games and serve-statistic predictions. Learn more about our Tennis Indices.

Our timeline tracks the WTA world No. 1 from 2014 to today: Serena Williams, Angelique Kerber, Karolina Pliskova, Garbiñe Muguruza, Simona Halep, Caroline Wozniacki, Naomi Osaka, Ashleigh Barty, Iga Świątek and Aryna Sabalenka. It illustrates how competitive the top of the women's game has been, with several players trading the No. 1 spot back and forth within a single season.

Yes. Alongside match winner, total games, games spread, aces and double faults, a binary classifier estimates the probability of any tiebreak, and a multiclass model produces the full distribution over the four possible best-of-3 set scores: 2-0, 2-1, 1-2, 0-2. Because there is no best-of-5 on the women's tour, that same four-class set-score model applies to every WTA match, Grand Slams included.