Today's AI Tennis Predictions and Analysis

Also available in Claude. Query these predictions in plain language — our native connector lets Claude call our models on demand, no code.

Set it up

Share this page

Last updated: 2026-06-19

Halle

atp 500 | Quarterfinals | grass
Germany

Alexander Zverev

VS

Raphael Collignon

Win Probability

Zverev
87.8%
Collignon
12.2%

AI Predictions

25.7 Games
17 Aces
6 D.Faults

Most likely set score: 2-0 (59%)

View Form, ELO Trends & Match Analysis

London

atp 500 | Quarterfinals | grass
Great Britain

Tommy Paul

VS

Alejandro Davidovich Fokina

Win Probability

Paul
68.1%
Fokina
31.9%

AI Predictions

25.1 Games
12 Aces
4 D.Faults

Most likely set score: 2-0 (37%)

View Form, ELO Trends & Match Analysis

London

atp 500 | Quarterfinals | grass
Great Britain

Alex de Minaur

VS

Brandon Nakashima

Win Probability

Minaur
62.1%
Nakashima
37.9%

AI Predictions

25.0 Games
14 Aces
4 D.Faults

Most likely set score: 2-0 (35%)

P(Tiebreak): 34.0%

View Form, ELO Trends & Match Analysis

London

atp 500 | Round of 16 | grass
Great Britain

Hamad Medjedovic

VS

Ugo Humbert

Win Probability

Medjedovic
43.1%
Humbert
56.9%

AI Predictions

25.6 Games
17 Aces
7 D.Faults

Most likely set score: 0-2 (31%)

View Form, ELO Trends & Match Analysis

Halle

atp 500 | Quarterfinals | grass
Germany

Ben Shelton

VS

Taylor Fritz

Win Probability

Shelton
37.3%
Fritz
62.7%

AI Predictions

26.9 Games
25 Aces
5 D.Faults

Most likely set score: 0-2 (36%)

View Form, ELO Trends & Match Analysis

Halle

atp 500 | Quarterfinals | grass
Germany

Frances Tiafoe

VS

Felix Auger-Aliassime

Win Probability

Tiafoe
34.0%
Auger-Aliassime
66.0%

AI Predictions

28.3 Games
19 Aces
5 D.Faults

Most likely set score: 0-2 (41%)

View Form, ELO Trends & Match Analysis

London

atp 500 | Quarterfinals | grass
Great Britain

Arthur Fery

VS

Francisco Cerundolo

Win Probability

Fery
26.6%
Cerundolo
73.4%

AI Predictions

25.4 Games
9 Aces
4 D.Faults

Most likely set score: 0-2 (44%)

P(Tiebreak): 30.0%

View Form, ELO Trends & Match Analysis

Halle

atp 500 | Quarterfinals | grass
Germany

Daniel Altmaier

VS

Daniil Medvedev

Win Probability

Altmaier
17.8%
Medvedev
82.2%

AI Predictions

25.6 Games
15 Aces
7 D.Faults

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

View Form, ELO Trends & Match Analysis

What's Inside Each Match Analysis

Click on any match card above to access our in-depth match analysis pages. Each article provides comprehensive insights you won't find elsewhere:

Probability Distribution Charts

Interactive visualizations showing the full probability distribution for total games, games spread, aces, and double faults predictions.

Cumulative Distribution Functions

CDF charts to understand probabilities like "chance of more than 25 aces" or "probability of under 22 games".

Tiebreak Likelihood

Binary classifier estimating the probability that any tiebreak is played in the match — useful for live-betting and watch-night planning.

Exact Set Score Distribution

Multiclass softmax across every valid set outcome (2-0, 2-1, 1-2, 0-2 in best-of-3; 3-0 through 0-3 in best-of-5), showing how the match is most likely to play out.

Player Ranking Evolution

Historical ATP ranking charts for both players, showing form trends and career trajectory.

Proprietary Tennis Indices

Our custom-built metrics capturing player form, surface performance, and match dynamics.

Learn How to Read These Charts

AI-Powered Tennis Predictions

Our proprietary deep learning models are trained on more than 30,000 ATP matches 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, Masters 1000, ATP 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), tournament format matters (Grand Slams are best-of-5 sets, meaning more games and serving opportunities), plus each player's serve index and their recent serve performance in the last matches. Learn more about our Tennis Indices

Tennis Power Rankings

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

View Power Rankings

Historical Tennis Analysis

Explore how key ATP match statistics have evolved across 11 seasons and 37,920 matches. These trends inform our AI models and provide context for current predictions.

37,920
Matches Analyzed
11
Seasons

ATP Tour (Best-of-3)

23.0
Avg Games
10.8
Avg Aces
5.5
Avg DFs

Grand Slams (Best-of-5)

30.3
Avg Games
14.5
Avg Aces
7.0
Avg DFs

ATP World #1 Ranking Timeline: Federer, Nadal, Djokovic to Sinner (2010-Present)

ATP #1 ranking timeline: Roger Federer held #1 in 2010, 2012, and 2018. Rafael Nadal held #1 in 2010-2011, 2013-2014, and 2017-2019. Novak Djokovic dominated with #1 in 2011-2016, 2018-2022, and 2023-2024. Andy Murray briefly held #1 in 2016-2017. Daniil Medvedev in 2022. Carlos Alcaraz from 2022 and Jannik Sinner from 2024.

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

Average Total Games per ATP Match by Surface (Hard, Clay, Grass)

ATP Tour average total games per match by surface type from 2015 to present. Hard court matches average around 23 games, clay around 22.8, and grass around 23.8.

Average Aces per ATP Match by Surface (Hard, Clay, Grass)

ATP Tour average aces per match by surface from 2015 to present. Grass court matches produce the most aces (~14-15), followed by hard court (~12) and clay (~7-8).

Average Double Faults per ATP Match by Surface (Hard, Clay, Grass)

ATP Tour average double faults per match by surface from 2015 to present. Grass court matches average ~6 double faults, hard court ~5.5, and clay ~5.

Average Total Games per Grand Slam Match by Surface (Hard, Clay, Grass)

Grand Slam average total games per match by surface from 2015 to present. Hard court matches average around 30 games, clay around 29.5, and grass around 31.5.

Average Aces per Grand Slam Match by Surface (Hard, Clay, Grass)

Grand Slam average aces per match by surface from 2015 to present. Grass court Slam matches produce the most aces (~16-18), followed by hard court (~15-16) and clay (~9-10).

Average Double Faults per Grand Slam Match by Surface (Hard, Clay, Grass)

Grand Slam average double faults per match by surface from 2015 to present. Hard court Slam matches average ~7.8 double faults, grass ~6.5, and clay ~6.

ATP Tournament Coverage

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

Grand Slams

  • Australian Open
  • Roland Garros
  • Wimbledon
  • US Open

Masters 1000

  • Indian Wells
  • Miami
  • Monte Carlo
  • Madrid
  • Rome
  • Toronto / Montreal
  • Cincinnati
  • Shanghai
  • Paris

ATP 500

  • Rotterdam
  • Dubai
  • Acapulco
  • Barcelona
  • Halle
  • Queen's Club
  • Hamburg
  • Washington
  • Tokyo
  • Vienna
  • Basel

ATP 250

  • Brisbane
  • Adelaide
  • Auckland
  • Montpellier
  • Buenos Aires
  • Munich
  • Stuttgart
  • Eastbourne
  • + 30 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 2025 Season Performance Model Calibration

Get Daily Tennis Predictions

If you like our content and want to receive daily predictions, subscribe to our Telegram channel and receive daily AI-driven tennis predictions directly on your phone.

Join Our Telegram Channel

Download Tennis Probability Data

Every match analysis includes downloadable probability distributions in JSON and CSV format. Access full PDFs, CDFs, and match prediction data for your own analysis.

Open any match analysis above and download structured data with a free account.

Learn How to Use Our Data

Frequently Asked Questions

Unlike other prediction services that only provide simple win/loss probabilities, we are the only platform offering full probabilistic distributions for games spread, aces and double faults predictions. Our deep learning models generate complete probability distributions, allowing you to see not just the expected value but the entire range of likely outcomes. This gives you unprecedented insight into serve statistics predictions that no other tennis prediction service provides.

Our models are trained on over 30,000 ATP matches and continuously refined. We believe in full transparency—all our historical predictions and outcomes are publicly available on our performance tracking page. You can verify our accuracy across different tournaments, surfaces, and prediction types.

A probability distribution shows the likelihood of each possible outcome, not just a single prediction. For example, instead of just saying "expected 24 games," we show you the probability of 20, 21, 22, 23, 24, 25+ games and so on. This is especially valuable for aces predictions and double faults predictions, where understanding the range of outcomes provides much deeper insight than a single number. Learn more about probability distributions.

We provide AI predictions for the entire ATP Tour calendar: all four Grand Slams (Australian Open, Roland Garros, Wimbledon, US Open), all nine Masters 1000 events, every ATP 500 tournament, and over 30 ATP 250 events. Our coverage includes matches on all surfaces—hard court, clay, and grass.

Our predictions are generated daily before matches begin. We process the latest ATP rankings, recent match results, and player form data to ensure predictions reflect the most current information available. Each match analysis page includes insights and predictions specific to that matchup.

Tennis Indices are our proprietary metrics that capture aspects of player performance not reflected in standard statistics. They include surface-specific form indicators, momentum metrics, and head-to-head adjustments. These indices are fed into our deep learning models to improve prediction accuracy. Learn more about our Tennis Indices.

Our deep learning models leverage over 500 features to generate predictions. These include rolling averages by surface type (hard, clay, grass) and tournament category (Grand Slams, Masters 1000, ATP 500/250), plus our proprietary Form Index and Surface Index. Each feature carries different weight depending on the prediction type. For example, aces predictions heavily rely on surface (grass produces more aces), tournament format (Grand Slams are best-of-5, meaning more serving opportunities), player serve indices, and recent serve performance trends.

We provide two types of charts in each match analysis. The probability distribution chart shows you the likelihood of each possible outcome—the peak indicates the most likely result, and the spread shows how certain the prediction is. The CDF (Cumulative Distribution Function) chart is perfect for understanding over/under scenarios—it shows the probability of outcomes being less than or equal to any value. Read our complete guide to understanding probability charts

Our proprietary ELO rating system is one of the key features fed into our neural networks. Unlike ATP rankings that reflect points accumulated over 52 weeks, ELO ratings capture current player strength by updating after every match. We calculate multiple ELO variants—including tournament-weighted, surface-specific, and performance-based ratings—that help our models understand player quality in real-time. These dynamic ratings, combined with our other features, enable our neural networks to generate accurate probability distributions for match outcomes. Learn more about our Tennis ELO Rating System

Yes. Beyond match winner, total games, games spread, aces and double faults, we run two dedicated models for match-format outcomes. A binary classifier estimates the probability that any tiebreak is played in the match — surfaced on each card whenever the model is confident enough to commit to a direction. A multiclass softmax model produces the full probability distribution over every valid set score: 2-0, 2-1, 1-2, 0-2 for best-of-3 events and 3-0, 3-1, 3-2, 2-3, 1-3, 0-3 for Grand Slams. The "Most likely set score" appears on each match card; the full distribution is plotted inside every match analysis page.