What features do your AI models use for predictions?

Our deep learning models leverage over 500 features to generate predictions. These include rolling averages calculated by surface type (hard, clay, grass) and tournament category (Grand Slams, Masters 1000, ATP 500/250), our proprietary Form Index and Surface Index.

Today's AI Tennis Predictions and Analysis

Q: How accurate are your AI tennis predictions?

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.

Q: How do I read the probability distribution and CDF charts?

We provide two types of charts in each match analysis. The probability distribution chart shows 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.

Q: What role do ELO ratings play in your predictions?

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.

Last updated: 2026-03-21

miami

masters_1000 | Round of 64 | hard

FL, U.S.A.

Francisco Cerundolo

Thiago Agustin Tirante

Win Probability

74.8%

25.2%

Cerundolo Tirante

View Games, Aces & Double Faults Predictions

miami

masters_1000 | Round of 64 | hard

FL, U.S.A.

Brandon Nakashima

Marin Cilic

Win Probability

72.9%

27.1%

Nakashima Cilic

View Games, Aces & Double Faults Predictions

miami

masters_1000 | Round of 64 | hard

FL, U.S.A.

Ben Shelton

Alexander Shevchenko

Win Probability

71.5%

28.5%

Shelton Shevchenko

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miami

masters_1000 | Round of 64 | hard

FL, U.S.A.

Felix Auger-Aliassime

Marton Fucsovics

Win Probability

64.4%

35.6%

Auger-Aliassime Fucsovics

View Games, Aces & Double Faults Predictions

miami

masters_1000 | Round of 64 | hard

FL, U.S.A.

Andrey Rublev

Alejandro Tabilo

Win Probability

60.3%

39.7%

Rublev Tabilo

View Games, Aces & Double Faults Predictions

miami

masters_1000 | Round of 64 | hard

FL, U.S.A.

Alejandro Davidovich Fokina

Quentin Halys

Win Probability

58.5%

41.5%

Fokina Halys

View Games, Aces & Double Faults Predictions

miami

masters_1000 | Round of 64 | hard

FL, U.S.A.

Aleksandar Vukic

Rafael Jodar

Win Probability

58.1%

41.9%

Vukic Jodar

View Games, Aces & Double Faults Predictions

miami

masters_1000 | Round of 64 | hard

FL, U.S.A.

Frances Tiafoe

Arthur Cazaux

Win Probability

54.4%

45.6%

Tiafoe Cazaux

View Games, Aces & Double Faults Predictions

miami

masters_1000 | Round of 64 | hard

FL, U.S.A.

Alex Michelsen

Cameron Norrie

Win Probability

50.8%

49.2%

Michelsen Norrie

View Games, Aces & Double Faults Predictions

miami

masters_1000 | Round of 64 | hard

FL, U.S.A.

Corentin Moutet

Tomas Machac

Win Probability

46.5%

53.5%

Moutet Machac

View Games, Aces & Double Faults Predictions

miami

masters_1000 | Round of 64 | hard

FL, U.S.A.

Terence Atmane

Arthur Rinderknech

Win Probability

43.2%

56.8%

Atmane Rinderknech

View Games, Aces & Double Faults Predictions

miami

masters_1000 | Round of 64 | hard

FL, U.S.A.

Zizou Bergs

Tomas Martin Etcheverry

Win Probability

42.5%

57.5%

Bergs Etcheverry

View Games, Aces & Double Faults Predictions

miami

masters_1000 | Round of 64 | hard

FL, U.S.A.

Gabriel Diallo

Ugo Humbert

Win Probability

39.5%

60.5%

Diallo Humbert

View Games, Aces & Double Faults Predictions

miami

masters_1000 | Round of 64 | hard

FL, U.S.A.

Adam Walton

Jakub Mensik

Win Probability

32.3%

67.7%

Walton Mensik

View Games, Aces & Double Faults Predictions

miami

masters_1000 | Round of 64 | hard

FL, U.S.A.

Kamil Majchrzak

Learner Tien

Win Probability

31.9%

68.1%

Majchrzak Tien

View Games, Aces & Double Faults Predictions

miami

masters_1000 | Round of 64 | hard

FL, U.S.A.

Martin Damm

Alexander Zverev

Win Probability

17.5%

82.5%

Damm Zverev

View Games, Aces & Double Faults Predictions

miami

masters_1000 | Round of 64 | hard

FL, U.S.A.

Rei Sakamoto

Daniil Medvedev

Win Probability

13.3%

86.7%

Sakamoto Medvedev

View Games, Aces & Double Faults Predictions

miami

masters_1000 | Round of 64 | hard

FL, U.S.A.

Damir Dzumhur

Jannik Sinner

Win Probability

9.6%

90.4%

Dzumhur Sinner

View Games, Aces & Double Faults Predictions

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, 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".

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

Aces Predictions

Double Faults

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

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

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 World #1 Ranking Timeline (2010-Present): Novak Djokovic has held the #1 ranking for the longest cumulative period, followed by Rafael Nadal and Roger Federer. Recent #1 players include Jannik Sinner (2024-2025) and Carlos Alcaraz (2022-2025).

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

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

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

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

ATP Tour match statistics by surface (2015-present): Hard court matches average 23 total games, 12 aces, and 5.5 double faults. Clay court matches average 22.8 games, 7.5 aces, and 5 double faults. Grass court matches average 23.8 games, 14.5 aces, and 6 double faults.

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

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

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

Grand Slam match statistics by surface (2015-present): Hard court matches average 30 total games, 15.5 aces, and 7.8 double faults. Clay court matches average 29.5 games, 9.8 aces, and 6 double faults. Grass court matches average 31.5 games, 17 aces, and 6.5 double faults.

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

Get Daily Tennis Predictions

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Frequently Asked Questions

Unlike other prediction services that only provide simple win/loss probabilities, we are the only platform offering full probabilistic distributions for 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