PREDICTIONS

Guide

The predictions table displays a list of upcoming matches. The bars at the centre of the table show the predicted probability of each player winning based on our models.

Above the bars the outright odds from Matchbook are shown where a market is available. We use the Matchbook as they offer the lowest commisions and have a good API which allows for easy automation. Of course if you are using a different bookmaker/exchange please double check the odds on their services.

A simple way to use the table is to multiply the odds by the probability. A number greater than 1 represents "value".

A tennis ball next the player's name is a trade that has been picked based on it having "value" and filtered for certain features such as better recent form.

In the hypothetical example below, its been identifed that there is value in betting on Michael Mmoh. Our model has predicted that their is only a 32% chance of him winning which roughly would equate to odds of 3.13. However on Matchbook they are 3.94 which represents value. Additionally Michael Mmoh has been in better form than his opponent.

Date Player 1 Player 1 Odds Player 2 Odds Player 2
Gilles Muller 1.32 3.94 Michael Mmoh
68%
32%

Below the probablity bars are two sections, Profile and Attributes. These provide some additional comparative statistics which can be used to support your decision process.

Within the Profile section there are a number of subsections which can be opened by clicking on the blue links. These include the last 5 matches and head to head, as well as a chart comparing the players' ranking history.

The Attributes section displays a radar chart with some key comparative features:

  • Win Rate - The player's win rate.
  • Form - The player's recent win rate used as an indicator of form.
  • Surface - The player's win rate on the match surface.
  • Serve - Winning % on serve.
  • Return - Winning % on return.
  • Experience - An indicator for players experience.
  • Reliablity - An indicator for how reliable the outcome for the player is in terms of what has been predicted.
  • Common Op. - Win rate over opponents that are common to both players.
  • Level Win Rate - Win rate over similar ranked opponents.
  • Quality Form - A measure of the quaility of the wins. ie winning against a player ranked 10 in the world counts more than winning against 50 in the rankings.
Mens - Biella Challenger (Clay)
Date Player 1 Player 1 Odds Player 2 Odds Player 2
19 Sep 2019, 11 a.m. Alessandro Giannessi 1.72 2.14 Marco Trungelliti
58%
42%
Profile   Price Hist   Attributes
19 Sep 2019, 11 a.m. Peter Heller 3.2 1.36 Gianluca Mager
26%
74%
Profile   Price Hist   Attributes
19 Sep 2019, 11 a.m. Paolo Lorenzi 1.68 2.2 Carlos Berlocq
60%
40%
Profile   Price Hist   Attributes
Completed Jeremy Jahn 5-7 5-7 Alejandro Davidovich Fokina
31%
69%
Profile   Attributes
Completed Federico Gaio 6-4 6-4 Viktor Galovic
58%
42%
Profile   Attributes
Completed Maxime Chazal 3-6 6-3 4-6 Elliot Benchetrit
44%
56%
Profile   Attributes
Completed Taro Daniel 6-3 6-4 Daniel Munoz-De La Nava
67%
33%
Profile   Attributes
Completed Eduard Esteve Lobato (4)6-7 3-6 Andrej Martin
28%
72%
Profile   Attributes
Completed Mario Vilella Martinez 6-1 6-4 Raul Brancaccio
69%
31%
Profile   Attributes
Markus Eriksson - - Stefano Napolitano
55%
45%
Profile   Attributes
Gian Marco Moroni - - Jaume Antoni Munar Clar
33%
67%
Profile   Attributes
Mens - Columbus Challenger (I.hard)
Date Player 1 Player 1 Odds Player 2 Odds Player 2
In play Renzo Olivo In play In play Benjamin Sigouin
47%
53%
Profile   Price Hist   Attributes
In play Alexander Ritschard In play In play Roberto Cid
46%
54%
Profile   Price Hist   Attributes
In play Edan Leshem In play In play Michael Mmoh
20%
80%
Profile   Price Hist   Attributes
18 Sep 2019, 4:30 p.m. Ryan Shane 1.51 2.58 Roy Smith
42%
58%
Profile   Price Hist   Attributes
18 Sep 2019, 4:30 p.m. Sekou Bangoura 2.15 1.75 Peter Polansky
47%
53%
Profile   Price Hist   Attributes
18 Sep 2019, 4:30 p.m. Kyle Seelig 6.1 1.14 Jeff Wolf
33%
67%
Profile   Price Hist   Attributes
18 Sep 2019, 6:05 p.m. Roberto Quiroz 1.31 3.72 James Kent Trotter
67%
33%
Profile   Price Hist   Attributes
18 Sep 2019, 6:05 p.m. Emilio Gomez 1.32 3.66 Ben Patael
47%
53%
Profile   Price Hist   Attributes
18 Sep 2019, 6:05 p.m. Donald Young 1.29 3.66 Gonzalo Escobar
58%
42%
Profile   Price Hist   Attributes
18 Sep 2019, 7:30 p.m. Mikael Torpegaard 1.17 5.1 Alexander Erler
67%
33%
Profile   Price Hist   Attributes
19 Sep 2019, 3 p.m. Liam Broady 2.89 1.42 Enzo Couacaud
47%
53%
Profile   Price Hist   Attributes
Mens - Glasgow Challenger (I.hard)
Date Player 1 Player 1 Odds Player 2 Odds Player 2
In play Viktor Durasovic In play In play John-Patrick Smith
45%
55%
Profile   Price Hist   Attributes
In play Roberto Ortega-Olmedo In play In play Botic Van De Zandschulp
40%
60%
Profile   Price Hist   Attributes
18 Sep 2019, 4:30 p.m. Johannes Haerteis 3.44 1.33 Tobias Kamke
46%
54%
Profile   Price Hist   Attributes
18 Sep 2019, 5 p.m. Ramkumar Ramanathan 1.51 2.59 Altug Celikbilek
67%
33%
Profile   Price Hist   Attributes
18 Sep 2019, 6 p.m. Quentin Halys 1.53 2.59 Arthur Rinderknech
60%
40%
Profile   Price Hist   Attributes
19 Sep 2019, 10 a.m. Alexandre Muller 2.42 1.57 Malek Jaziri
37%
63%
Profile   Price Hist   Attributes
Completed Mirza Basic 6-4 6-3 Karol Drzewiecki
60%
40%
Profile   Attributes
Completed Mathias Bourgue 7-5 3-6 7-6(2) Lukas Klein
59%
41%
Profile   Attributes
Completed Jonathan Mridha 5-7 6-4 3-6 Emil Ruusuvuori
21%
79%
Profile   Attributes
Luca Vanni - - Cem Ilkel
42%
58%
Profile   Attributes
Antoine Escoffier - - Ruben Bemelmans
45%
55%
Profile   Attributes
Mens - Kaohsiung Challenger (Carpet)
Date Player 1 Player 1 Odds Player 2 Odds Player 2
19 Sep 2019, 4 a.m. Ji Sung Nam 1.85 2.03 Andrew Harris
46%
54%
Profile   Price Hist   Attributes
19 Sep 2019, 5 a.m. Steven Diez 3.62 1.25 Yuichi Sugita
33%
67%
Profile   Price Hist   Attributes
19 Sep 2019, 6 a.m. Prajnesh Gunneswaran 1.37 3.2 Enrique Lopez-Perez
67%
33%
Profile   Price Hist   Attributes
19 Sep 2019, 7 a.m. Hiroki Moriya 5.8 1.15 John Millman
31%
69%
Profile   Price Hist   Attributes
Completed Go Soeda 6-2 6-3 Chun Hsin Tseng
62%
38%
Profile   Attributes
Completed Jiri Vesely 7-6(6) 6-1 Teimuraz Gabashvili
67%
33%
Profile   Attributes
Completed Frederico Ferreira Silva 7-6(6) 1-6 6-3 Yosuke Watanuki
60%
40%
Profile   Attributes
Completed Renta Tokuda 2-6 3-6 Jason Jung
38%
62%
Profile   Attributes
Mens - Moselle Open - Metz (I.hard)
Date Player 1 Player 1 Odds Player 2 Odds Player 2
In play Yannick Maden In play In play Ugo Humbert
33%
67%
Profile   Price Hist   Attributes
18 Sep 2019, 3:55 p.m. Gregoire Barrere 1.75 2.3 Antoine Hoang
69%
31%
Profile   Price Hist   Attributes
18 Sep 2019, 5:05 p.m. Benoit Paire 2.42 1.68 Richard Gasquet
57%
43%
Profile   Price Hist   Attributes
18 Sep 2019, 6:30 p.m. Filip Krajinovic 1.99 1.97 Fernando Verdasco
42%
58%
Profile   Price Hist   Attributes
19 Sep 2019, 9 a.m. Lucas Pouille 1.79 2.17 Lorenzo Sonego
45%
55%
Profile   Price Hist   Attributes
19 Sep 2019, 9 a.m. Pierre-Hugues Herbert 3.42 1.39 Jo-Wilfried Tsonga
36%
64%
Profile   Price Hist   Attributes
Completed Gilles Simon 6-3 6-4 Marius Copil
67%
33%
Profile   Attributes
David Goffin - - Pablo Carreno-Busta
67%
33%
Profile   Attributes
Mens - Sibiu Challenger (Clay)
Date Player 1 Player 1 Odds Player 2 Odds Player 2
19 Sep 2019, 9 a.m. Danilo Petrovic 2.05 0.0 Juan Pablo Varillas
46%
54%
Profile   Price Hist   Attributes
19 Sep 2019, 9 a.m. Alex Molcan 1.5 2.61 Filip Cristian Jianu
42%
58%
Profile   Price Hist   Attributes
19 Sep 2019, 10 a.m. Federico Coria 1.21 4.55 Vit Kopriva
73%
27%
Profile   Price Hist   Attributes
Completed Blaz Rola 6-3 4-6 6-3 Fabien Reboul
69%
31%
Profile   Attributes
Completed Dmitry Popko 1-6 2-6 Zdenek Kolar
58%
42%
Profile   Attributes
Completed Nino Serdarusic 6-3 6-4 Tallon Griekspoor
31%
69%
Profile   Attributes
Completed Petr Nouza 3-6 1-6 Pedro Sousa
12%
88%
Profile   Attributes
Completed Bernabe Zapata Miralles 2-6 4-6 Nikolas Sanchez-Izquierdo
60%
40%
Profile   Attributes
Jelle Sels - - Pedro Martinez Portero
32%
68%
Profile   Attributes
Laurent Lokoli - - Attila Balazs
37%
63%
Profile   Attributes
Mens - St. Petersburg Open - St. Petersburg (I.hard)
Date Player 1 Player 1 Odds Player 2 Odds Player 2
In play Salvatore Caruso In play In play Thomas Fabbiano
47%
53%
Profile   Price Hist   Attributes
In play Damir Dzumhur In play In play Mikhail Kukushkin
48%
52%
Profile   Price Hist   Attributes
18 Sep 2019, 4:35 p.m. Alexander Bublik 1.79 2.24 Casper Ruud
42%
58%
Profile   Price Hist   Attributes
18 Sep 2019, 6 p.m. Egor Gerasimov 2.28 1.74 Adrian Mannarino
47%
53%
Profile   Price Hist   Attributes
19 Sep 2019, noon Roberto Carballes Baena 4.7 1.25 Matteo Berrettini
24%
76%
Profile   Price Hist   Attributes
19 Sep 2019, 4:30 p.m. Evgeny Donskoy 7.6 1.14 Daniil Medvedev
19%
81%
Profile   Price Hist   Attributes
19 Sep 2019, 5:30 p.m. Andrey Rublev 1.41 3.32 Richard Berankis
67%
33%
Profile   Price Hist   Attributes
Completed Marton Fucsovics 7-5 6-1 Alexey Vatutin
67%
33%
Profile   Attributes
Completed Joao Sousa 6-2 6-3 Jozef Kovalik
58%
42%
Profile   Attributes
Womens - Guangzhou Open - Guangzhou (Hard)
Date Player 1 Player 1 Odds Player 2 Odds Player 2
In play Elina Svitolina In play In play Marie Bouzkova
69%
31%
Profile   Price Hist   Attributes
18 Sep 2019, 4 p.m. Aleksandra Krunic 3.46 1.37 Anna Blinkova
31%
69%
Profile   Price Hist   Attributes
Completed Kateryna Kozlova 2-6 4-6 Shuai Zhang
52%
48%
Profile   Attributes
Completed Bernarda Pera 4-6 6-1 (5)6-7 Samantha Stosur
55%
45%
Profile   Attributes
Completed Sofia Kenin 6-4 6-2 Katarina Zavatska
85%
15%
Profile   Attributes
Completed Viktorija Golubic 4-6 7-5 6-0 Katerina Siniakova
40%
60%
Profile   Attributes
Completed Saisai Zheng 5-7 6-3 5-7 Jasmine Paolini
72%
28%
Profile   Attributes
Completed Nina Stojanovic 7-5 7-6(4) Magdalena Frech
53%
47%
Profile   Attributes
Womens - Korea Open - Seoul (Hard)
Date Player 1 Player 1 Odds Player 2 Odds Player 2
19 Sep 2019, 4 a.m. Priscilla Hon 3.88 1.32 Ajla Tomljanovic
37%
63%
Profile   Price Hist   Attributes
19 Sep 2019, 5 a.m. Timea Babos 2.83 1.51 Karolina Muchova
40%
60%
Profile   Price Hist   Attributes
19 Sep 2019, 6 a.m. Patricia Maria Tig 2.1 1.85 Paula Badosa Gibert
53%
47%
Profile   Price Hist   Attributes
Completed Ysaline Bonaventure 6-4 3-6 0-6 Yafan Wang
37%
63%
Profile   Attributes
Completed Margarita Gasparyan 3-6 5-7 Kirsten Flipkens
40%
60%
Profile   Attributes
Completed Anastasia Potapova 5-7 (4)6-7 Magda Linette
27%
73%
Profile   Attributes
Completed Kristie Ahn 0-6 6-4 7-6(2) Ana Bogdan
63%
37%
Profile   Attributes
Completed Ekaterina Alexandrova 3-6 6-3 6-4 Kristyna Pliskova
56%
44%
Profile   Attributes
Womens - Toray Pan Pacific Open - Osaka (Hard)
Date Player 1 Player 1 Odds Player 2 Odds Player 2
19 Sep 2019, 4 a.m. Sloane Stephens 2.1 1.83 Camila Giorgi
53%
47%
Profile   Price Hist   Attributes
19 Sep 2019, 5:30 a.m. Yulia Putintseva 1.32 4.05 Varvara Flink
68%
32%
Profile   Price Hist   Attributes
19 Sep 2019, 6:30 a.m. Donna Vekic 1.3 4.0 Misaki Doi
63%
37%
Profile   Price Hist   Attributes
19 Sep 2019, 9:30 a.m. Anastasia Pavlyuchenkova 2.42 1.68 Kiki Bertens
44%
56%
Profile   Price Hist   Attributes
Completed Zarina Diyas 7-5 0-6 4-6 Madison Keys
29%
71%
Profile   Attributes
Completed Viktoriya Tomova 5-7 3-6 Naomi Osaka
27%
73%
Profile   Attributes
Completed Elise Mertens 6-3 1-6 6-2 Su-Wei Hsieh
65%
35%
Profile   Attributes
Completed Angelique Kerber 6-2 6-4 Nicole Gibbs
74%
26%
Profile   Attributes

About us

PROVIDING DATA-LED FORESIGHT ON PROFESSIONAL TENNIS MATCHES

Our purpose is to provide Tennis Brainers the tools and insights to make smarter decisions on their tennis trades. Our models compile data on a huge range of key attributes to unlock hidden advantages and isolate value in the betting markets. From surface, form, age, previous meetings, service speed, to height and weight, we collect and construct hundreds of features that are systematically utilised to build the most accurate predictions on the market. With player level attributes, live pricing and insightful visualisations on meaningful statistics, Tennis Brain adds the power of data and machine learning to tennis trading.

Our algorithms are constantly updated to incorporate new data feeds and trends to consistently achieve returns as the markets and tour evolve. All our predictions are currently free to access, and we are developing to further expand our markets to include set and game scores, providing additional and potentially bigger opportunities within the tennis betting markets. We hope you enjoy the site and make the most of what we believe is an essential tool to successful tennis trading.


Match Predictions

Utilising complex data processing and exhaustive mathematical modelling

Sets & Games Predictions

Currently in development, expanding our service into further markets

Value Identification

Through analysing betting prices across scheduled matches