Using Soft Computing Techniques for Prediction of Winners in Tennis Matches

Mateus de Araujo Fernandes

doi:doi:10.11648/j.mlr.20170203.12

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Using Soft Computing Techniques for Prediction of Winners in Tennis Matches

Mateus de Araujo Fernandes

Published in Machine Learning Research (Volume 2, Issue 3)

Received: 24 February 2017 Accepted: 20 March 2017 Published: 10 April 2017

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Abstract

The forecast of winners in sports brings valuable information for both organizers, media and audience, and this is particularly important in tennis, where the results of a round in a tournament determine which matches will occur in the next round. With that in mind, this work presents a study of the main factors influencing matches predictability and, from this analysis, a new hybrid approach is proposed to calculate the chances of victory of each of the competitors before the start of a match. A Fuzzy Inference System, with its ability to reproduce knowledge of an expert among mixed information, a Neural Network, with the capability of features extraction from examples, and a Strength Equation with optimized weighting factors are the techniques employed. These predictors have as inputs data from previous performances of the players, which in this case try to capture their short, medium and long-term performances, as well as their affinity for the different types of surfaces. Subsequently the results from these predictors are combined by a voting system. The results are encouraging, showing significant gains when comparing to the use of the ATP ranking.

Published in	Machine Learning Research (Volume 2, Issue 3)
DOI	10.11648/j.mlr.20170203.12
Page(s)	86-98
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Artificial Intelligence, Forecast, Soft Computing

References

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APA Style

Mateus de Araujo Fernandes. (2017). Using Soft Computing Techniques for Prediction of Winners in Tennis Matches. Machine Learning Research, 2(3), 86-98. https://doi.org/10.11648/j.mlr.20170203.12

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ACS Style

Mateus de Araujo Fernandes. Using Soft Computing Techniques for Prediction of Winners in Tennis Matches. Mach. Learn. Res. 2017, 2(3), 86-98. doi: 10.11648/j.mlr.20170203.12

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AMA Style

Mateus de Araujo Fernandes. Using Soft Computing Techniques for Prediction of Winners in Tennis Matches. Mach Learn Res. 2017;2(3):86-98. doi: 10.11648/j.mlr.20170203.12

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@article{10.11648/j.mlr.20170203.12,
  author = {Mateus de Araujo Fernandes},
  title = {Using Soft Computing Techniques for Prediction of Winners in Tennis Matches},
  journal = {Machine Learning Research},
  volume = {2},
  number = {3},
  pages = {86-98},
  doi = {10.11648/j.mlr.20170203.12},
  url = {https://doi.org/10.11648/j.mlr.20170203.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20170203.12},
  abstract = {The forecast of winners in sports brings valuable information for both organizers, media and audience, and this is particularly important in tennis, where the results of a round in a tournament determine which matches will occur in the next round. With that in mind, this work presents a study of the main factors influencing matches predictability and, from this analysis, a new hybrid approach is proposed to calculate the chances of victory of each of the competitors before the start of a match. A Fuzzy Inference System, with its ability to reproduce knowledge of an expert among mixed information, a Neural Network, with the capability of features extraction from examples, and a Strength Equation with optimized weighting factors are the techniques employed. These predictors have as inputs data from previous performances of the players, which in this case try to capture their short, medium and long-term performances, as well as their affinity for the different types of surfaces. Subsequently the results from these predictors are combined by a voting system. The results are encouraging, showing significant gains when comparing to the use of the ATP ranking.},
 year = {2017}
}

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TY  - JOUR
T1  - Using Soft Computing Techniques for Prediction of Winners in Tennis Matches
AU  - Mateus de Araujo Fernandes
Y1  - 2017/04/10
PY  - 2017
N1  - https://doi.org/10.11648/j.mlr.20170203.12
DO  - 10.11648/j.mlr.20170203.12
T2  - Machine Learning Research
JF  - Machine Learning Research
JO  - Machine Learning Research
SP  - 86
EP  - 98
PB  - Science Publishing Group
SN  - 2637-5680
UR  - https://doi.org/10.11648/j.mlr.20170203.12
AB  - The forecast of winners in sports brings valuable information for both organizers, media and audience, and this is particularly important in tennis, where the results of a round in a tournament determine which matches will occur in the next round. With that in mind, this work presents a study of the main factors influencing matches predictability and, from this analysis, a new hybrid approach is proposed to calculate the chances of victory of each of the competitors before the start of a match. A Fuzzy Inference System, with its ability to reproduce knowledge of an expert among mixed information, a Neural Network, with the capability of features extraction from examples, and a Strength Equation with optimized weighting factors are the techniques employed. These predictors have as inputs data from previous performances of the players, which in this case try to capture their short, medium and long-term performances, as well as their affinity for the different types of surfaces. Subsequently the results from these predictors are combined by a voting system. The results are encouraging, showing significant gains when comparing to the use of the ATP ranking.
VL  - 2
IS  - 3
ER  -

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Author Information

Mateus de Araujo Fernandes

Federal Institute of Education, Science and Technology in Sergipe, Aracaju/SE, Brazil

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APA Style

Mateus de Araujo Fernandes. (2017). Using Soft Computing Techniques for Prediction of Winners in Tennis Matches. Machine Learning Research, 2(3), 86-98. https://doi.org/10.11648/j.mlr.20170203.12

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ACS Style

Mateus de Araujo Fernandes. Using Soft Computing Techniques for Prediction of Winners in Tennis Matches. Mach. Learn. Res. 2017, 2(3), 86-98. doi: 10.11648/j.mlr.20170203.12

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AMA Style

Mateus de Araujo Fernandes. Using Soft Computing Techniques for Prediction of Winners in Tennis Matches. Mach Learn Res. 2017;2(3):86-98. doi: 10.11648/j.mlr.20170203.12

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@article{10.11648/j.mlr.20170203.12,
  author = {Mateus de Araujo Fernandes},
  title = {Using Soft Computing Techniques for Prediction of Winners in Tennis Matches},
  journal = {Machine Learning Research},
  volume = {2},
  number = {3},
  pages = {86-98},
  doi = {10.11648/j.mlr.20170203.12},
  url = {https://doi.org/10.11648/j.mlr.20170203.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20170203.12},
  abstract = {The forecast of winners in sports brings valuable information for both organizers, media and audience, and this is particularly important in tennis, where the results of a round in a tournament determine which matches will occur in the next round. With that in mind, this work presents a study of the main factors influencing matches predictability and, from this analysis, a new hybrid approach is proposed to calculate the chances of victory of each of the competitors before the start of a match. A Fuzzy Inference System, with its ability to reproduce knowledge of an expert among mixed information, a Neural Network, with the capability of features extraction from examples, and a Strength Equation with optimized weighting factors are the techniques employed. These predictors have as inputs data from previous performances of the players, which in this case try to capture their short, medium and long-term performances, as well as their affinity for the different types of surfaces. Subsequently the results from these predictors are combined by a voting system. The results are encouraging, showing significant gains when comparing to the use of the ATP ranking.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Using Soft Computing Techniques for Prediction of Winners in Tennis Matches
AU  - Mateus de Araujo Fernandes
Y1  - 2017/04/10
PY  - 2017
N1  - https://doi.org/10.11648/j.mlr.20170203.12
DO  - 10.11648/j.mlr.20170203.12
T2  - Machine Learning Research
JF  - Machine Learning Research
JO  - Machine Learning Research
SP  - 86
EP  - 98
PB  - Science Publishing Group
SN  - 2637-5680
UR  - https://doi.org/10.11648/j.mlr.20170203.12
AB  - The forecast of winners in sports brings valuable information for both organizers, media and audience, and this is particularly important in tennis, where the results of a round in a tournament determine which matches will occur in the next round. With that in mind, this work presents a study of the main factors influencing matches predictability and, from this analysis, a new hybrid approach is proposed to calculate the chances of victory of each of the competitors before the start of a match. A Fuzzy Inference System, with its ability to reproduce knowledge of an expert among mixed information, a Neural Network, with the capability of features extraction from examples, and a Strength Equation with optimized weighting factors are the techniques employed. These predictors have as inputs data from previous performances of the players, which in this case try to capture their short, medium and long-term performances, as well as their affinity for the different types of surfaces. Subsequently the results from these predictors are combined by a voting system. The results are encouraging, showing significant gains when comparing to the use of the ATP ranking.
VL  - 2
IS  - 3
ER  -

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