Football player value assessment using machine learning techniques

Ahmet Talha Yiğit; Barış Samak; Tolga Kaya

doi:10.1007/978-3-030-23756-1_36

Football player value assessment using machine learning techniques

Ahmet Talha Yiğit^*, Barış Samak, Tolga Kaya

^*Corresponding author for this work

Department of Management Engineering

Istanbul Technical University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

7 Citations (Scopus)

Abstract

Sports analytics is a field that is growing in popularity and application throughout the world. One of the open problems in this field is the valuation of football players. The aim of this study is to establish a football player value assessment model using machine learning techniques to support transfer decisions of football clubs. The proposed models will mainly be based on the intrinsic features of the individual players which are provided in Football Manager video game. To do this, based on the individual statistics of 5316 players who are active in 11 different major leagues from Europe and South America, different value assessment models are conducted using advanced supervised learning techniques like ridge and lasso regressions, random forests and extreme gradient boosting. All the models have been built in R programming language. The performances of the models are compared based on their mean squared errors. An ensemble model with inflation is proposed as the output.

Original language	English
Title of host publication	Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference
Editors	Cengiz Kahraman, Sezi Cevik Onar, Basar Oztaysi, Irem Ucal Sari, Selcuk Cebi, A.Cagri Tolga
Publisher	Springer Verlag
Pages	289-297
Number of pages	9
ISBN (Print)	9783030237554
DOIs	https://doi.org/10.1007/978-3-030-23756-1_36
Publication status	Published - 2020
Event	International Conference on Intelligent and Fuzzy Systems, INFUS 2019 - Istanbul, Turkey Duration: 23 Jul 2019 → 25 Jul 2019

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	1029
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	International Conference on Intelligent and Fuzzy Systems, INFUS 2019
Country/Territory	Turkey
City	Istanbul
Period	23/07/19 → 25/07/19

Bibliographical note

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Keywords

Ensemble learning
Extreme gradient boosting
Football analytics
Lasso regression
Machine learning
Player value prediction

Access to Document

10.1007/978-3-030-23756-1_36

Cite this

Yiğit, A. T., Samak, B., & Kaya, T. (2020). Football player value assessment using machine learning techniques. In C. Kahraman, S. Cevik Onar, B. Oztaysi, I. U. Sari, S. Cebi, & A. C. Tolga (Eds.), Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference (pp. 289-297). (Advances in Intelligent Systems and Computing; Vol. 1029). Springer Verlag. https://doi.org/10.1007/978-3-030-23756-1_36

Yiğit, Ahmet Talha ; Samak, Barış ; Kaya, Tolga. / Football player value assessment using machine learning techniques. Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference. editor / Cengiz Kahraman ; Sezi Cevik Onar ; Basar Oztaysi ; Irem Ucal Sari ; Selcuk Cebi ; A.Cagri Tolga. Springer Verlag, 2020. pp. 289-297 (Advances in Intelligent Systems and Computing).

@inproceedings{73c897cd2dae47289fe1ad5e4857309d,

title = "Football player value assessment using machine learning techniques",

abstract = "Sports analytics is a field that is growing in popularity and application throughout the world. One of the open problems in this field is the valuation of football players. The aim of this study is to establish a football player value assessment model using machine learning techniques to support transfer decisions of football clubs. The proposed models will mainly be based on the intrinsic features of the individual players which are provided in Football Manager video game. To do this, based on the individual statistics of 5316 players who are active in 11 different major leagues from Europe and South America, different value assessment models are conducted using advanced supervised learning techniques like ridge and lasso regressions, random forests and extreme gradient boosting. All the models have been built in R programming language. The performances of the models are compared based on their mean squared errors. An ensemble model with inflation is proposed as the output.",

keywords = "Ensemble learning, Extreme gradient boosting, Football analytics, Lasso regression, Machine learning, Player value prediction",

author = "Yiğit, {Ahmet Talha} and Barı{\c s} Samak and Tolga Kaya",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; International Conference on Intelligent and Fuzzy Systems, INFUS 2019 ; Conference date: 23-07-2019 Through 25-07-2019",

year = "2020",

doi = "10.1007/978-3-030-23756-1_36",

language = "English",

isbn = "9783030237554",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer Verlag",

pages = "289--297",

editor = "Cengiz Kahraman and {Cevik Onar}, Sezi and Basar Oztaysi and Sari, {Irem Ucal} and Selcuk Cebi and A.Cagri Tolga",

booktitle = "Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference",

address = "Germany",

}

Yiğit, AT, Samak, B & Kaya, T 2020, Football player value assessment using machine learning techniques. in C Kahraman, S Cevik Onar, B Oztaysi, IU Sari, S Cebi & AC Tolga (eds), Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference. Advances in Intelligent Systems and Computing, vol. 1029, Springer Verlag, pp. 289-297, International Conference on Intelligent and Fuzzy Systems, INFUS 2019, Istanbul, Turkey, 23/07/19. https://doi.org/10.1007/978-3-030-23756-1_36

Football player value assessment using machine learning techniques. / Yiğit, Ahmet Talha; Samak, Barış; Kaya, Tolga.
Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference. ed. / Cengiz Kahraman; Sezi Cevik Onar; Basar Oztaysi; Irem Ucal Sari; Selcuk Cebi; A.Cagri Tolga. Springer Verlag, 2020. p. 289-297 (Advances in Intelligent Systems and Computing; Vol. 1029).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Football player value assessment using machine learning techniques

AU - Yiğit, Ahmet Talha

AU - Samak, Barış

AU - Kaya, Tolga

PY - 2020

Y1 - 2020

N2 - Sports analytics is a field that is growing in popularity and application throughout the world. One of the open problems in this field is the valuation of football players. The aim of this study is to establish a football player value assessment model using machine learning techniques to support transfer decisions of football clubs. The proposed models will mainly be based on the intrinsic features of the individual players which are provided in Football Manager video game. To do this, based on the individual statistics of 5316 players who are active in 11 different major leagues from Europe and South America, different value assessment models are conducted using advanced supervised learning techniques like ridge and lasso regressions, random forests and extreme gradient boosting. All the models have been built in R programming language. The performances of the models are compared based on their mean squared errors. An ensemble model with inflation is proposed as the output.

AB - Sports analytics is a field that is growing in popularity and application throughout the world. One of the open problems in this field is the valuation of football players. The aim of this study is to establish a football player value assessment model using machine learning techniques to support transfer decisions of football clubs. The proposed models will mainly be based on the intrinsic features of the individual players which are provided in Football Manager video game. To do this, based on the individual statistics of 5316 players who are active in 11 different major leagues from Europe and South America, different value assessment models are conducted using advanced supervised learning techniques like ridge and lasso regressions, random forests and extreme gradient boosting. All the models have been built in R programming language. The performances of the models are compared based on their mean squared errors. An ensemble model with inflation is proposed as the output.

KW - Ensemble learning

KW - Extreme gradient boosting

KW - Football analytics

KW - Lasso regression

KW - Machine learning

KW - Player value prediction

UR - http://www.scopus.com/inward/record.url?scp=85069507753&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-23756-1_36

DO - 10.1007/978-3-030-23756-1_36

M3 - Conference contribution

AN - SCOPUS:85069507753

SN - 9783030237554

T3 - Advances in Intelligent Systems and Computing

SP - 289

EP - 297

BT - Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference

A2 - Kahraman, Cengiz

A2 - Cevik Onar, Sezi

A2 - Oztaysi, Basar

A2 - Sari, Irem Ucal

A2 - Cebi, Selcuk

A2 - Tolga, A.Cagri

PB - Springer Verlag

T2 - International Conference on Intelligent and Fuzzy Systems, INFUS 2019

Y2 - 23 July 2019 through 25 July 2019

ER -

Yiğit AT, Samak B, Kaya T. Football player value assessment using machine learning techniques. In Kahraman C, Cevik Onar S, Oztaysi B, Sari IU, Cebi S, Tolga AC, editors, Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making - Proceedings of the INFUS 2019 Conference. Springer Verlag. 2020. p. 289-297. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-23756-1_36

Football player value assessment using machine learning techniques

Abstract

Publication series

Conference

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this