National Basketball Association Player Salary Prediction Using Supervised Machine Learning Methods

Emirhan Özbalta; Mücahit Yavuz; Tolga Kaya

doi:10.1007/978-3-030-85577-2_22

National Basketball Association Player Salary Prediction Using Supervised Machine Learning Methods

Emirhan Özbalta^*, Mücahit Yavuz, Tolga Kaya

^*Bu çalışma için yazışmadan sorumlu yazar

İşletme Mühendisliği Bölümü

Istanbul Technical University

Araştırma sonucu: ???type-name??? › Konferans katkısı › bilirkişi

Özet

Basketball is one of the most popular sports in the world and National Basketball Association (NBA) is the main figure for it. With the purpose of sustaining the balance between the basketball teams in the league, salary cap is implemented for all the teams in NBA. Considering the salary cap, decision makers of basketball teams should be careful while spending their budget. Since there are no transfer fees in NBA, salaries are the main expense for basketball teams. Therefore, determining the salaries of basketball players while making contracts is crucial to compose the best possible basketball team. In this research, dataset from the NBA 2K20 MyTeam video game and NBA players’ performance statistics of 2019–2020 season will be combined to predict the salaries for new contracts of NBA players by using machine learning methods. Shrinkage methods will be used to select best subsets. After, regression and decision tree models will be used to see which one produces the best mean squared error values. Results show that predicted salaries are very close to the new contract salaries of NBA players.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference
Editörler	Cengiz Kahraman, Selcuk Cebi, Sezi Cevik Onar, Basar Oztaysi, A. Cagri Tolga, Irem Ucal Sari
Yayınlayan	Springer Science and Business Media Deutschland GmbH
Sayfalar	189-196
Sayfa sayısı	8
ISBN (Basılı)	9783030855765
DOI'lar	https://doi.org/10.1007/978-3-030-85577-2_22
Yayın durumu	Yayınlandı - 2022
Etkinlik	International Conference on Intelligent and Fuzzy Systems, INFUS 2021 - Istanbul, Turkey Süre: 24 Ağu 2021 → 26 Ağu 2021

Yayın serisi

Adı	Lecture Notes in Networks and Systems
Hacim	308
ISSN (Basılı)	2367-3370
ISSN (Elektronik)	2367-3389

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	International Conference on Intelligent and Fuzzy Systems, INFUS 2021
Ülke/Bölge	Turkey
Şehir	Istanbul
Periyot	24/08/21 → 26/08/21

Bibliyografik not

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Belgeye Erişim

10.1007/978-3-030-85577-2_22

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Özbalta, E., Yavuz, M., & Kaya, T. (2022). National Basketball Association Player Salary Prediction Using Supervised Machine Learning Methods. In C. Kahraman, S. Cebi, S. Cevik Onar, B. Oztaysi, A. C. Tolga, & I. U. Sari (Eds.), Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference (pp. 189-196). (Lecture Notes in Networks and Systems; Hac. 308). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-85577-2_22

Özbalta, Emirhan ; Yavuz, Mücahit ; Kaya, Tolga. / National Basketball Association Player Salary Prediction Using Supervised Machine Learning Methods. Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference. editör / Cengiz Kahraman ; Selcuk Cebi ; Sezi Cevik Onar ; Basar Oztaysi ; A. Cagri Tolga ; Irem Ucal Sari. Springer Science and Business Media Deutschland GmbH, 2022. pp. 189-196 (Lecture Notes in Networks and Systems).

@inproceedings{0fcd8d95039f4baebf459292bcd2ff9d,

title = "National Basketball Association Player Salary Prediction Using Supervised Machine Learning Methods",

abstract = "Basketball is one of the most popular sports in the world and National Basketball Association (NBA) is the main figure for it. With the purpose of sustaining the balance between the basketball teams in the league, salary cap is implemented for all the teams in NBA. Considering the salary cap, decision makers of basketball teams should be careful while spending their budget. Since there are no transfer fees in NBA, salaries are the main expense for basketball teams. Therefore, determining the salaries of basketball players while making contracts is crucial to compose the best possible basketball team. In this research, dataset from the NBA 2K20 MyTeam video game and NBA players{\textquoteright} performance statistics of 2019–2020 season will be combined to predict the salaries for new contracts of NBA players by using machine learning methods. Shrinkage methods will be used to select best subsets. After, regression and decision tree models will be used to see which one produces the best mean squared error values. Results show that predicted salaries are very close to the new contract salaries of NBA players.",

keywords = "Basketball, Machine learning, NBA, Random forest, Sports analytics, Supervised learning",

author = "Emirhan {\"O}zbalta and M{\"u}cahit Yavuz and Tolga Kaya",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; International Conference on Intelligent and Fuzzy Systems, INFUS 2021 ; Conference date: 24-08-2021 Through 26-08-2021",

year = "2022",

doi = "10.1007/978-3-030-85577-2_22",

language = "English",

isbn = "9783030855765",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "189--196",

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

booktitle = "Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference",

address = "Germany",

}

Özbalta, E, Yavuz, M & Kaya, T 2022, National Basketball Association Player Salary Prediction Using Supervised Machine Learning Methods. in C Kahraman, S Cebi, S Cevik Onar, B Oztaysi, AC Tolga & IU Sari (eds), Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference. Lecture Notes in Networks and Systems, hac. 308, Springer Science and Business Media Deutschland GmbH, pp. 189-196, International Conference on Intelligent and Fuzzy Systems, INFUS 2021, Istanbul, Turkey, 24/08/21. https://doi.org/10.1007/978-3-030-85577-2_22

National Basketball Association Player Salary Prediction Using Supervised Machine Learning Methods. / Özbalta, Emirhan; Yavuz, Mücahit; Kaya, Tolga.
Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference. ed. / Cengiz Kahraman; Selcuk Cebi; Sezi Cevik Onar; Basar Oztaysi; A. Cagri Tolga; Irem Ucal Sari. Springer Science and Business Media Deutschland GmbH, 2022. p. 189-196 (Lecture Notes in Networks and Systems; Hac. 308).

Araştırma sonucu: ???type-name??? › Konferans katkısı › bilirkişi

TY - GEN

T1 - National Basketball Association Player Salary Prediction Using Supervised Machine Learning Methods

AU - Özbalta, Emirhan

AU - Yavuz, Mücahit

AU - Kaya, Tolga

PY - 2022

Y1 - 2022

N2 - Basketball is one of the most popular sports in the world and National Basketball Association (NBA) is the main figure for it. With the purpose of sustaining the balance between the basketball teams in the league, salary cap is implemented for all the teams in NBA. Considering the salary cap, decision makers of basketball teams should be careful while spending their budget. Since there are no transfer fees in NBA, salaries are the main expense for basketball teams. Therefore, determining the salaries of basketball players while making contracts is crucial to compose the best possible basketball team. In this research, dataset from the NBA 2K20 MyTeam video game and NBA players’ performance statistics of 2019–2020 season will be combined to predict the salaries for new contracts of NBA players by using machine learning methods. Shrinkage methods will be used to select best subsets. After, regression and decision tree models will be used to see which one produces the best mean squared error values. Results show that predicted salaries are very close to the new contract salaries of NBA players.

AB - Basketball is one of the most popular sports in the world and National Basketball Association (NBA) is the main figure for it. With the purpose of sustaining the balance between the basketball teams in the league, salary cap is implemented for all the teams in NBA. Considering the salary cap, decision makers of basketball teams should be careful while spending their budget. Since there are no transfer fees in NBA, salaries are the main expense for basketball teams. Therefore, determining the salaries of basketball players while making contracts is crucial to compose the best possible basketball team. In this research, dataset from the NBA 2K20 MyTeam video game and NBA players’ performance statistics of 2019–2020 season will be combined to predict the salaries for new contracts of NBA players by using machine learning methods. Shrinkage methods will be used to select best subsets. After, regression and decision tree models will be used to see which one produces the best mean squared error values. Results show that predicted salaries are very close to the new contract salaries of NBA players.

KW - Basketball

KW - Machine learning

KW - NBA

KW - Random forest

KW - Sports analytics

KW - Supervised learning

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U2 - 10.1007/978-3-030-85577-2_22

DO - 10.1007/978-3-030-85577-2_22

M3 - Conference contribution

AN - SCOPUS:85115265346

SN - 9783030855765

T3 - Lecture Notes in Networks and Systems

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EP - 196

BT - Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference

A2 - Kahraman, Cengiz

A2 - Cebi, Selcuk

A2 - Cevik Onar, Sezi

A2 - Oztaysi, Basar

A2 - Tolga, A. Cagri

A2 - Sari, Irem Ucal

PB - Springer Science and Business Media Deutschland GmbH

T2 - International Conference on Intelligent and Fuzzy Systems, INFUS 2021

Y2 - 24 August 2021 through 26 August 2021

ER -

Özbalta E, Yavuz M, Kaya T. National Basketball Association Player Salary Prediction Using Supervised Machine Learning Methods. In Kahraman C, Cebi S, Cevik Onar S, Oztaysi B, Tolga AC, Sari IU, editörler, Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation - Proceedings of the INFUS 2021 Conference. Springer Science and Business Media Deutschland GmbH. 2022. p. 189-196. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-030-85577-2_22

National Basketball Association Player Salary Prediction Using Supervised Machine Learning Methods

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