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Application and Development of the Expected Goals for Hockey Player Evaluation. / Shmakov, N.; J.S., Dong (Editor); M., Izadi (Editor); Z., Hou (Editor).

1st International Sports Analytics Conference and Exhibition, ISACE 2024. Springer Nature, 2024. p. 235-248 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14794 LNCS).

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

Harvard

Shmakov, N, J.S., D (ed.), M., I (ed.) & Z., H (ed.) 2024, Application and Development of the Expected Goals for Hockey Player Evaluation. in 1st International Sports Analytics Conference and Exhibition, ISACE 2024. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14794 LNCS, Springer Nature, pp. 235-248, The International Sports Analytics Conference and Exhibition 2024, Париж, France, 12/07/24. https://doi.org/10.1007/978-3-031-69073-0_21

APA

Shmakov, N., J.S., D. (Ed.), M., I. (Ed.), & Z., H. (Ed.) (2024). Application and Development of the Expected Goals for Hockey Player Evaluation. In 1st International Sports Analytics Conference and Exhibition, ISACE 2024 (pp. 235-248). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14794 LNCS). Springer Nature. https://doi.org/10.1007/978-3-031-69073-0_21

Vancouver

Shmakov N, J.S. D, (ed.), M. I, (ed.), Z. H, (ed.). Application and Development of the Expected Goals for Hockey Player Evaluation. In 1st International Sports Analytics Conference and Exhibition, ISACE 2024. Springer Nature. 2024. p. 235-248. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-031-69073-0_21

Author

Shmakov, N. ; J.S., Dong (Editor) ; M., Izadi (Editor) ; Z., Hou (Editor). / Application and Development of the Expected Goals for Hockey Player Evaluation. 1st International Sports Analytics Conference and Exhibition, ISACE 2024. Springer Nature, 2024. pp. 235-248 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

BibTeX

@inproceedings{6fc12d27cadf441eab0b47fcfe3782dd,
title = "Application and Development of the Expected Goals for Hockey Player Evaluation",
abstract = "Hockey clubs are currently subject to strict budgetary and regulatory restrictions, which increase competition and struggle for the best players, according to the ratio between expected salaries and final utility to the team. These restrictions force clubs to evaluate the effectiveness of player contracts and continuously benchmark them with the transfer market to find reinforcements. This article aims to create a framework for evaluating any hockey player in any professional league. The built framework is based on the Expected Goals model (XG). The XG model calculates the probability of each shot being a goal by solving a binary probabilistic classification task. The final rating is calculated using the explainable and auditable Expected Goals on Target model (XGoT), which complements the classical Expected Goals model by adding information about points on the gate plane for the shots on goal. This introduces the realization factor into the model. This article expands the discussion of the XG methodology for evaluating player efficiency and puts forward a new way of player evaluation in ice hockey. The traditional approach of rating creation in hockey has been strengthened by the implementation of the opposition level that balances the game in equal strengths, power play, and penalty kill to assemble an overall rating. The resulting fully interpretable framework facilitates stakeholders to make transfer decisions based on reliable analytics. {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.",
keywords = "Expected Goals, Player Efficiency, Player Evaluation, Salary Fund, Transfer Policy, Earnings, Efficiency, Sports, Wages, Classification tasks, Expected goal, Goal models, Ice hockey, Player efficiency, Player evaluation, Probabilistic classification, Salary fund, Target model, Transfer policies, Budget control",
author = "N. Shmakov and Dong J.S. and Izadi M. and Hou Z.",
note = "Код конференции: 320569 Export Date: 21 October 2024 Адрес для корреспонденции: Shmakov, N.; Centre for Econometrics and Business Analytics (CEBA), 7/9 Universitetskaya nab., Russian Federation; эл. почта: st095555@student.spbu.ru; null ; Conference date: 12-07-2024 Through 13-07-2024",
year = "2024",
doi = "10.1007/978-3-031-69073-0_21",
language = "Английский",
isbn = "9783031690723",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "235--248",
booktitle = "1st International Sports Analytics Conference and Exhibition, ISACE 2024",
address = "Германия",
url = "https://formal-analysis.com/isace/2024/",

}

RIS

TY - GEN

T1 - Application and Development of the Expected Goals for Hockey Player Evaluation

AU - Shmakov, N.

A2 - J.S., Dong

A2 - M., Izadi

A2 - Z., Hou

N1 - Conference code: 1

PY - 2024

Y1 - 2024

N2 - Hockey clubs are currently subject to strict budgetary and regulatory restrictions, which increase competition and struggle for the best players, according to the ratio between expected salaries and final utility to the team. These restrictions force clubs to evaluate the effectiveness of player contracts and continuously benchmark them with the transfer market to find reinforcements. This article aims to create a framework for evaluating any hockey player in any professional league. The built framework is based on the Expected Goals model (XG). The XG model calculates the probability of each shot being a goal by solving a binary probabilistic classification task. The final rating is calculated using the explainable and auditable Expected Goals on Target model (XGoT), which complements the classical Expected Goals model by adding information about points on the gate plane for the shots on goal. This introduces the realization factor into the model. This article expands the discussion of the XG methodology for evaluating player efficiency and puts forward a new way of player evaluation in ice hockey. The traditional approach of rating creation in hockey has been strengthened by the implementation of the opposition level that balances the game in equal strengths, power play, and penalty kill to assemble an overall rating. The resulting fully interpretable framework facilitates stakeholders to make transfer decisions based on reliable analytics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

AB - Hockey clubs are currently subject to strict budgetary and regulatory restrictions, which increase competition and struggle for the best players, according to the ratio between expected salaries and final utility to the team. These restrictions force clubs to evaluate the effectiveness of player contracts and continuously benchmark them with the transfer market to find reinforcements. This article aims to create a framework for evaluating any hockey player in any professional league. The built framework is based on the Expected Goals model (XG). The XG model calculates the probability of each shot being a goal by solving a binary probabilistic classification task. The final rating is calculated using the explainable and auditable Expected Goals on Target model (XGoT), which complements the classical Expected Goals model by adding information about points on the gate plane for the shots on goal. This introduces the realization factor into the model. This article expands the discussion of the XG methodology for evaluating player efficiency and puts forward a new way of player evaluation in ice hockey. The traditional approach of rating creation in hockey has been strengthened by the implementation of the opposition level that balances the game in equal strengths, power play, and penalty kill to assemble an overall rating. The resulting fully interpretable framework facilitates stakeholders to make transfer decisions based on reliable analytics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

KW - Expected Goals

KW - Player Efficiency

KW - Player Evaluation

KW - Salary Fund

KW - Transfer Policy

KW - Earnings

KW - Efficiency

KW - Sports

KW - Wages

KW - Classification tasks

KW - Expected goal

KW - Goal models

KW - Ice hockey

KW - Player efficiency

KW - Player evaluation

KW - Probabilistic classification

KW - Salary fund

KW - Target model

KW - Transfer policies

KW - Budget control

UR - https://www.mendeley.com/catalogue/f9c09c32-53b4-3225-9826-8c0d45de28d7/

U2 - 10.1007/978-3-031-69073-0_21

DO - 10.1007/978-3-031-69073-0_21

M3 - статья в сборнике материалов конференции

SN - 9783031690723

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 235

EP - 248

BT - 1st International Sports Analytics Conference and Exhibition, ISACE 2024

PB - Springer Nature

Y2 - 12 July 2024 through 13 July 2024

ER -

ID: 126221107