A data-driven approach to predicting the most valuable player in a game

被引:2
作者
Romero, Francisco P. [1 ]
Lozano-Murcia, Catalina [1 ]
Lopez-Gomez, Julio A. [1 ]
Angulo Sanchez-Herrera, Eusebio [2 ]
Sanchez-Lopez, Eduardo [1 ]
机构
[1] Univ Castilla La Mancha, Dept Informat Technol & Syst, Ciudad Real 13071, Spain
[2] Univ Castilla La Mancha, Dept Math, Ciudad Real, Spain
关键词
handball; meta-heuristics; most valuable player; optimization; principal component analysis; sports analytics; OPTIMIZATION; FRAMEWORK; SELECTION;
D O I
10.1002/cmm4.1155
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
The identification of outstanding behaviors is a matter of essential importance in sports analytics. However, analyzing how human experts select each match's most valuable player (MVP) according to objective and subjective factors is a great challenge. This article proposes a data-driven approach for sports team performance based on the weighted aggregation of statistical indicators. The proposal is divided into two approaches: The first conducts a principal component analysis to examine the relationship between each game's statistical indicators. The other addresses a meta-heuristic analysis to weight the attributes and choose the MVPs optimally. Finally, we apply the proposed approach to the 2018 European Men's Handball Championship and take the "Player of the Match" of each game as an example to illustrate its usefulness and efficacy. We perform multiple analyses, including a comparison with a fuzzy multi-criteria decision-making method that show that the data-driven approach can predict the "Player of the Match" in most matches. It also allows us to estimate and quantify the expert evaluations, which are often difficult to obtain in a disaggregated form.
引用
收藏
页数:11
相关论文
共 50 条
[41]   Hybrid Hydrological Data-Driven Approach for Daily Streamflow Forecasting [J].
Ghaith, Maysara ;
Siam, Ahmad ;
Li, Zhong ;
El-Dakhakhni, Wael .
JOURNAL OF HYDROLOGIC ENGINEERING, 2020, 25 (02)
[42]   A data-driven approach to increasing the lifetime of IoT sensor nodes [J].
Suryavansh, Shikhar ;
Benna, Abu ;
Guest, Chris ;
Chaterji, Somali .
SCIENTIFIC REPORTS, 2021, 11 (01)
[43]   A data-driven approach for optimal operational and financial commodity hedging [J].
Rettinger, Moritz ;
Mandl, Christian ;
Minner, Stefan .
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2024, 316 (01) :341-360
[44]   Depth analysis of battery performance based on a data-driven approach [J].
Zhang, Zhen ;
Sun, Hongrui ;
Sun, Hui .
ELECTROCHIMICA ACTA, 2024, 474
[45]   A data-driven approach to optimized medication dosing: a focus on heparin [J].
Mohammad M. Ghassemi ;
Stefan E. Richter ;
Ifeoma M. Eche ;
Tszyi W. Chen ;
John Danziger ;
Leo A. Celi .
Intensive Care Medicine, 2014, 40 :1332-1339
[46]   A Data-Driven Approach for Ancillary Bundle Recommendation to Segmented Users [J].
Swami, Aakash ;
Gugulothu, Narendhar ;
Tirumala, V ;
Bhat, Sanjay .
2024 IEEE INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS, ICDMW, 2024, :606-612
[47]   A data-driven approach to optimizing clinical study eligibility criteria [J].
Fang, Yilu ;
Liu, Hao ;
Idnay, Betina ;
Ta, Casey ;
Marder, Karen ;
Weng, Chunhua .
JOURNAL OF BIOMEDICAL INFORMATICS, 2023, 142
[48]   The scenario approach: A tool at the service of data-driven decision making [J].
Campi, M. C. ;
Care, A. ;
Garatti, S. .
ANNUAL REVIEWS IN CONTROL, 2021, 52 :1-17
[49]   Predictive chiller operation: A data-driven loading and scheduling approach [J].
Sala-Cardoso, Enric ;
Delgado-Prieto, Miguel ;
Kampouropoulos, Konstantinos ;
Romeral, Luis .
ENERGY AND BUILDINGS, 2020, 208
[50]   A data-driven Bayesian approach for optimal dynamic product transitions [J].
Flores-Tlacuahuac, Antonio ;
Fuentes-Cortes, Luis Fabian .
AICHE JOURNAL, 2024, 70 (06)