Player Performance Prediction in Football Game

Pariath, Richard; Shah, Shailin; Surve, Aditya; Mittal, Jayashri

doi:10.1109/iceca.2018.8474750

Cited by 14 publications

(6 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In [11], the authors analyze the multiple factors or aspects that could be recruited to objectively evaluate a player in football, no matter grassroot players or high-level players. The mentioned factors do not include the traditional factors at club levels, such as player transfer, squad formation and strategic planning.…”

Section: Related Workmentioning

confidence: 99%

Correlation-Aware Sport Training Evaluation for Players With Trust Based on Mahalanobis Distance

Fan

Tian²,

et al. 2022

IEEE Access

View full text Add to dashboard Cite

With the widely-adopted idea of health and longevity, sports have been becoming one of the most popular entertainment ways of the public. For the majority of sports, players need to know about their concrete physical conditions in a real time manner so as to pursue a good sport score or ranking in a competition or a race. Generally, we can achieve the above goal through analyzing and evaluating the daily training scores of each player. However, there are often multiple physical trainings for players and various correlations are existent among them, which significantly decrease the fairness and trust of player training score evaluation and ranking since traditional multi-dimensional data integration solutions are often based on a strong hypothesis, i.e., the involved multiple dimensions are independent with each other. In view of this shortcoming, we introduce the Mahalanobis Distance into the multi-dimensional player training score evaluation and further propose a correlation-aware player training score evaluation method with trust (abbreviated as CPEMD) based on Mahalanobis Distance. As Mahalanobis Distance can eliminate the hidden linear correlations among the involved multiple dimensions, we can guarantee the fairness and trust of Mahalanobis Distance-based player training score evaluation and ranking results. At last, we use a case study to show the feasibility of CPEMD in this paper.

show abstract

Section: Related Workmentioning

confidence: 99%

Correlation-Aware Sport Training Evaluation for Players With Trust Based on Mahalanobis Distance

Fan

Tian²,

et al. 2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Diversas possibilidades foram destacadas por [Pariath et al 2018], [Ruiz et al 2017] e [Kempe et al 2018] no uso de Sport Analytics: auxiliar equipes, treinadores, gerentes de esporte na contratação de atletas; verificação de qualidades e defeitos de equipes adversárias direcionando tática de treinamentos e atuações durante as competições; validar se um determinado atleta corresponde ao salário pago ou verificar qual atleta deveria ser contratado para aumentar a qualidade e eficácia do time.…”

Section: Foco Na Equipeunclassified

“…Já os autores [Jelinek et al 2014], [Nagarajan and Li 2018] e [Pariath et al 2018] focam na capacidade de cada atleta de contribuir para o desempenho do time, como acerto de passes no futebol ou quantidades de assistências e rebotes no caso do basquete, enquanto [Ruiz et al 2017] e [Kempe et al 2018] estudam qual o desempenho geral das equipes.…”

Section: Foco Na Equipeunclassified

“…Para [Aoki et al 2017] vários fatores favorecem a utilização de esportes que tenham confederações ou ligas bem definidas: primeiramente porque ligas formam sistemas Para o futebol foi verificado que os objetivos estavam equilibrados entre Previsão de Resultados estudados por [Aoki et al 2017], [Fernandez et al 2016], [Dubbs 2018] e [Adam 2016] e Foco na Equipe [Ruiz et al 2017], [Kempe et al 2018], [Cintia et al 2015] e [Pariath et al 2018], enquanto apenas dois tinham maior interesse em melhorias individuais dos atletas [Naglah et al 2018]…”

Section: Por Esporteunclassified

See 1 more Smart Citation

Sport Analytics A Systematic Review

Bogo

Oliveira

Lima

et al. 2019

Anais Do XVI Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2019)

View full text Add to dashboard Cite

Ciência de Dados é uma das áreas da Tecnologia de Informação em maior desenvolvimento, sendo usada nos mais diversos ramos, impactando fortemente a competitividade das empresas e a forma como as pessoas interagem. O seu uso nos esportes é designado como Sport Analytics, sendo usado para entender padrões, definir táticas/treinamentos e prever resultados. Este artigo procura resumir as pesquisas que estão sendo realizadas neste campo, bem como entender o conhecimento gerado e como ele está sendo aplicado.

show abstract

“…Nsolo et al (2018) analysed and compared data on players in the Big-5 leagues, screened out the attributes and outstanding techniques of players at different positions, and used machine learning to predict and evaluate players’ performance. Pariath et al (2018) designed a computer system that could evaluate various attributes and skills of football players, and the core of the evaluation scale depended on the position of football players and the skills they had. Brooks et al (2016) developed the PlayeRank program, a data-driven algorithm that could provide multidimensional and positional stratification for football players’ performance evaluation.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Playing Time Benefits of Foreign Players in the Big-5 European Football Leagues

Zhang

Jiang

2022

Journal of Human Kinetics

View full text Add to dashboard Cite

To explore the benefits that foreign players bring to their clubs, this study used foreign players in the Big-5 European leagues (2013/2014-2017/2018 seasons) as samples and constructed a benefit model based on playing time and game points to evaluate the contribution of foreign players to their clubs in different leagues. The results showed the following: 1) from the 2013/2014 season to the 2017/2018 season, foreign players in the Bundesliga had the highest playing time benefits (PTBs) (0.526 ± 0.012), followed by foreign players in La Liga (0.523 ± 0.014), the Premier League (0.518 ± 0.011), Serie A (0.500 ± 0.012) and Ligue 1 (0.486 ± 0.011); 2) foreign players from South America had the highest PTBs in the Big-5 leagues, while those from Africa had the lowest PTBs. However, among the different leagues, there were no significant differences in the PTBs of foreign players from the same continent; 3) PTBs of foreign players in the forward position were lower than those of foreign players in the defender position; 4) the country that produced foreign players in the Big-5 leagues with the highest total PTBs was Brazil, followed by Argentina, Spain and France. Additionally, the top 15 countries by total PTBs qualified for either the 2014 or the 2018 World Cup Final.

show abstract

Player Performance Prediction in Football Game

Cited by 14 publications

References 7 publications

Correlation-Aware Sport Training Evaluation for Players With Trust Based on Mahalanobis Distance

Correlation-Aware Sport Training Evaluation for Players With Trust Based on Mahalanobis Distance

Sport Analytics A Systematic Review

Evaluation of the Playing Time Benefits of Foreign Players in the Big-5 European Football Leagues

Contact Info

Product

Resources

About