A game-predicting expert system using big data and machine learning

Gu, Wei; Foster, Krista; Shang, Jennifer; Wei, Lirong

doi:10.1016/j.eswa.2019.04.025

Cited by 66 publications

(35 citation statements)

References 25 publications

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“…Not only the basketball results are predicted using machine learning. In [ 7 ], the badminton scores are predicted, [ 8 ] concerns football football match scores, while [ 9 ] predicts the hockey results.…”

Section: Related Work and Existing Solutionsmentioning

confidence: 99%

Who Will Score? A Machine Learning Approach to Supporting Football Team Building and Transfers

Ćwiklinski

Giełczyk

Choraś

2021

Entropy

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Background: the machine learning (ML) techniques have been implemented in numerous applications, including health-care, security, entertainment, and sports. In this article, we present how the ML can be used for building a professional football team and planning player transfers. Methods: in this research, we defined numerous parameters for player assessment, and three definitions of a successful transfer. We used the Random Forest, Naive Bayes, and AdaBoost algorithms in order to predict the player transfer success. We used realistic, publicly available data in order to train and test the classifiers. Results: in the article, we present numerous experiments; they differ in the weights of parameters, the successful transfer definitions, and other factors. We report promising results (accuracy = 0.82, precision = 0.84, recall = 0.82, and F1-score = 0.83). Conclusion: the presented research proves that machine learning can be helpful in professional football team building. The proposed algorithm will be developed in the future and it may be implemented as a professional tool for football talent scouts.

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Section: Related Work and Existing Solutionsmentioning

confidence: 99%

Who Will Score? A Machine Learning Approach to Supporting Football Team Building and Transfers

Ćwiklinski

Giełczyk

Choraś

2021

Entropy

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“…5 Previous research has examined the application of machine learning techniques to the NHL. In 2019, Gu et al 13 described an expert system that used a support vector machine to predict game outcomes. In 2015, Demers 10 compared the performance of 2 Stanley Cup prediction systems using a relevance vector machine algorithm and a support vector machine algorithm.…”

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confidence: 99%

Machine Learning Outperforms Logistic Regression Analysis to Predict Next-Season NHL Player Injury: An Analysis of 2322 Players From 2007 to 2017

Luu

Wright

Haeberle

et al. 2020

Orthopaedic Journal of Sports Medicine

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Background: The opportunity to quantitatively predict next-season injury risk in the National Hockey League (NHL) has become a reality with the advent of advanced computational processors and machine learning (ML) architecture. Unlike static regression analyses that provide a momentary prediction, ML algorithms are dynamic in that they are readily capable of imbibing historical data to build a framework that improves with additive data. Purpose: To (1) characterize the epidemiology of publicly reported NHL injuries from 2007 to 2017, (2) determine the validity of a machine learning model in predicting next-season injury risk for both goalies and position players, and (3) compare the performance of modern ML algorithms versus logistic regression (LR) analyses. Study Design: Descriptive epidemiology study. Methods: Professional NHL player data were compiled for the years 2007 to 2017 from 2 publicly reported databases in the absence of an official NHL-approved database. Attributes acquired from each NHL player from each professional year included age, 85 performance metrics, and injury history. A total of 5 ML algorithms were created for both position player and goalie data: random forest, K Nearest Neighbors, Naïve Bayes, XGBoost, and Top 3 Ensemble. LR was also performed for both position player and goalie data. Area under the receiver operating characteristic curve (AUC) primarily determined validation. Results: Player data were generated from 2109 position players and 213 goalies. For models predicting next-season injury risk for position players, XGBoost performed the best with an AUC of 0.948, compared with an AUC of 0.937 for LR ( P < .0001). For models predicting next-season injury risk for goalies, XGBoost had the highest AUC with 0.956, compared with an AUC of 0.947 for LR ( P < .0001). Conclusion: Advanced ML models such as XGBoost outperformed LR and demonstrated good to excellent capability of predicting whether a publicly reportable injury is likely to occur the next season.

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“…Modelos para prever diretamente resultados foram estudados por [Fernandez et al 2016], [Gu et al 2019], [Young et al 2019] e [Dubbs 2018] levando em consideração o treino das equipes (quantidade, modelos), bem como do desempenho individual do atleta. Uma variação propostas [Hamdad et al 2018] foi avaliar o desempenho das equipes com base nos salários de cada jogador.…”

Section: Previsão De Resultadosunclassified

Sport Analytics A Systematic Review

Bogo

Oliveira

Lima

et al. 2019

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

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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.

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A game-predicting expert system using big data and machine learning

Cited by 66 publications

References 25 publications

Who Will Score? A Machine Learning Approach to Supporting Football Team Building and Transfers

Who Will Score? A Machine Learning Approach to Supporting Football Team Building and Transfers

Machine Learning Outperforms Logistic Regression Analysis to Predict Next-Season NHL Player Injury: An Analysis of 2322 Players From 2007 to 2017

Sport Analytics A Systematic Review

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