A physics-based algorithm to perform predictions in football leagues

Stock, Eduardo Velasco; Silva, Roberto da; Fernandes, Henrique A.

doi:10.1016/j.physa.2022.127532

Cited by 2 publications

(1 citation statement)

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“…Stock et al [19] extended a stochastic model for football leagues based on the team's potential for predicting the tournament's outcome by using the market value or/and the ongoing team's performance. An agent-based model was proposed in their work, where N teams play against each other, and the team's potential is incremented or decremented based on simulated championships.…”

Section: Background Literaturementioning

confidence: 99%

Tactically Maximize Game Advantage by Predicting Football Substitutions Using Machine Learning

Mohandas,

Ahsan,

Haider

2023

BDCC

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Football (also known as Soccer), boasts a staggering fan base of 3.5 billion individuals spread across 200 countries, making it the world’s most beloved sport. The widespread adoption of advanced technology in sports has become increasingly prominent, empowering players, coaches, and team management to enhance their performance and refine team strategies. Among these advancements, player substitution plays a crucial role in altering the dynamics of a match. However, due to the absence of proven methods or software capable of accurately predicting substitutions, these decisions are often based on instinct rather than concrete data. The purpose of this research is to explore the potential of employing machine learning algorithms to predict substitutions in Football, and how it could influence the outcome of a match. This study investigates the effect of timely and tactical substitutions in football matches and their influence on the match results. Machine learning techniques such as Logistic Regression (LR), Decision tree (DT), K-nearest Neighbor (KNN), Support Vector Machine (SVM), Multinomial Naïve Bayes (MNB), Random Forest (RF) classifiers were implemented and tested to develop models and to predict player substitutions. Relevant data was collected from the Kaggle dataset, which contains data of 51,738 substitutions from 9074 European league football matches in 5 leagues spanning 6 seasons. Machine learning models were trained and tested using an 80-20 data split and it was observed that RF model provided the best accuracy of over 70% and the best F1-score of 0.65 on the test set across all football leagues. SVM model achieved the best Precision of almost 0.8. However, the worst computation time of up to 2 min was consumed. LR showed some overfitting issues with 100% accuracy in the training set, but only 60% accuracy was obtained for the test set. To conclude, based on the time of substitution and match score-line, it was possible to predict the players who can be substituted, which can provide a match advantage. The achieved results provided an effective way to decide on player substitutions for both the team manager and coaches.

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Section: Background Literaturementioning

confidence: 99%