Machine Learning-Based Identification of the Strongest Predictive Variables of Winning and Losing in Belgian Professional Soccer

Abstract

This study aimed to identify the strongest predictive variables of winning and losing in the highest Belgian soccer division. A predictive machine learning model based on a broad range of variables (n = 100) was constructed, using a dataset consisting of 576 games. To avoid multicollinearity and reduce dimensionality, Variance Inflation Factor (threshold of 5) and BorutaShap were respectively applied. A total of 13 variables remained and were used to predict winning or losing using Extreme Gradient Boosting. TreeExplainer was applied to determine feature importance on a global and local level. The model showed an accuracy of 89.6% ± 3.1% (precision: 88.9%; recall: 90.1%, f1-score: 89.5%), correctly classifying 516 out of 576 games. Shots on target from the attacking penalty box showed to be the best predictor. Several physical indicators are amongst the best predictors, as well as contextual variables such as ELO -ratings, added transfers value of the benched players and match location. The results show the added value of the inclusion of a broad spectrum of variables when predicting and evaluating game outcomes. Similar modelling approaches can be used by clubs to identify the strongest predictive variables for their leagues, and evaluate and improve their current quantitative analyses.

Identifying Modifiable Predictors of COVID-19 Vaccine Side Effects: A Machine Learning Approach

“…To prepare the input features for analysis, categorical variables were converted into dummy variables using OneHotEncoder, a scikit-learn (version 1.0.1) preprocessing package in Python. To avoid collinearity effect between the input variables, a Variance Inflation Factor analysis was conducted (threshold = 5) [ 26 ].…”

Cloud Affected Solar UV Prediction With Three-Phase Wavelet Hybrid Convolutional Long Short-Term Memory Network Multi-Step Forecast System

“…Feature selection approaches are essential components of the model designing phase to achieve the optimum performance of a forecast model. The Python-based BorutaShap algorithm remarkably eliminates irrelevant and largely redundant features, as revealed in a study where it was employed in identifying the strongest data series of winning and losing the Belgian professional soccer [22]. Along with utilizing selective filtering, the application of robust data decomposition schemes such as SWT efficiently accomplishes dimensionality reduction of the input variables.…”

“…It is highly compatible and facilitates any tree-based learner such as RF, XGBoost, decision tree (DT), etc. as the base model [22], [32]. To select the most significant features, the Boruta algorithm creates shadow features (exact replicas) of each feature and shuffles the values in the shadowed features to remove their correlations with the response variable [33].…”

Identification of Statin’s Action in a Small Cohort of Patients with Major Depression

“…Such datasets, i.e., high-dimension low-sample size (HDLSS), are very common in clinical settings [36], [37] and are known to present several statistical challenges [38][39][40][41]. Machine learning (ML) techniques offer several tools to handle these challenges and have been used extensively for high-dimensionality problems [42][43][44][45][46][47]. When the sample size is small, feature selection is a crucial data preprocessing step that allows choosing the variables that contribute the most to the target effect while minimizing possible redundancies.…”