Football Match Line-Up Prediction Based on Physiological Variables: A Machine Learning Approach

Abstract

One of the great challenges for football coaches is to choose the football line-up that gives more guarantees of success. Even though there are several dimensions to analyse the problem, such as the opposing team characteristics. The objective of this study is to identify, based on the players’ physiological variables collected using Global Positioning Systems (GPS), which players are the most suitable to be part of the starting team/line-up. The work was developed in two stages, first with the choice of the most important variables using the Recursive Feature Elimination algorithm, and then using logistic regression on these chosen variables. The logistic regression resulted in an index, called the line-up preparedness index, for the following player positions: Fullbacks, Central Midfielders and Wingers. For the other players’ positions, the model results were not satisfactory.

Sports Analytics and Text Mining NBA Data to Assess Recovery from Injuries and Their Economic Impact

“…This is evident in diverse applications, from aiding team management in estimating and mitigating injury risks to assisting betting companies in forecasting player and team performance. The overarching goal remains consistent: to reduce associated costs and elevate performance metrics, either for an individual player or the entire team [1][2][3][4]. Engaging in sports boasts numerous advantages but also leads to injuries.…”

“…New Orleans Pelicans (previously the New Orleans Hornets, and before that, the Charlotte Hornets);2 Memphis Grizzlies (The team started in Vancouver and moved to Memphis in 2001);3 Brooklyn Nets (previously known as the New Jersey Nets until 2012);4 Charlotte Hornets (The team was previously known as the Charlotte Bobcats).…”