The rapid and impromptu interest in the coupling of machine learning (ML) algorithms with
wearable and contactless sensors aimed at tackling real‐world problems warrants a …

Sports analytics—broadly defined as the pursuit of improvement in athletic performance
through the analysis of data—has expanded its footprint both in the professional sports …

Rapid technological progress and digitalization have considerably changed the role of
technology in sports in the past two decades. As the human limits of performance have been …

Given the large pitch, numerous players, limited player turnovers, and sparse scoring,
soccer is arguably the most challenging to analyze of all the major team sports. In this work …

There has been an explosion of data collected about sports. Because such data is extremely
rich and complex, machine learning is increasingly being used to extract actionable insights …

A variety of machine learning models have been proposed to assess the performance of
players in professional sports. However, they have only a limited ability to model how player …

This study explores a big and open database of soccer leagues in 10 European countries.
Data related to players, teams and matches covering seven seasons (from 2009/2010 to …

Using data from one season of NBA games, Basketball Data Science: With Applications in R
is the perfect book for anyone interested in learning and applying data analytics in …

A major task of sports analytics is player evaluation. Previous methods commonly measured
the impact of players' actions on desirable outcomes (eg, goals or winning) without …

Point prediction of future record values based on sequences of previous lower or upper
records is considered by means of the method of maximum product of spacings, where the …