Abstract
The use of principal component analysis (PCA) provides information about the main characteristics of teams, based on a set of indicators, instead of displaying individualized information for each of these indicators. In this work we have considered reducing an extensive data matrix to improve interpretation, using PCA. Subsequently, with new components and with multiple linear regression, we have carried out a comparative analysis between the best and bottom teams of LaLiga. The sample consisted of the matches corresponding to the 2015/16, 2016/17 and 2017/18 seasons. The results showed that the best teams were characterized and differentiated from bottom teams in the realization of a greater number of successful passes and in the execution of a greater number of dynamic offensive transitions. The bottom teams were characterized by executing more defensive than offensive actions, showing fewer number of goals and a greater ball possession time in the final third of the field. Goals, ball possession time in the final third of the field, number of effective shots and crosses are the main discriminating performance factors of football. This information allows us to increase knowledge about the key performance indicators (KPI) in football.
Highlights
Published: 19 March 2021The identification of performance factors, understood as variables that define some aspect of performance and that help achieve sports success [1], is essential to try to identify the most appropriate behavior patterns that can lead to success [2] and enable the increase and prediction of performance [3,4]
We are facing a sport of a complex and dynamic nature, which makes the identification of these performance profiles a very difficult task [7] because the success of the game can be associated with multiple factors, some of them being unpredictable or uncontrollable, such as arbitration decisions, individual successes or failures of players, match location, type of competition or even chance
The results obtained from the linear discriminant analysis (LDA) were 85.63%7well classified and 14.36% poorly classified, which reveals that the indicators used correctly classified the teams as best and bottom
Summary
Published: 19 March 2021The identification of performance factors, understood as variables that define some aspect of performance and that help achieve sports success [1], is essential to try to identify the most appropriate behavior patterns that can lead to success [2] and enable the increase and prediction of performance [3,4]. The analysis of the matches will identify those variables related to success [5], and the grouping and combination of these success indicators of different nature will allow the construction of football performance profiles [4,6]. Football research has turned to a multitude of performance indicators [8], and some studies have tried to identify them through the comparative analysis of successful and unsuccessful teams [9,10,11,12,13,14,15,16,17] Some of these works show conflicting results. This may be Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
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