On an Entropy-based Performance Analysis in Sports

Abstract

This paper discusses the major assumptions of influential ecological approaches on the human movement variability in sports and how it can be analyzed by benefiting from well-known measures of entropy. These measures are exploited so as to further understand the performance of athletes from a dynamical and chaotic perspective. Based on the presented evidences, entropy-based techniques will be considered to measure, analyze and evaluate the human performance variability under three different case studies: i) golf; ii) tennis; and iii) soccer. At a first stage, the athletes' performance will be analyzed at the individual level by considering the golf putting (pendulum movement) and the tennis serve (ballistic movement). Under these gestures, the approximate entropy is considered to extract the variability inherent to the process variables. Afterwards, the athletes' performance will be analyzed at the collective level by considering the soccer case (team sport). To that end, OPEN ACCESS 2 both approximate entropy and Shannon's entropy are mutually considered to assess the variability of football players' trajectory. To outline the applicability of entropy-based measures to analyze sports, this article ends with an overall reflection about the potential of such measures towards an increased understanding on the overall human performance. This methodology proves to be useful to provide decisive information and feedback for coaches, sports analysts and even for the athletes.

The re-organization of action in golf putting under different task constraints

“…Although this may seem unexpected, as it means that golfers depict a larger variability when near the hole, it is in line with the literature [22]. Nonetheless, these find-ings do not fully unveil the temporal structure of the golf putting.…”

“…As discussed by Couceiro et al [22], in order to evaluate (re)organization of action during putting performance, a single time-series is needed that may represent the overall putting data over time in all the trials under specific task constraints. For this purpose, here, we present six timeseries corresponding to the angular and linear trajectories of the putter in 3D space, for each player and at each distance, i.e., 24 time-series for each player.…”

Towards An Entropic Approach Of The Movement

“…If we accept that entropy tends to increase the https://doi.org/10.15405/epes.23045.74 Corresponding Author: Elena Ioana Iconaru Selection and peer-review under responsibility of the Organizing Committee of the conference eISSN: 2672-815X variability of movement patterns, it follows that sports performance has an anti-entropic character by excellence. Moreover, many researchers interpret the variability in the field of application of sports science as a factor that forces the athlete to adapt and stabilize his actions in relation to the constraints of the tasks to be performed (Couceiro et al, 2013;Couceiro, Clemente, Dias et al, 2014).…”

A novel application of entropy analysis for assessing changes in movement variability during cumulative tackles in young elite rugby league players

“…Furthermore, entropy provides researchers the ability to quantify complexity setting high regularity as low entropy and a very random movement as high entropy. Within the past 20 years, entropy analysis has gained popularity in movement sciences in sports to describe changes in postural control [ 35 – 38 ], assessment of running [ 39 , 40 ], human walking data [ 41 – 43 ], tactical behaviour in soccer [ 44 , 45 ], force production [ 46 – 48 ] and as a measure of system complexity in sports [ 49 ]. Recently, it has also been validated for detecting increases in movement variability in elite rugby players during resistance training when a ball is included [ 50 , 51 ].…”