Contextualised physical metrics: The physical demands vary with phase of play during elite soccer match play.

The physical demands of elite soccer match play have traditionally been measured using aggregated whole-match metrics. However, match play is increasingly considered as distinct phases of play, although the influence of phase of play on match physical demands remains largely unknown. This study compared physical intensity, acceleration and deceleration demands, between phases of play and according to playing position.

Evidence for a new model of the complex interrelationship of ball possession, physical intensity and performance in elite soccer.

Background: How the physical metrics, especially physical intensity, and possession interact with each other, and subsequently combine to influence performance remains opaque. Therefore, we investigated the interrelationship of possession, physical metrics, and team performance in elite soccer.

Methods: Four seasons of a top European league were used to derive 80 team league performances (points), together with possession and physical data.

The influence of ball in/out of play and possession in elite soccer: Towards a more valid measure of physical intensity during competitive match-play.

The physical demands of soccer match-play have typically been assessed using a low-resolution whole match approach ignoring whether the ball is in or out of play (BIP/BOP) and during these periods which team has possession. This study investigated the effect of fundamental match structure variables (BIP/BOP, in/out of possession) on the physical demands, and especially intensity, of elite match-play. For 1083 matches from a major European league, whole match duration, and player physical tracking data, were divided into BIP/BOP, and in/out of possession periods throughout the match, using on-ball event data.

Symbiotic adaptive neuro-evolution applied to rainfall-runoff modelling in northern England.

This paper uses a symbiotic adaptive neuro-evolutionary algorithm to breed neural network models for the River Ouse catchment. It advances on traditional evolutionary approaches by evolving and optimising individual neurons. Furthermore, it is ideal for experimentation with alternative objective functions.