Synthetic Methods to Deal with Big Data in Soccer - The FMH Social Networks Project

Ricardo Duarte, Sérgio Tomás, Daniel Baião

2014

Abstract

Big data is a controversial issue in performance analysis. Social networks have been adopted as a synthetic method to uncover the web of interactions captured from long data sets. Here, we describe the research findings of two studies. First, we investigated the influence of the ball possession characteristics in the competitive success of Spanish La Liga teams. We found that competitive performance was influenced by the density and connectivity of the teams, mainly due to the way teams use their possession time to give intensity to his game. In the second study, we developed and validated a multiple context-dependent social networks method for applied performance analysis. Face and quantitative content validity were assessed using panels of subject-matter experts. Sensibility was also measured, suggesting the multiple context-dependent networks are sensitive enough to capture differences in the way players interact with each other in different game contexts.

References

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Paper Citation


in Harvard Style

Duarte R., Tomás S. and Baião D. (2014). Synthetic Methods to Deal with Big Data in Soccer - The FMH Social Networks Project . In - PerSoccer, (icSPORTS 2014) ISBN , pages 0-0


in Bibtex Style

@conference{persoccer14,
author={Ricardo Duarte and Sérgio Tomás and Daniel Baião},
title={Synthetic Methods to Deal with Big Data in Soccer - The FMH Social Networks Project},
booktitle={ - PerSoccer, (icSPORTS 2014)},
year={2014},
pages={},
publisher={SciTePress},
organization={INSTICC},
doi={},
isbn={},
}


in EndNote Style

TY - CONF
JO - - PerSoccer, (icSPORTS 2014)
TI - Synthetic Methods to Deal with Big Data in Soccer - The FMH Social Networks Project
SN -
AU - Duarte R.
AU - Tomás S.
AU - Baião D.
PY - 2014
SP - 0
EP - 0
DO -