Literature DB >> 25793661

Match statistics related to winning in the group stage of 2014 Brazil FIFA World Cup.

Hongyou Liu1, Miguel-Ángel Gomez, Carlos Lago-Peñas, Jaime Sampaio.   

Abstract

Identifying match statistics that strongly contribute to winning in football matches is a very important step towards a more predictive and prescriptive performance analysis. The current study aimed to determine relationships between 24 match statistics and the match outcome (win, loss and draw) in all games and close games of the group stage of FIFA World Cup (2014, Brazil) by employing the generalised linear model. The cumulative logistic regression was run in the model taking the value of each match statistic as independent variable to predict the logarithm of the odds of winning. Relationships were assessed as effects of a two-standard-deviation increase in the value of each variable on the change in the probability of a team winning a match. Non-clinical magnitude-based inferences were employed and were evaluated by using the smallest worthwhile change. Results showed that for all the games, nine match statistics had clearly positive effects on the probability of winning (Shot, Shot on Target, Shot from Counter Attack, Shot from Inside Area, Ball Possession, Short Pass, Average Pass Streak, Aerial Advantage and Tackle), four had clearly negative effects (Shot Blocked, Cross, Dribble and Red Card), other 12 statistics had either trivial or unclear effects. While for the close games, the effects of Aerial Advantage and Yellow Card turned to trivial and clearly negative, respectively. Information from the tactical modelling can provide a more thorough and objective match understanding to coaches and performance analysts for evaluating post-match performances and for scouting upcoming oppositions.

Keywords:  match analysis; modelling; notational analysis; soccer

Mesh:

Year:  2015        PMID: 25793661     DOI: 10.1080/02640414.2015.1022578

Source DB:  PubMed          Journal:  J Sports Sci        ISSN: 0264-0414            Impact factor:   3.337


  15 in total

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4.  Predicting Wins, Losses and Attributes' Sensitivities in the Soccer World Cup 2018 Using Neural Network Analysis.

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5.  What Frequency of Technical Activity Is Needed to Improve Results? New Approach to Analysis of Match Status in Professional Soccer.

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6.  Evaluation of the Technical Performance of Football Players in the UEFA Champions League.

Authors:  Qing Yi; Miguel-Ángel Gómez-Ruano; Hongyou Liu; Shaoliang Zhang; Binghong Gao; Fabian Wunderlich; Daniel Memmert
Journal:  Int J Environ Res Public Health       Date:  2020-01-17       Impact factor: 3.390

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Authors:  Georgios Ermidis; Rasmus C Ellegard; Vincenzo Rago; Morten B Randers; Peter Krustrup; Malte N Larsen
Journal:  Int J Environ Res Public Health       Date:  2021-05-25       Impact factor: 3.390

Review 8.  Big data and tactical analysis in elite soccer: future challenges and opportunities for sports science.

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Journal:  Springerplus       Date:  2016-08-24

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Journal:  PLoS One       Date:  2018-10-31       Impact factor: 3.240

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Journal:  J Hum Kinet       Date:  2020-03-31       Impact factor: 2.193

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