Predicting wins, losses and attributes' sensitivities in the soccer World Cup 2018 using neural network analysis

Hassan, A, Akl, A-R, Hassan, I and Sunderland, C ORCID: https://orcid.org/0000-0001-7484-1345, 2020. Predicting wins, losses and attributes' sensitivities in the soccer World Cup 2018 using neural network analysis. Sensors, 20 (11): 3213. ISSN 1424-8220

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Abstract

Predicting the results of soccer competitions and the contributions of match attributes, in particular, has gained popularity in recent years. Big data processing obtained from different sensors, cameras and analysis systems needs modern tools that can provide a deep understanding of the relationship between this huge amount of data produced by sensors and cameras, both linear and non-linear data. Using data mining tools does not appear sufficient to provide a deep understanding of the relationship between the match attributes and results and how to predict or optimize the results based upon performance variables. This study aimed to suggest a different approach to predict wins, losses and attributes' sensitivities which enables the prediction of match results based on the most sensitive attributes that affect it as a second step. A radial basis function neural network model has successfully weighted the effectiveness of all match attributes and classified the team results into the target groups as a win or loss. The neural network model's output demonstrated a correct percentage of win and loss of 83.3% and 72.7% respectively, with a low Root Mean Square training error of 2.9% and testing error of 0.37%. Out of 75 match attributes, 19 were identified as powerful predictors of success. The most powerful respectively were: the Total Team Medium Pass Attempted (MBA) 100%; the Distance Covered Team Average in zone 3 (15-20 km/h; Zone3_TA) 99%; the Team Average ball delivery into the attacking third of the field (TA_DAT) 80.9%; the Total Team Covered Distance without Ball Possession (Not in_Poss_TT) 76.8%; and the Average Distance Covered by Team (Game TA) 75.1%. Therefore, the novel radial based function neural network model can be employed by sports scientists to adapt training, tactics and opposition analysis to improve performance.

Item Type:	Journal article
Publication Title:	Sensors
Creators:	Hassan, A., Akl, A.-R., Hassan, I. and Sunderland, C.
Publisher:	MDPI
Date:	1 June 2020
Volume:	20
Number:	11
ISSN:	1424-8220
Identifiers:	Number Type 10.3390/s20113213 DOI 1331594 Other
Rights:	© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Divisions:	Schools > School of Science and Technology
Record created by:	Jill Tomkinson
Date Added:	10 Jun 2020 13:08
Last Modified:	10 Jun 2020 13:08
URI:	https://irep.ntu.ac.uk/id/eprint/39973

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