The Use of Modular Feed Forward Neural Networks in Anticipating the Results of Handball Championship 2015

Amr Hassan

doi:doi:10.11648/j.ajss.20150305.13

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The Use of Modular Feed Forward Neural Networks in Anticipating the Results of Handball Championship 2015

Amr Hassan

Published in American Journal of Sports Science (Volume 3, Issue 5)

Received: 7 August 2015 Accepted: 15 August 2015 Published: 21 August 2015

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Abstract

Observation is a highly recommended approach in game analysis as it helps form a better understanding for the types of relations within the game. The aim of this study is to present a new approach for predicting competitions results which are based on game analysis by the use of Modular Forward Neural Networks (MFNN). The data of 80 games were analyzed (i.e. Fast break, Breakthrough, different type of shot…). The Data used to train Modular Feed Forward networks include 21 processing elements (PEs) as input, one element as output, 2 hidden layers, 100 epochs – termination Cross Validation, random initial weights, and weight update batch. The MFNN test contains single output case threshold 0, 5 on level 1000. Results show significant correlation between game results and neural network output 0.93, 0.96. Actual network output was 0, 91. Normalized Root Mean Square Error was 0,078. Final mean squared error was 0.9. The variables mostly affecting the results of (MFNN) were: fast breaks, and blocked shots. Using MFNN in predicting game results based on game details is considered a novel approach for evaluating the level of teams and competitors and for improving the training plans and tactics

Published in	American Journal of Sports Science (Volume 3, Issue 5)
DOI	10.11648/j.ajss.20150305.13
Page(s)	93-97
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Team Handball, Neural Networks, Anticipation

References

[1]	Y. Taskiran. (2007). “2007 EHF Youth Coaches´ Course during the 2007 W 19 European Championship,” Coach. E H F Youth Championship, Eur.
[2]	A. Hohmann and M. Lames. (2005). “Trainings-und Wettspielanalyse,” M. K. & K. R. In A. Hohmann and (Hrsg.), Eds. Handbuch Sportspiel. - Schorndorf : Hofmann, pp. 376–394.
[3]	D. Memmert and K. Roth. (2003). “Individualtaktische Leistungsdiagnostik im Sportspiel,” Spectrum, vol. 15, no. July, pp. 44–70.
[4]	W. Schöllhorn and P. Jürgen. (2002). “Prozessanalysen in der Bewegungs- und Sportspielforschung —Sportinformatik,” Spectr. der Sport., vol. 14, no. 1, pp. 30–52.
[5]	M. Pfeiffer and J. Perl. (2006). “Analysis of tactical Structures in team handball by means of artificial neural networks,” Int. J. Comput. Sci. Sport, vol. 5, no. 1, pp. 4–14.
[6]	P. Rudelsdorfer, N. Schrapf, H. Possegger, T. Mauthner, H. Bischof, and M. Tilp. (2014). “A novel method for the analysis of sequential actions in team handball,” Int. J. Comp. Sci. Sport, vol. 13, no. 1, pp. 69–84.
[7]	N, Scharpf., M, Tilp. (2013). “Action sequence analysis in team handball,” J. Hum. Sport Exerc. North Am., vol. 8, no. 3Proc, pp. 615–621.
[8]	S, Alsaied., N, Scharpf., A, Hassan., M, Tilp. (2015). “Analysis Of Interaction Between Offense And Defence Tactics In Team Handball By Means Of Artificial Neural Networks,” in 20th Annual ECSS-Congress, Malmö.
[9]	M. Janta, C. Ebert, and V. Senner. (2012). “Functionality and performance of customized sole inlays for various sports applications,” Procedia Eng., vol. 34, pp. 290–294.
[10]	Y. Torun and G. Tas. (2012). “Remote Sensing Image Classification By Non-Parallel Svms,” Adv. Inf. Technol. andManagement, vol. 1, no. 3, pp. 1–3.
[11]	H. O. Stekler, D. Sendor, and R. Verlander. (2010). “Issues in sports forecasting,” Int. J. Forecast., vol. 26, no. 3, pp. 606–621.
[12]	X. Biao. (2012). “Prediction of Sports Performance based on Genetic Algorithm and Artificial Neural Network,” Int. J. Digit. Content Technol. its Appl., vol. 6, no. 22, pp. 141–149.
[13]	S. R. Iyer and R. Sharda. (2009). “Prediction of athletes performance using neural networks: An application in cricket team selection,” Expert Syst. Appl., vol. 36, no. 3, pp. 5510–5522.
[14]	V. G. Ivancevic and T. T. Ivancevic. (2010). Quantum Neural Computation. Springer Science & Business Media,.
[15]	A. O. Ali, I. A. Saleh, and T. R. Badawy. (2010). “Intelligent Adaptive Intrusion Detection Systems Using Neural Networks ( Comparitive study ),” Int. J. Video& Image Process. Netw. Secur. IJVIPNS-IJENS, vol. 10, no. 01, pp. 1–8.

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Amr Hassan. (2015). The Use of Modular Feed Forward Neural Networks in Anticipating the Results of Handball Championship 2015. American Journal of Sports Science, 3(5), 93-97. https://doi.org/10.11648/j.ajss.20150305.13

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Amr Hassan. The Use of Modular Feed Forward Neural Networks in Anticipating the Results of Handball Championship 2015. Am. J. Sports Sci. 2015, 3(5), 93-97. doi: 10.11648/j.ajss.20150305.13

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Amr Hassan. The Use of Modular Feed Forward Neural Networks in Anticipating the Results of Handball Championship 2015. Am J Sports Sci. 2015;3(5):93-97. doi: 10.11648/j.ajss.20150305.13

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@article{10.11648/j.ajss.20150305.13,
  author = {Amr Hassan},
  title = {The Use of Modular Feed Forward Neural Networks in Anticipating the Results of Handball Championship 2015},
  journal = {American Journal of Sports Science},
  volume = {3},
  number = {5},
  pages = {93-97},
  doi = {10.11648/j.ajss.20150305.13},
  url = {https://doi.org/10.11648/j.ajss.20150305.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajss.20150305.13},
  abstract = {Observation is a highly recommended approach in game analysis as it helps form a better understanding for the types of relations within the game. The aim of this study is to present a new approach for predicting competitions results which are based on game analysis by the use of Modular Forward Neural Networks (MFNN). The data of 80 games were analyzed (i.e. Fast break, Breakthrough, different type of shot…). The Data used to train Modular Feed Forward networks include 21 processing elements (PEs) as input, one element as output, 2 hidden layers, 100 epochs – termination Cross Validation, random initial weights, and weight update batch. The MFNN test contains single output case threshold 0, 5 on level 1000. Results show significant correlation between game results and neural network output 0.93, 0.96. Actual network output was 0, 91. Normalized Root Mean Square Error was 0,078. Final mean squared error was 0.9. The variables mostly affecting the results of (MFNN) were: fast breaks, and blocked shots. Using MFNN in predicting game results based on game details is considered a novel approach for evaluating the level of teams and competitors and for improving the training plans and tactics},
 year = {2015}
}

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TY - JOUR
T1 - The Use of Modular Feed Forward Neural Networks in Anticipating the Results of Handball Championship 2015
AU - Amr Hassan
Y1 - 2015/08/21
PY - 2015
N1 - https://doi.org/10.11648/j.ajss.20150305.13
DO - 10.11648/j.ajss.20150305.13
T2 - American Journal of Sports Science
JF - American Journal of Sports Science
JO - American Journal of Sports Science
SP - 93
EP - 97
PB - Science Publishing Group
SN - 2330-8540
UR - https://doi.org/10.11648/j.ajss.20150305.13
AB - Observation is a highly recommended approach in game analysis as it helps form a better understanding for the types of relations within the game. The aim of this study is to present a new approach for predicting competitions results which are based on game analysis by the use of Modular Forward Neural Networks (MFNN). The data of 80 games were analyzed (i.e. Fast break, Breakthrough, different type of shot…). The Data used to train Modular Feed Forward networks include 21 processing elements (PEs) as input, one element as output, 2 hidden layers, 100 epochs – termination Cross Validation, random initial weights, and weight update batch. The MFNN test contains single output case threshold 0, 5 on level 1000. Results show significant correlation between game results and neural network output 0.93, 0.96. Actual network output was 0, 91. Normalized Root Mean Square Error was 0,078. Final mean squared error was 0.9. The variables mostly affecting the results of (MFNN) were: fast breaks, and blocked shots. Using MFNN in predicting game results based on game details is considered a novel approach for evaluating the level of teams and competitors and for improving the training plans and tactics
VL - 3
IS - 5
ER -

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Author Information

Amr Hassan

Department of Sports Training, Faculty of Sports Education, Mansoura University, Mansoura, Egypt; Institute of Sport Science, University of Graz, Graz, Austria

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APA Style

Amr Hassan. (2015). The Use of Modular Feed Forward Neural Networks in Anticipating the Results of Handball Championship 2015. American Journal of Sports Science, 3(5), 93-97. https://doi.org/10.11648/j.ajss.20150305.13

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ACS Style

Amr Hassan. The Use of Modular Feed Forward Neural Networks in Anticipating the Results of Handball Championship 2015. Am. J. Sports Sci. 2015, 3(5), 93-97. doi: 10.11648/j.ajss.20150305.13

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AMA Style

Amr Hassan. The Use of Modular Feed Forward Neural Networks in Anticipating the Results of Handball Championship 2015. Am J Sports Sci. 2015;3(5):93-97. doi: 10.11648/j.ajss.20150305.13

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@article{10.11648/j.ajss.20150305.13,
  author = {Amr Hassan},
  title = {The Use of Modular Feed Forward Neural Networks in Anticipating the Results of Handball Championship 2015},
  journal = {American Journal of Sports Science},
  volume = {3},
  number = {5},
  pages = {93-97},
  doi = {10.11648/j.ajss.20150305.13},
  url = {https://doi.org/10.11648/j.ajss.20150305.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajss.20150305.13},
  abstract = {Observation is a highly recommended approach in game analysis as it helps form a better understanding for the types of relations within the game. The aim of this study is to present a new approach for predicting competitions results which are based on game analysis by the use of Modular Forward Neural Networks (MFNN). The data of 80 games were analyzed (i.e. Fast break, Breakthrough, different type of shot…). The Data used to train Modular Feed Forward networks include 21 processing elements (PEs) as input, one element as output, 2 hidden layers, 100 epochs – termination Cross Validation, random initial weights, and weight update batch. The MFNN test contains single output case threshold 0, 5 on level 1000. Results show significant correlation between game results and neural network output 0.93, 0.96. Actual network output was 0, 91. Normalized Root Mean Square Error was 0,078. Final mean squared error was 0.9. The variables mostly affecting the results of (MFNN) were: fast breaks, and blocked shots. Using MFNN in predicting game results based on game details is considered a novel approach for evaluating the level of teams and competitors and for improving the training plans and tactics},
 year = {2015}
}

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