Data Analytics and Football Industry on the Egyptian Premier League

Gomaa Mohamed Othman; Mohamed Dawoud Al-shenawy

doi:doi:10.11648/j.ajss.20221004.12

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Data Analytics and Football Industry on the Egyptian Premier League

Gomaa Mohamed Othman, Mohamed Dawoud Al-shenawy

Published in American Journal of Sports Science (Volume 10, Issue 4)

Received: 5 July 2022 Accepted: 16 August 2022 Published: 10 January 2023

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Abstract

The aim of this study is to identify the level of accuracy in penalty kicks using different techniques of performance analysis in the Egyptian Football League by making a comparison between two models of analysis techniques used, Two models were used: (Korstat XG) and (Instat XG). The researchers used the descriptive survey method for a sample of the Egyptian Football League (10) teams. The appropriate statistical method was used using the statistical analysis program Spss. The most important results of this study were the following: We note from the table that instat, which gives the penalty kick value of 0.75, is the closest to the accuracy, as its accuracy of expecrltation during five seasons reached 99.94% after it was expected that 493.5 penalty kicks were scored, while 491 penalty kicks were actually recorded. On the other hand, the KoraStat model, which gives the penalty kick a value of 0.89, has an accuracy of expectaion 83.84%, after it was expected to score 585.63 penalty kicks, while 491 penalty kicks were actually recordedWhich shows that the value of scoring a penalty kick in the Egyptian League corresponds more to the model of the company Instat, which gives for each penalty kick a value of 0.75 as an expected goal.

Published in	American Journal of Sports Science (Volume 10, Issue 4)
DOI	10.11648/j.ajss.20221004.12
Page(s)	92-95
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

Analytics, Data, Sports Industry

References

[1]	Lu WL, Ting JA, Little JJ, Murphy KP (2013) Learning to track and identify players from broadcast sports videos. IEEE Trans Pattern Anal Mach Intell 35 (7): 1704–1716.
[2]	Lucey P, Bialkowski A, Carr P, Morgan S, Matthews I, Sheikh Y (2013) Representing and discovering adversarial team behaviors using player roles. Paper presented at the IEEE CVPR. 23–28 June 2013.
[3]	Lucey P, Oliver D, Carr P, Roth J, Matthews I (2013) Assessing team strategy using spatiotemporal data. Paper presented at the 19th ACM SIGKDD, Chicago, Illinois, USA.
[4]	Lynch C (2008) Big data: How do your data grow? Nature 455 (7209): 28–29.
[5]	Mackenzie R, Cushion C (2013) Performance analysis in football: a critical review and implications for future research. J Sports Sci 31 (6): 639–676. doi: 10.1080/02640414.2012.746720.
[6]	Marx V (2013) Biology: the big challenges of big data. Nature 498 (7453): 255–260. doi: 10.1038/498255a.
[7]	McGarry T, Anderson DI, Wallace SA, Hughes M, Franks IM (2002) Sport competition as a dynamical self-organizing system. J Sports Sci 20: 771–781.
[8]	Medeiros J (2014) The winning formula: data analytics has become the latest tool keeping football teams one step ahead. Wired. http://www.wired.co.uk/magazine/archive/2014/01/features/the-winning-formula.
[9]	Memmert D, Perl J (2009) Analysis and simulation of creativity learning by means of artificial neural networks. Hum Mov Sci 28 (2): 263–282. doi: 10.1016/j.humov.2008.07.006.
[10]	Mesirov JP (2010) Computer science. Accessible reproducible research. Science 327 (5964): 415–416. doi: 10.1126/science.1179653.
[11]	Mohr M, Krustrup P, Bangsbo J (2005) Fatigue in soccer: a brief review. J Sports Sci 23 (6): 593–599. doi: 10.1080/02640410400021286.
[12]	Montoliu R, Martin-Felez R, Torres-Sospedra J, Martinez-Uso A (2015) Team activity recognition in Association Football using a Bag-of-Words-based method. Hum Mov Sci 41: 165–178. doi: 10.1016/j.humov.2015.03.007.
[13]	Nakanishi R, Murakami K, Naruse T (2008) Dynamic positioning method based on dominant region diagram to realize successful cooperative play. In: Visser U, Ribeiro F, Ohashi T, Dellaert F (eds) Robo cup 2007: Robot Soccer World Cup XI, Vol 5001. Springer, Berlin, pp 488–495.
[14]	Noor AM, Holmberg L, Gillett C, Grigoriadis A (2015) Big data: the challenge for small research groups in the era of cancer genomics. Br J Cancer 113 (10): 1405–1412. doi: 10.1038/bjc.2015.341.
[15]	Norton S (2014) Germany’s 12th man at the World Cup: Big Data. CIO Journal. http://blogs.wsj.com/cio/2014/07/10/germanys-12th-man-at-the-world-cup-big-data/
[16]	Ohmann C, Canham S, Danielyan E, Robertshaw S, Legre Y, Clivio L, Demotes J (2015) ‘Cloud computing’ and clinical trials: report from an ECRIN workshop. Trials 16: 318. doi: 10.1186/s13063-015-0835-6.
[17]	Olthof SB, Frencken WG, Lemmink KA (2015) The older, the wider: on-field tactical behavior of elite-standard youth soccer players in small-sided games. Hum Mov Sci 41: 92–102. doi: 10.1016/j.humov.2015.02.004.

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Gomaa Mohamed Othman, Mohamed Dawoud Al-shenawy. (2023). Data Analytics and Football Industry on the Egyptian Premier League. American Journal of Sports Science, 10(4), 92-95. https://doi.org/10.11648/j.ajss.20221004.12

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ACS Style

Gomaa Mohamed Othman; Mohamed Dawoud Al-shenawy. Data Analytics and Football Industry on the Egyptian Premier League. Am. J. Sports Sci. 2023, 10(4), 92-95. doi: 10.11648/j.ajss.20221004.12

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AMA Style

Gomaa Mohamed Othman, Mohamed Dawoud Al-shenawy. Data Analytics and Football Industry on the Egyptian Premier League. Am J Sports Sci. 2023;10(4):92-95. doi: 10.11648/j.ajss.20221004.12

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@article{10.11648/j.ajss.20221004.12,
  author = {Gomaa Mohamed Othman and Mohamed Dawoud Al-shenawy},
  title = {Data Analytics and Football Industry on the Egyptian Premier League},
  journal = {American Journal of Sports Science},
  volume = {10},
  number = {4},
  pages = {92-95},
  doi = {10.11648/j.ajss.20221004.12},
  url = {https://doi.org/10.11648/j.ajss.20221004.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajss.20221004.12},
  abstract = {The aim of this study is to identify the level of accuracy in penalty kicks using different techniques of performance analysis in the Egyptian Football League by making a comparison between two models of analysis techniques used, Two models were used: (Korstat XG) and (Instat XG). The researchers used the descriptive survey method for a sample of the Egyptian Football League (10) teams. The appropriate statistical method was used using the statistical analysis program Spss. The most important results of this study were the following: We note from the table that instat, which gives the penalty kick value of 0.75, is the closest to the accuracy, as its accuracy of expecrltation during five seasons reached 99.94% after it was expected that 493.5 penalty kicks were scored, while 491 penalty kicks were actually recorded. On the other hand, the KoraStat model, which gives the penalty kick a value of 0.89, has an accuracy of expectaion 83.84%, after it was expected to score 585.63 penalty kicks, while 491 penalty kicks were actually recordedWhich shows that the value of scoring a penalty kick in the Egyptian League corresponds more to the model of the company Instat, which gives for each penalty kick a value of 0.75 as an expected goal.},
 year = {2023}
}

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TY  - JOUR
T1  - Data Analytics and Football Industry on the Egyptian Premier League
AU  - Gomaa Mohamed Othman
AU  - Mohamed Dawoud Al-shenawy
Y1  - 2023/01/10
PY  - 2023
N1  - https://doi.org/10.11648/j.ajss.20221004.12
DO  - 10.11648/j.ajss.20221004.12
T2  - American Journal of Sports Science
JF  - American Journal of Sports Science
JO  - American Journal of Sports Science
SP  - 92
EP  - 95
PB  - Science Publishing Group
SN  - 2330-8540
UR  - https://doi.org/10.11648/j.ajss.20221004.12
AB  - The aim of this study is to identify the level of accuracy in penalty kicks using different techniques of performance analysis in the Egyptian Football League by making a comparison between two models of analysis techniques used, Two models were used: (Korstat XG) and (Instat XG). The researchers used the descriptive survey method for a sample of the Egyptian Football League (10) teams. The appropriate statistical method was used using the statistical analysis program Spss. The most important results of this study were the following: We note from the table that instat, which gives the penalty kick value of 0.75, is the closest to the accuracy, as its accuracy of expecrltation during five seasons reached 99.94% after it was expected that 493.5 penalty kicks were scored, while 491 penalty kicks were actually recorded. On the other hand, the KoraStat model, which gives the penalty kick a value of 0.89, has an accuracy of expectaion 83.84%, after it was expected to score 585.63 penalty kicks, while 491 penalty kicks were actually recordedWhich shows that the value of scoring a penalty kick in the Egyptian League corresponds more to the model of the company Instat, which gives for each penalty kick a value of 0.75 as an expected goal.
VL  - 10
IS  - 4
ER  -

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Author Information

Gomaa Mohamed Othman

Faculty of Physical Education, Zagazig University, Cairo, Egypt
Mohamed Dawoud Al-shenawy

Sports Performance Specialist Analysis, Cairo University, Cairo, Egypt

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APA Style

Gomaa Mohamed Othman, Mohamed Dawoud Al-shenawy. (2023). Data Analytics and Football Industry on the Egyptian Premier League. American Journal of Sports Science, 10(4), 92-95. https://doi.org/10.11648/j.ajss.20221004.12

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ACS Style

Gomaa Mohamed Othman; Mohamed Dawoud Al-shenawy. Data Analytics and Football Industry on the Egyptian Premier League. Am. J. Sports Sci. 2023, 10(4), 92-95. doi: 10.11648/j.ajss.20221004.12

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AMA Style

Gomaa Mohamed Othman, Mohamed Dawoud Al-shenawy. Data Analytics and Football Industry on the Egyptian Premier League. Am J Sports Sci. 2023;10(4):92-95. doi: 10.11648/j.ajss.20221004.12

Copy | Download

@article{10.11648/j.ajss.20221004.12,
  author = {Gomaa Mohamed Othman and Mohamed Dawoud Al-shenawy},
  title = {Data Analytics and Football Industry on the Egyptian Premier League},
  journal = {American Journal of Sports Science},
  volume = {10},
  number = {4},
  pages = {92-95},
  doi = {10.11648/j.ajss.20221004.12},
  url = {https://doi.org/10.11648/j.ajss.20221004.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajss.20221004.12},
  abstract = {The aim of this study is to identify the level of accuracy in penalty kicks using different techniques of performance analysis in the Egyptian Football League by making a comparison between two models of analysis techniques used, Two models were used: (Korstat XG) and (Instat XG). The researchers used the descriptive survey method for a sample of the Egyptian Football League (10) teams. The appropriate statistical method was used using the statistical analysis program Spss. The most important results of this study were the following: We note from the table that instat, which gives the penalty kick value of 0.75, is the closest to the accuracy, as its accuracy of expecrltation during five seasons reached 99.94% after it was expected that 493.5 penalty kicks were scored, while 491 penalty kicks were actually recorded. On the other hand, the KoraStat model, which gives the penalty kick a value of 0.89, has an accuracy of expectaion 83.84%, after it was expected to score 585.63 penalty kicks, while 491 penalty kicks were actually recordedWhich shows that the value of scoring a penalty kick in the Egyptian League corresponds more to the model of the company Instat, which gives for each penalty kick a value of 0.75 as an expected goal.},
 year = {2023}
}

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TY  - JOUR
T1  - Data Analytics and Football Industry on the Egyptian Premier League
AU  - Gomaa Mohamed Othman
AU  - Mohamed Dawoud Al-shenawy
Y1  - 2023/01/10
PY  - 2023
N1  - https://doi.org/10.11648/j.ajss.20221004.12
DO  - 10.11648/j.ajss.20221004.12
T2  - American Journal of Sports Science
JF  - American Journal of Sports Science
JO  - American Journal of Sports Science
SP  - 92
EP  - 95
PB  - Science Publishing Group
SN  - 2330-8540
UR  - https://doi.org/10.11648/j.ajss.20221004.12
AB  - The aim of this study is to identify the level of accuracy in penalty kicks using different techniques of performance analysis in the Egyptian Football League by making a comparison between two models of analysis techniques used, Two models were used: (Korstat XG) and (Instat XG). The researchers used the descriptive survey method for a sample of the Egyptian Football League (10) teams. The appropriate statistical method was used using the statistical analysis program Spss. The most important results of this study were the following: We note from the table that instat, which gives the penalty kick value of 0.75, is the closest to the accuracy, as its accuracy of expecrltation during five seasons reached 99.94% after it was expected that 493.5 penalty kicks were scored, while 491 penalty kicks were actually recorded. On the other hand, the KoraStat model, which gives the penalty kick a value of 0.89, has an accuracy of expectaion 83.84%, after it was expected to score 585.63 penalty kicks, while 491 penalty kicks were actually recordedWhich shows that the value of scoring a penalty kick in the Egyptian League corresponds more to the model of the company Instat, which gives for each penalty kick a value of 0.75 as an expected goal.
VL  - 10
IS  - 4
ER  -

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