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English Premier League Scoreline Analysis: A Stochastic and Game Theory Approach

Received: 4 May 2021    Accepted: 24 May 2021    Published: 31 May 2021
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Abstract

Making an appropriate decision in the selection of sustainable club from other clubs studied involves the use of right statistical approach, hence the need for stochastic and game theory analysis of English premier league scoreline. The following clubs Manchester United (MU), Chelsea (C), Arsenal (A), Manchester City (MC), Liverpool (LP), Tottenham (T) and Everton (E) were studied for both home and away matches for the period of 2010/2011 to 2019/2020 season. The optimal strategy and overall optimal strategy for MR G and MR B were obtained for each season and the 10 seasons respectively. The result showed that Manchester United has the highest probability (0.29) of being selected by MR B and Liverpool has the probability of 0.27 of being selected by MR G. The matrix of flow was also obtained when Manchester United played against Liverpool, given that Manchester United is home, as WWWLWWDWDD, and when Manchester United is away and Liverpool home, as WDLWLLDDWW. The two and four step transition matrix was also used to predict the future matches and their probabilities obtained given the probabilities of the previous game. The limiting distribution of the transition probability matrix obtained showed that Manchester United has a 67% chance of winning Liverpool while Liverpool has a 33% chance of winning Manchester United, this shows that Manchester United is stronger at home. Thus, the two most sustainable clubs out of the seven clubs studied are Manchester United and Liverpool.

Published in American Journal of Theoretical and Applied Statistics (Volume 10, Issue 3)
DOI 10.11648/j.ajtas.20211003.11
Page(s) 136-145
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

Game Theory, Stochastic Model, English Premier League, Probability, Optimal Strategy, Linear Programming

References
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[3] Baio,G. and Blangiardo, M. (2010). Bayesian hierarchical model for the prediction of Football results, Journal of Applied Statistics 32 (7): 253-264.
[4] Clarke, S. R. and Norman, J. M.(1998), When to rush behind in Australian Rules football: A Dynamic programming approach. Journal of Operational Research Society, 49 (5), 530-536.
[5] Etaga, H. O., Umeokeke E. T., Nwosu C. R., Etaga N. C., Umeh E. U., Awopeju B., Eriobu N., Okoye V.C. and Omoruyi F. A (2017), Stochastic Modeling/Game Theory Analysis.
[6] Hirotsu, N. and Wright, M. (2003), A markov chain approach to Optimal Pinch hitting strategies in a designated hitter rule baseball game, Journal of the Operations Research Society of Japan, 46, 353-371.
[7] Ian M. and Phil S. (2013), Forecasting International Soccer Match Results Using Bivariate Discrete Distributions with General Dependence Structure. Centre for Operational Research and Applied Statistics, Salford Business School, University of Salford, Manchester UK paper no 321/06.
[8] Ismail, I. A., Ramly N. A., Kafrawy M. M. and Nasef, M. M (2007), Game Theory Using Genetic Algorithms, Proceedings of the World Congress on Engineering, vol 1, July 2-4, 2007, London, U.K.
[9] Jongwon, K., Nic J., Nimai, P., Besim, A. and Goran V. (2019), The Attacking Process in Football: A Taxonomy for Classifying How Teams Create Goal Scoring Opportunities Using a Case Study of Crystal Palace FC, Front. Psychol. 10: 2202 doi: 10.3389/f psyg.2019.02202.
[10] Norman, J. M. (1999). Markov process applications in sports in IFORS conference. Benjing of Scoreline, International Journal of Physical Sciences Research, Vol 1, no 2, pp. 1-13.
[11] Rory, P. B and Fadi, T. (2019), A Machine Learning Framework for Sport Result Prediction. Applied Computing and Informatics vol 15 pp 27-33.
[12] Ryan, B., Timoth, J. N., Georgios, C. and Sarvapali D. R (2020), Optimising Game Tactics for Football. In Proc. Of the 19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2020), Auckland, New Zealand, May 9-13, 2020, IFAAMAS, 9pp.
[13] Sindik, J. and Vidak, N.(2008), Application of Game theory in Describing Efficacy of Decision making in Sportsman’s Tactical Performance in Team Sports. Interdisciplinary Description of Complex System vol 6 no 1 pp 53-66.
[14] Singh P. A (2010), Optimal Solution Strategy for Games. International Journal on Computer Science and Engineering vol 2 no 9 pp 2778-2782.
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  • APA Style

    Ngonadi Lilian Oluebube, Ezemma George Chijioke, Etaga Harrison Oghenekevwe, Ugoh Christogonus Ifeanyichukwu. (2021). English Premier League Scoreline Analysis: A Stochastic and Game Theory Approach. American Journal of Theoretical and Applied Statistics, 10(3), 136-145. https://doi.org/10.11648/j.ajtas.20211003.11

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    ACS Style

    Ngonadi Lilian Oluebube; Ezemma George Chijioke; Etaga Harrison Oghenekevwe; Ugoh Christogonus Ifeanyichukwu. English Premier League Scoreline Analysis: A Stochastic and Game Theory Approach. Am. J. Theor. Appl. Stat. 2021, 10(3), 136-145. doi: 10.11648/j.ajtas.20211003.11

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    AMA Style

    Ngonadi Lilian Oluebube, Ezemma George Chijioke, Etaga Harrison Oghenekevwe, Ugoh Christogonus Ifeanyichukwu. English Premier League Scoreline Analysis: A Stochastic and Game Theory Approach. Am J Theor Appl Stat. 2021;10(3):136-145. doi: 10.11648/j.ajtas.20211003.11

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  • @article{10.11648/j.ajtas.20211003.11,
      author = {Ngonadi Lilian Oluebube and Ezemma George Chijioke and Etaga Harrison Oghenekevwe and Ugoh Christogonus Ifeanyichukwu},
      title = {English Premier League Scoreline Analysis: A Stochastic and Game Theory Approach},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {10},
      number = {3},
      pages = {136-145},
      doi = {10.11648/j.ajtas.20211003.11},
      url = {https://doi.org/10.11648/j.ajtas.20211003.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20211003.11},
      abstract = {Making an appropriate decision in the selection of sustainable club from other clubs studied involves the use of right statistical approach, hence the need for stochastic and game theory analysis of English premier league scoreline. The following clubs Manchester United (MU), Chelsea (C), Arsenal (A), Manchester City (MC), Liverpool (LP), Tottenham (T) and Everton (E) were studied for both home and away matches for the period of 2010/2011 to 2019/2020 season. The optimal strategy and overall optimal strategy for MR G and MR B were obtained for each season and the 10 seasons respectively. The result showed that Manchester United has the highest probability (0.29) of being selected by MR B and Liverpool has the probability of 0.27 of being selected by MR G. The matrix of flow was also obtained when Manchester United played against Liverpool, given that Manchester United is home, as WWWLWWDWDD, and when Manchester United is away and Liverpool home, as WDLWLLDDWW. The two and four step transition matrix was also used to predict the future matches and their probabilities obtained given the probabilities of the previous game. The limiting distribution of the transition probability matrix obtained showed that Manchester United has a 67% chance of winning Liverpool while Liverpool has a 33% chance of winning Manchester United, this shows that Manchester United is stronger at home. Thus, the two most sustainable clubs out of the seven clubs studied are Manchester United and Liverpool.},
     year = {2021}
    }
    

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  • TY  - JOUR
    T1  - English Premier League Scoreline Analysis: A Stochastic and Game Theory Approach
    AU  - Ngonadi Lilian Oluebube
    AU  - Ezemma George Chijioke
    AU  - Etaga Harrison Oghenekevwe
    AU  - Ugoh Christogonus Ifeanyichukwu
    Y1  - 2021/05/31
    PY  - 2021
    N1  - https://doi.org/10.11648/j.ajtas.20211003.11
    DO  - 10.11648/j.ajtas.20211003.11
    T2  - American Journal of Theoretical and Applied Statistics
    JF  - American Journal of Theoretical and Applied Statistics
    JO  - American Journal of Theoretical and Applied Statistics
    SP  - 136
    EP  - 145
    PB  - Science Publishing Group
    SN  - 2326-9006
    UR  - https://doi.org/10.11648/j.ajtas.20211003.11
    AB  - Making an appropriate decision in the selection of sustainable club from other clubs studied involves the use of right statistical approach, hence the need for stochastic and game theory analysis of English premier league scoreline. The following clubs Manchester United (MU), Chelsea (C), Arsenal (A), Manchester City (MC), Liverpool (LP), Tottenham (T) and Everton (E) were studied for both home and away matches for the period of 2010/2011 to 2019/2020 season. The optimal strategy and overall optimal strategy for MR G and MR B were obtained for each season and the 10 seasons respectively. The result showed that Manchester United has the highest probability (0.29) of being selected by MR B and Liverpool has the probability of 0.27 of being selected by MR G. The matrix of flow was also obtained when Manchester United played against Liverpool, given that Manchester United is home, as WWWLWWDWDD, and when Manchester United is away and Liverpool home, as WDLWLLDDWW. The two and four step transition matrix was also used to predict the future matches and their probabilities obtained given the probabilities of the previous game. The limiting distribution of the transition probability matrix obtained showed that Manchester United has a 67% chance of winning Liverpool while Liverpool has a 33% chance of winning Manchester United, this shows that Manchester United is stronger at home. Thus, the two most sustainable clubs out of the seven clubs studied are Manchester United and Liverpool.
    VL  - 10
    IS  - 3
    ER  - 

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Author Information
  • Department of Statistics, Faculty of Physical Sciences, Nnamdi Azikiwe University, Awka, Nigeria

  • Department of Statistics, Faculty of Physical Sciences, Nnamdi Azikiwe University, Awka, Nigeria

  • Department of Statistics, Faculty of Physical Sciences, Nnamdi Azikiwe University, Awka, Nigeria

  • Department of Statistics, Faculty of Physical Sciences, Nnamdi Azikiwe University, Awka, Nigeria

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