An application of PART to the Football Manager data for players clusters analyses to inform club team formation

  • Published: 01 January 2018
  • We aim to show how a neural network based machine learning projective clustering algorithm, Projective Adaptive Resonance Theory (PART), can be effectively used to provide data-informed sports decisions. We illustrate this data-driven decision recommendation for AS Roma player market in the Summer 2018 season, using the two separate databases of fourty-seven attributes taken from Football Manager 2018 for each of the twenty-four soccer player, with the first including players of the AS Roma squad 2017-18, and the second consisting of all players linked with transfer moves to AS Roma. This is high dimensional data as players should be grouped only in terms of their performance with respect to a small subset of attributes. Projective clustering analyses provide a purely data-driven analysis to identify critical attributes and attribute characteristics for a group of players to form a natural cluster (in lower dimensional data space) in an unsupervised way. By merging the two databases, our unsupervised clustering analysis provides evidence-based recommendations about the club team formation, and in particular, the decision to buy and sell players within the same clusters, under different scenarios including financial constraints.

    Citation: Marco Tosato, Jianhong Wu. An application of PART to the Football Manager data for players clusters analyses to inform club team formation[J]. Big Data and Information Analytics, 2018, 3(1): 43-54. doi: 10.3934/bdia.2018002

    Related Papers:

  • We aim to show how a neural network based machine learning projective clustering algorithm, Projective Adaptive Resonance Theory (PART), can be effectively used to provide data-informed sports decisions. We illustrate this data-driven decision recommendation for AS Roma player market in the Summer 2018 season, using the two separate databases of fourty-seven attributes taken from Football Manager 2018 for each of the twenty-four soccer player, with the first including players of the AS Roma squad 2017-18, and the second consisting of all players linked with transfer moves to AS Roma. This is high dimensional data as players should be grouped only in terms of their performance with respect to a small subset of attributes. Projective clustering analyses provide a purely data-driven analysis to identify critical attributes and attribute characteristics for a group of players to form a natural cluster (in lower dimensional data space) in an unsupervised way. By merging the two databases, our unsupervised clustering analysis provides evidence-based recommendations about the club team formation, and in particular, the decision to buy and sell players within the same clusters, under different scenarios including financial constraints.


    加载中
    [1] [Financial Fair Play club summary - Official document, retrieved from www.uefa.com.
    [2] [ Guide to Football Manager, retrieved from www.guidetofm.com.
    [3] [ C. C. Aggarwal, J. L. Wolf, P. S. Yu, C. Procopiuc and J. S. Park, Fast algorithms for projected clustering, Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data, ACM Press, (1999), 61-72.
    [4] [ R. Agrawal, J. Gehrke, D. Gunopulos and P. Raghavan, Automatic subspace clustering of high dimensional data for data mining applications, Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data, ACM Press (1998), 94-105.
    [5] [J. Cao and J. Wu, Dynamics of projective adaptive resonance theory model: The foundation of PART algorithm, IEEE Transactions on Neural Networks, 2004.
    [6] [ J. Cao and J. Wu, Projective ART for clustering data sets in high dimensional spaces, Neural Networks, 2002.
    [7] [C. H. Cheng, A. W. Fu and Y. Zhang, Entropy-based subspace clustering for mining numerical data, Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM Press (1999), 84-93.
    [8] [G. Gan, C. Ma and J. Wu, Data clustering: Theory, algorithms and applications, SIAM, Philadelphia, ASA, Alexandria, VA, (2007), xxii+466 pp.
    [9] [J. D. Hunter, J. Wu and J. Milton, Clustering neural spike trains with transient responses, Proceedings of the 47th IEEE Conference on Decision and Control, 2008.
    [10] [Director - L. Myles, An alternative reality: The football manager documentary, Sports Interactive, 2014.
  • Reader Comments
  • © 2018 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(5371) PDF downloads(2095) Cited by(0)

Article outline

Figures and Tables

Figures(5)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog