Cricket Team Prediction with Hadoop: Statistical Modeling Approach

被引:9
|
作者
Agarwal, Shubham [1 ]
Yadav, Lavish [1 ]
Mehta, Shikha [1 ]
机构
[1] Jaypee Inst Informat Technol, Noida, India
来源
5TH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND QUANTITATIVE MANAGEMENT, ITQM 2017 | 2017年 / 122卷
关键词
Cricket team prediction; Hadoop; Hive; Sports Prediction; Statistical Modelin;
D O I
10.1016/j.procs.2017.11.402
中图分类号
F [经济];
学科分类号
02 ;
摘要
Cricket is one of the most popular team games in the world. In this paper, we embark on predicting the best suitable Team to be lined for a particular match. We propose statistical modeling approach to predict the perfect players for the match to be played. As cricket is not a very simple sport, there are many factors affecting the line-up and selection of players for a particular match such as Player's Overall Stats, Player Performances with different Teams and the most important Last 5 Performances. All these factors have been considered for selection of players in playing 11 from the Team of 16. This work suggests that the relative team strength between the competing teams forms a distinctive feature for predicting the winner. Modeling the team strength boils down to modeling individual player batting and bowling performances, forming the basis of approach used. Career statistics as well as the recent performances of a player have been used to model. Player independent factors have also been considered in order to predict the outcome of a match. Experimental analysis was performed using Hadoop and Hive for Indian players. Results establish that proposed approach is able to obtain up to 91% accuracy as compared to the real results available over WWW. (C) 2017 The Authors. Published by Elsevier B.V.
引用
收藏
页码:525 / 532
页数:8
相关论文
共 50 条
  • [31] A MODEL SELECTION APPROACH IN STATISTICAL MODELING
    Mentes, Turhan
    HACETTEPE JOURNAL OF MATHEMATICS AND STATISTICS, 2010, 39 (01): : 131 - 135
  • [32] Statistical approach to modeling of spatiotemporal dynamics
    Mandelj, S
    Grabec, I
    Govekar, E
    INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS, 2001, 11 (11): : 2731 - 2738
  • [33] MapReduce Workload Modeling with Statistical Approach
    Hailong Yang
    Zhongzhi Luan
    Wenjun Li
    Depei Qian
    Journal of Grid Computing, 2012, 10 : 279 - 310
  • [34] Team selection after a short cricket series
    Lemmer, Hermanus Hofmeyr
    EUROPEAN JOURNAL OF SPORT SCIENCE, 2013, 13 (02) : 200 - 206
  • [35] MODELING APPROACH TO CORROSION PREDICTION
    EDELEANU, C
    HINES, JG
    BRITISH CORROSION JOURNAL, 1983, 18 (01): : 6 - 9
  • [36] Hadoop Performance Self-Tuning Using a Fuzzy-Prediction Approach
    Lee, Gil Jae
    Fortes, Jose A. B.
    2016 IEEE INTERNATIONAL CONFERENCE ON AUTONOMIC COMPUTING (ICAC), 2016, : 55 - 64
  • [37] A STATISTICAL-ANALYSIS OF BATTING IN CRICKET
    KIMBER, AC
    HANSFORD, AR
    JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY, 1993, 156 : 443 - 455
  • [38] Modeling Team Dynamics for the Characterization and Prediction of Delays in User Stories
    Kula, Elvan
    van Deursen, Arie
    Gousios, Georgios
    2021 36TH IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING ASE 2021, 2021, : 991 - 1002
  • [39] A Statistical Approach for Prediction of Projects Based on Simulation
    de Souza, Mariane Moreira
    de Oliveira, Humberto C. B.
    de Vasconcelos, Alexandre M. L.
    Bezerra Oliveira, Sandro R.
    APPLIED COMPUTING 2008, VOLS 1-3, 2008, : 23 - +
  • [40] Statistical Models approach for Solar Radiation Prediction
    Ferrari, S.
    Lazzaroni, M.
    Piuri, V.
    Cristaldi, L.
    Faifer, M.
    2013 IEEE INTERNATIONAL INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE (I2MTC), 2013, : 1734 - 1739