PSL Eye: Predicting the Winning Team in Pakistan Super League (PSL) Matches
Keywords:Pakistan Super League, T20, PSL, Prediction, Neural Networks, Machine Learning
Pakistan Super League (PSL) is a well-known T20 cricket league with millions of viewers. With this large viewer base, predicting the outcome of PSL matches opens a new research avenue for academic researchers. In this paper, we collect PSL data from relevant sources and generate a validated data set for machine learning experiments. We implement the “PSL Eye” solution which employs Neural Networks (NNs) to predict the match winning team. We preprocess the dataset to eliminate the extra variables then we tune the hyper parameters of NN. After acquiring the optimal values of hyper parameters, we run our NN based PSL Eye to obtain the final results. The overall accuracy of PSL-Eye with testing data set is 82% which is very promising and shows the importance of NN in predicting PSL match outcome.
Iyer SR, Sharda R. Prediction of athletes performance using neural networks: An application in cricket team selection. Expert Systems with Applications. 2009 Apr 1;36(3):5510-22.
Petersen C, Pyne DB, Portus MJ, Dawson B. Analysis of Twenty/20 cricket performance during the 2008 Indian Premier League. International Journal of Performance Analysis in Sport. 2008 Nov 10;8(3):63-9.
Bilal M, Asif S, Yousuf S, Afzal U. 2018 Pakistan General Election: Understanding the Predictive Power of Social Media. In2018 12th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS) 2018 Nov 24 (pp. 1-6).
Kaluarachchi A, Aparna SV. CricAI: A classification based tool to predict the outcome in ODI cricket. In2010 Fifth International Conference on Information and Automation for Sustainability 2010 Dec 17 (pp. 250-255).
Prakash CD, Patvardhan C, Singh S. A new category based deep performance index using machine learning for ranking IPL cricketers. Int. Jl. of Electronics, Electrical and Computational System IJEECS ISSN. 2016 Feb.
Salman M, Qaisar S, Qamar AM. Classification and legality analysis of bowling action in the game of cricket. Data Mining and Knowledge Discovery. 2017 Nov 1;31(6):1706-34.
PSL DATA REPOSITORY. [online] Available at: https://github.com/nasir03082409229/psl-eye.git [Accessed 30 Oct. 2019].
Géron A. Hands-on machine learning with Scikit-Learn and TensorFlow: concepts, tools, and techniques to build intelligent systems. " O'Reilly Media, Inc."; 2017 Mar 13.
Nafees U, Parveen S, Zahid K, Afzal U. A Tensorflow Based Neural Network to Predict Drone Strikes in Pakistan. In2018 12th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS) 2018 Nov 24 (pp. 1-6). IEEE.
Sankaranarayanan VV, Sattar J, Lakshmanan LV. Auto-Play: A data mining approach to ODI Cricket simulation and prediction. InProceedings of the 2014 SIAM International Conference on Data Mining 2014 Apr 28 (pp. 1064-1072). Society for Industrial and Applied Mathematics.
Kumar G. Machine learning for soccer analytics. KU Leuven. 2013. Kumar, Gunjan. "Machine learning for soccer analytics." KU Leuven (2013).
Portus MR, Farrow D. Enhancing cricket batting skill: implications for biomechanics and skill acquisition research and practice. Sports Biomechanics. 2011 Nov 1;10(4):294-305.
Saikia H, Bhattacharjee D, Lemmer HH. Predicting the performance of bowlers in IPL: an application of artificial neural network. International Journal of Performance Analysis in Sport. 2012 Apr 1;12(1):75-89.
Battiti R, Villani A, Le Nhat T. Neural network models for intelligent networks: deriving the location from signal patterns. Proceedings of AINS. 2002 May 8.
Kalgotra P, Sharda R, Chakraborty G. Predictive modeling in sports leagues: an application in Indian Premier League. Available at SSRN 2465300. 2013 Apr 28.
Kansal P, Kumar P, Arya H, Methaila A. Player valuation in indian premier league auction using data mining technique. In2014 international conference on contemporary computing and informatics (IC3I) 2014 Nov 27 (pp. 197-203). IEEE.
Passi K, Pandey N. Increased Prediction Accuracy in the Game of Cricket using Machine Learning. arXiv preprint arXiv:1804.04226. 2018 Apr 9.
Jhanwar MG, Pudi V. Predicting the Outcome of ODI Cricket Matches: A Team Composition Based Approach. InMLSA@ PKDD/ECML 2016 Sep 19.
Prakash CD, Patvardhan C, Lakshmi CV. Data Analytics based Deep Mayo Predictor for IPL-9. International Journal of Computer Applications. 2016 Oct;152(6):6-10.
Singh S, Kaur P. IPL visualization and prediction using HBase. Procedia computer science. 2017 Jan 1;122:910-5.
Lamsal R, Choudhary A. Predicting Outcome of Indian Premier League (IPL) Matches Using Machine Learning. arXiv preprint arXiv:1809.09813. 2018 Sep.
Pakistan Cricket Board official website [online] Available at: https://www.pcb.com.pk/ [Accessed 30 Oct. 2019].
ESPN Cricinfo [online] Available at: http://www.espncricinfo.com/ [Accessed 30 Oct. 2019].
CRICINGIF [online] Available at: https://www.cricingif.com/series/1278/pakistan-super-league-psl. [Accessed 30 Oct. 2019].
PSL EYE: [online] Available at: .https://psl.hairnet.com.pk/ [Accessed 30 Oct. 2019]