Call for Paper - January 2024 Edition
IJCA solicits original research papers for the January 2024 Edition. Last date of manuscript submission is December 20, 2023. Read More

Predicting the Best Team Players of Pakistan Super League using Machine Learning Algorithms

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2021
Jawaria Ashraf, Sania Bhatti, Shahnawaz Talpur

Jawaria Ashraf, Sania Bhatti and Shahnawaz Talpur. Predicting the Best Team Players of Pakistan Super League using Machine Learning Algorithms. International Journal of Computer Applications 183(16):6-13, July 2021. BibTeX

	author = {Jawaria Ashraf and Sania Bhatti and Shahnawaz Talpur},
	title = {Predicting the Best Team Players of Pakistan Super League using Machine Learning Algorithms},
	journal = {International Journal of Computer Applications},
	issue_date = {July 2021},
	volume = {183},
	number = {16},
	month = {Jul},
	year = {2021},
	issn = {0975-8887},
	pages = {6-13},
	numpages = {8},
	url = {},
	doi = {10.5120/ijca2021921486},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Owing to short and fast paced play, T20 is the adored format of cricket sport. In T20 cricket, Pakistan super league (PSL) is one of the most famous professional leagues founded to strengthen Pakistan cricket by scrutinizing the young talent. However, the selection of the best players for PSL teams is a very critical phase which certainly affects the final results of the play. To avoid biasness caused by the human nature in selection process, this study aims to select and rank the team of top fifteen players based on their batting and bowling performance in previous five seasons of PSL using Machine learning approach. For this purpose, Support vector machine (SVM), Random forest, Naive Bayes, Linear regression and K-nearest neighbor (classification) techniques have been employed for the development of predictive model from individual batting and bowling features sets. Based on comparison of applied techniques, the evaluated results have been plotted in term of accuracy, precision, recall and “f1score”. For the selection of both batsman (in term of runs scored) and bowlers (in term of wickets taken), Random Forest performed well by yielding an accuracy of 100%. Findings of this research also ascertain that batting performance leads over bowling performance.


  1. Jayalath, K. P. 2018. A machine learning approach to analyze ODI cricket predictors. Journal of Sports Analytics, 4(1), 73-84.
  2. Tekade, P., Markad, K., Amage, A., &Natekar, B. 2020. Cricket match outcome prediction using machine learning. International journal, 5(7).
  3. Kapadia, K., Abdel-Jaber, H., Thabtah, F., &Hadi, W. 2020. Sport analytics for cricket game results using machine learning: An experimental study. Applied Computing and Informatics.
  4. Munir, F., Hasan, M., & Ahmed, S. 2015. Predicting a T20 cricket match result while the match is in progress (Doctoral dissertation, Brac University).
  5. Kampakis, S., & Thomas, W. 2015. Using machine learning to predict the outcome of english county twenty over cricket matches. arXiv preprint arXiv:1511.05837.
  6. Ahmed, W., Amjad, M., Junejo, K., Mahmood, T., & Khan, A. 2020. Is the performance of a cricket team really unpredictable? a case study on pakistan team using machine learning. Indian Journal of Science and Technology, 13(34), 3586-3599.
  7. Shetty, M., Rane, S., Pandita, C., &Salvi, S. 2020. Machine learning-based Selection of Optimal sports Team based on the Players Performance. In 2020 5th International Conference on Communication and Electronics Systems (ICCES) (pp. 1267-1272). IEEE.
  8. Singh, S., Aggarwal, Y., &Kundu, K. 2020. Quantitative Analysis of Forthcoming ICC Men’s T20 World Cup 2020 Winner Prediction using Machine Learning. International Journal of Computer Applications, 975, 8887.
  9. Nimmagadda, A., Kalyan, N. V., Venkatesh, M.,Teja, N. N. S., &Raju, C. G. 2018. Cricket score and winning prediction using data mining. International Journal for Advance Research and Development, 3(3), 299-302.
  10. Pathak, N., &Wadhwa, H. 2016. Applications of modern classification techniques to predict the outcome of ODI cricket. Procedia Computer Science, 87, 55-60.
  11. Jhanwar, M. G., &Pudi, V. 2016. Predicting the Outcome of ODI Cricket Matches: A Team Composition Based Approach. In MLSA@ PKDD/ECML.
  12. Kumar, J., Kumar, R., & Kumar, P. 2018. Outcome prediction of ODI cricket matches using decision trees and MLP networks. In 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC) (pp. 343-347). IEEE.
  13. Somaskandhan, P., Wijesinghe, G., Wijegunawardana, L. B., Bandaranayake, A., &Deegalla, S. 2017, December. Identifying the optimal set of attributes that impose high impact on the end results of a cricket match using machine learning. In 2017 IEEE International Conference on Industrial and Information Systems (ICIIS) (pp. 1-6). IEEE.
  14. Thenmozhi, D., Mirunalini, P., Jaisakthi, S. M., Vasudevan, S., Kannan, V. V., &Sadiq, S. 2019. MoneyBall-Data Mining on Cricket Dataset. In 2019 International Conference on Computational Intelligence in Data Science (ICCIDS) (pp. 1-5). IEEE.
  15. [Online]. Available:
  16. İshrat, R. İ. A. Z., Mushtaq, N., Jillani, M. M., &Navaz, U. 2019. Performance analysis of Pakistan super league players using principle component analysis approach. Scientific Journal of Mehmet AkifErsoy University, 2(4), 127-135.


Pakistan Super League, Batting, Bowling, Machine Learning, Prediction, Classification, Ranking