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IPL Prediction Using Machine Learning


Affiliations
1 Student , Vidyavardhini's College of Engineering and Technology, K.T. Marg, Vartak College Campus Vasai Road, Vasai-Virar, Maharashtra - 401 202, India
2 Student, Vidyavardhini's College of Engineering and Technology, K.T. Marg, Vartak College Campus Vasai Road, Vasai-Virar, Maharashtra - 401 202, India
3 Assistant Professor, Vidyavardhini's College of Engineering and Technology, K.T. Marg, Vartak College Campus Vasai Road, Vasai-Virar, Maharashtra - 401 202, India

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Cricket is amongst the most popular sports in the world. Indian Premier League, more commonly known as IPL is the biggest domestic cricket league in the world. It generates a lot of revenue along with excitement among fans. Many bookers, bettors, and fans like to predict the outcome of a particular match which changes with every ball. This project studies and compares different Machine Learning techniques that can be applied to predict the outcome of a match. Features like team strength and individual strength of a player are also included along with conventional features like toss, home ground, weather and pitch conditions that are taken into account for predicting the result. Machine Learning algorithms such as Naïve Bayes, Random Forest Classifier, Logistic Regression, XGBoost, AdaBoost, and Decision Tree are selected to determine the predictive model with highest accuracy.


Keywords

AdaBoost, Decision Tree, Indian Premier League, Machine Learning, Naïve Bayes, Logistic Regression, Random Forest Classifier, XGBoost.
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  • K. Kapadia, H. Abdel-Jaber, F. Thabtah, and W. Hadi. "Sport ana lytics for cricket game results using machine learning: An experimental study," Appl.Comput. Inform., vol. ahead-of-print, no. ahead-of print, 2019, doi: 10.1016/j.aci.2019.11.006.
  • P. K. Dubey, H. Suri, and S. Gupta, “Naïve Bayes algorithm based match winner prediction model for T20 Cricket," in S. S. Dash, S. Das, B. K. Panigrahi (eds) Intell. Comput. Appl.. Advances Intell. Syst. Comput., vol 1172, 2021. Springer, Singapore, doi: 10.1007/978-981-15-5566-4_38.
  • A. Tripathi, R. Islam, V. Khandor, and V. Murugan, "Prediction of IPL matches using Machine Learning while tackling ambiguity in results," Indian J. Sci. Technol., vol. 13, no. 38, pp. 4013-4035, 2020, doi: 10.17485/IJST/v13i38.1649.
  • Espncricinfo. [Online].Available: https://www.espncricinfo.com/
  • Indian Premier League. https://www.iplt20.com/
  • “kaggle”.[Online]. Available: https://www.kaggle.com/datasets
  • Dream11. https://www.dream11.com/
  • H. Barot, A. Kothari, P. Bide, B. Ahir, and R. Kankaria, "Analysis and prediction for the Indian Premier League," Int. Conf. Emerg. Technol., 2020, pp. 1-7, doi: 10.1109/INCET49848.2020.9153972.
  • Beautiful Soup Python library. https://pypi.org/project/beautifulsoup4/

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  • IPL Prediction Using Machine Learning

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Authors

Abhineet Menon
Student , Vidyavardhini's College of Engineering and Technology, K.T. Marg, Vartak College Campus Vasai Road, Vasai-Virar, Maharashtra - 401 202, India
Dhruv Khator
Student, Vidyavardhini's College of Engineering and Technology, K.T. Marg, Vartak College Campus Vasai Road, Vasai-Virar, Maharashtra - 401 202, India
Dhru Prajapati
Student, Vidyavardhini's College of Engineering and Technology, K.T. Marg, Vartak College Campus Vasai Road, Vasai-Virar, Maharashtra - 401 202, India
Archana Ekbote
Assistant Professor, Vidyavardhini's College of Engineering and Technology, K.T. Marg, Vartak College Campus Vasai Road, Vasai-Virar, Maharashtra - 401 202, India

Abstract


Cricket is amongst the most popular sports in the world. Indian Premier League, more commonly known as IPL is the biggest domestic cricket league in the world. It generates a lot of revenue along with excitement among fans. Many bookers, bettors, and fans like to predict the outcome of a particular match which changes with every ball. This project studies and compares different Machine Learning techniques that can be applied to predict the outcome of a match. Features like team strength and individual strength of a player are also included along with conventional features like toss, home ground, weather and pitch conditions that are taken into account for predicting the result. Machine Learning algorithms such as Naïve Bayes, Random Forest Classifier, Logistic Regression, XGBoost, AdaBoost, and Decision Tree are selected to determine the predictive model with highest accuracy.


Keywords


AdaBoost, Decision Tree, Indian Premier League, Machine Learning, Naïve Bayes, Logistic Regression, Random Forest Classifier, XGBoost.

References





DOI: https://doi.org/10.17010/ijcs%2F2022%2Fv7%2Fi3%2F171267