Abstract
The chapter explores the use of machine learning, data science, and Python in the context of cricket analytics. It highlights the importance of interdisciplinary collaboration and its potential to enhance the accuracy and speed of cricket analytics. It discusses the various data sources that can be used for cricket analytics and how machine learning algorithms can extract valuable insights. The chapter also provides an overview of various Python libraries commonly used in cricket analytics and explains how they can be used for data cleaning, feature engineering, and model building. Additionally, the chapter discusses the challenges and limitations of cricket analytics and provides suggestions for future research directions.
TopBackground
In recent years, there has been a surge of interest in applying machine learning and data science techniques to cricket analytics, encompassing player performance analysis, match outcome prediction, and team selection. Researchers have employed various algorithms and approaches to extract insights from cricket data.
Player performance analysis has been a prominent focus, utilizing machine learning algorithms to identify key performance indicators. For instance, Mittal et al. (Mittal et al., 2021) developed a predictive model that considered batting average, strike rate, and bowling economy to assess player performance and predict match results. Vestly et al. (Vestly et al., 2023) utilized data science techniques to analyze batting techniques and uncover patterns correlated with successful innings.
Match prediction has also garnered attention, with researchers employing machine learning algorithms to forecast match outcomes based on historical data. Priya et al. (Priya et al., 2022) utilized logistic regression, random forest, k-Nearest neighbor, support vector machine, and decision tree algorithms to predict winners in T20 cricket matches. Anuraj et al. (Anuraj et al., 2023) explored sports data mining approaches, considering factors such as venue conditions, team rankings, and past performance, to develop predictive models for T20 International World Cup matches.
Key Terms in this Chapter
Visual Analytics: The use of visual representations, such as graphs and charts, to facilitate data exploration and analysis.
Analytics: Collecting, processing, and analyzing data to obtain insights and make informed decisions.
Machine Learning: A subfield of artificial intelligence that involves the development of algorithms and models that enable computers to learn and improve from experience.
Data Science: A multidisciplinary field that uses statistical and computational methods to extract knowledge and insights from data.
Supervised Learning: A type of machine learning where the algorithm is trained on labeled data, meaning that the desired output is known beforehand, and the algorithm learns to map the input to the output.
Python: An open-source programming language widely used in data science and machine learning.
Predictive Modeling: Using statistical algorithms and machine learning techniques to build models that can predict future outcomes.
Regression Analysis: A statistical technique used to model the relationship between a dependent variable and one or more independent variables.