Data Science and ML in Cricket

 Objective of the data science in cricket  - Increate team performance  - Maximiser winning chances  Here's a simplified version: --- The IPL has expanded cricket, increasing the number of matches and the amount of data collected. Modern cricket data analysis involves tracking various factors like player positions, ball movements, shot types, delivery angle, spin, speed, and trajectory, which makes data cleaning and preprocessing more complex. **Dynamic Modeling** In cricket, numerous variables must be tracked, including player actions, ball attributes, and potential outcomes. The complexity of modeling depends on the type of predictive questions asked. Predictive models become especially challenging when analyzing hypothetical scenarios, like how a batsman’s shot might change with different ball angles or speeds. **Predictive Analytics Complexity** Cricket decision-making often relies on queries like "how often does a batsman play a specific shot against a certain b...
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Data Mining

Data Mining 
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Introduction 
Data mining finds valuable information hidden in large volumes of data. Data mining is the analysis of data and the use of software techniques for finding patterns and regularities in sets of data. The computer is responsible for finding the patterns by identifying the underlying rules and features in the data.

Databases used in Data Mining 
  • Flat Files.
  • Relational Databases.
  • DataWarehouse.
  • Transactional Databases.
  • Multimedia Databases.
  • Spatial Databases.
  • Time Series Databases.
  • World Wide Web(WWW)


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