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 Collection

Collecting data
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Your data analysis is incomplete without your data. To start your analysis you are on first stage to get data from multiple sources and department for better analysis .
Now we will discuss getting data .
  • Sources 
  • Platform
  • Services 
  • Departments 
Data collection : 
What is Data collection : 
Data collection can be required for the multiple objective and multiple business processes i.e. Sales , Marketing , Finance , Hiring etc. 
To collect the data there are many tools are available 

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