Data Science and ML in Cricket

 Objective of the data science in cricket 

- Increate team performance 

- Maximiser winning chances 

Here's a simplified version:

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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 ball type" or "how a bowler adjusts based on a batsman’s response." These questions require detailed datasets and advanced models to accurately predict outcomes.