Sports Analytics: IPL Match Prediction
The Business Problem
A franchise team needs to know which players to target in the upcoming auction based on hidden statistical advantages, not just popular names.
Project Overview
Sports analytics is one of the fastest-growing fields in data. In this project, you will analyze over a decade of Indian Premier League (IPL) ball-by-ball data. You'll uncover which batsmen have the best strike rate in the death overs, which bowlers are most economical in the powerplay, and deploy your findings as a beautiful Python web app.
Step-by-Step Implementation
Follow this structured path to complete the project successfully.
Step 1: Data Wrangling
Merge the 'Matches' dataset with the 'Deliveries' dataset. Filter for specific overs (16-20) to analyze death over performance.
Step 2: Metric Calculation
Calculate custom metrics: Strike Rate, Economy Rate, and Dot Ball Percentage for every player.
Step 3: Visualizations
Use Plotly to create interactive scatter plots comparing Strike Rate vs Average.
Step 4: Build Web App
Write a Streamlit script (`app.py`) that allows users to select a player from a dropdown and instantly see their historical stats.
Step 5: Deploy
Push your code to GitHub and deploy it for free using Streamlit Cloud.
Expected Output
A live web link to your Streamlit app that anyone in the world can visit and interact with.
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