https://github.com/ashrockzzz2003/cricket_best_11_prediction

This project is an attempt to use regression to predict the best XI players for the 2023 Cricket World Cup given a team and an opponent.
https://github.com/ashrockzzz2003/cricket_best_11_prediction

api classification clustering cricket espncricinfo flask ml python regression web-scraping

Last synced: about 2 months ago
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This project is an attempt to use regression to predict the best XI players for the 2023 Cricket World Cup given a team and an opponent.

• Host: GitHub
• URL: https://github.com/ashrockzzz2003/cricket_best_11_prediction
• Owner: Ashrockzzz2003
• License: mit
• Created: 2023-10-24T08:31:05.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-01-07T09:45:57.000Z (over 1 year ago)
• Last Synced: 2025-02-11T21:00:02.692Z (4 months ago)
• Topics: api, classification, clustering, cricket, espncricinfo, flask, ml, python, regression, web-scraping
• Language: Jupyter Notebook
• Homepage:
• Size: 43.7 MB
• Stars: 0
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# World Cup Best-XI Selection with ML

- This project is an attempt to use ideas of ML to predict the best XI players for the 2023 Cricket World Cup given a team, venue and an opponent. 

- The data used for this project is ODI records of players from 2023 Cricket World Cup. 

- The data was gathered from [ESPN Cricinfo](https://www.espncricinfo.com/) using web scraping.

## Problem Statements

- How many runs will a `player` score against a particular `opposition` in a `venue`.

- What will be the economy of a `player` against an `opposition` in a `venue`.

- In the next match, how will a `player` get out (Caught behind, stumped, run out ... etc) against an `opposition` in a `venue`.

- Given a new domestic player will he be a X-Factor Bowler like Bumrah, Cummins, Strong middle-order batsman like Shreyas Iyer, K L Rahul or an explosive all rounder like Maxwell or a top class batsman like Kohli.

## Where to Start

- The `final_data` folder contains the data used for this project.

- The `predicting_runs.ipynb`, `predicting_economy.ipynb` and `predicting_Dismissal.ipynb` notebooks contain the ML models used for predicting runs, economy and predicting dismissal respectively.

- The `clustering_players.ipynb` contains the code to cluster the players into 4 groups, X-Factor Bowlers, Strong Middle Order and Wicket Keeping batsmen, Explosive All Rounders and Top Class Batsmen.

- The `post_clustering.ipynb` contains the code that classifies a new player into one of the clusters from above!

## Team

| Team |

| ---- |

|`Ashwin Narayanan S`|

|`Sreepadh`|

|`Ananya R`|

|`Arjun P`|

|`Kona Deepak`|