An open API service indexing awesome lists of open source software.

https://github.com/absarraashid3/kickmetrics

Kick Metrics is a cutting-edge web application transforming football analytics through data science, machine learning, and modern web development. It offers tools for analyzing player performance, predicting market value, and planning team strategies, catering to professional teams, scouts, analysts, and football enthusiasts.
https://github.com/absarraashid3/kickmetrics

Last synced: over 1 year ago
JSON representation

Kick Metrics is a cutting-edge web application transforming football analytics through data science, machine learning, and modern web development. It offers tools for analyzing player performance, predicting market value, and planning team strategies, catering to professional teams, scouts, analysts, and football enthusiasts.

Awesome Lists containing this project

README

          

# KickMetrics
KickMetrics is a cutting-edge web application that transforms football analytics through the integration of data science, machine learning, and modern web development. It offers tools for analyzing player performance, predicting market values, and planning team strategies, catering to professional teams, scouts, analysts, and football enthusiasts.

## Key Features
Player Performance Analysis: Utilize advanced metrics and visualizations to assess player strengths and weaknesses.
Market Value Prediction: Employ machine learning models to estimate player market values based on performance data.
Team Strategy Planning: Analyze team dynamics and optimize strategies using data-driven insights.
User-Friendly Interface: Navigate through an intuitive UI designed for both professionals and enthusiasts.

## Installation
To set up the project locally, follow these steps:

Clone the repository
git clone https://github.com/AbsarRaashid3/KickMetrics.git

Navigate to the project directory:
cd KickMetrics

Install backend dependencies:
cd backend
npm install

Install frontend dependencies:
cd ../frontend
npm install

## Usage
Data Import: Upload datasets containing player and match statistics.
Analysis Tools: Navigate to the analysis section to explore various metrics and visualizations.
Predictions: Use the prediction module to estimate player market values and potential performance.
Strategy Planning: Utilize team analysis tools to devise optimal game strategies.

## Technologies Used
Frontend:

React.js

Redux

Chart.js (for data visualization)

Axios

Backend:

Node.js

Express.js

MongoDB

Mongoose

Python (for machine learning models)

Flask

Others:

JWT for authentication

D3.js for advanced data visualizations

Docker for containerization