https://github.com/paulinhok14/csgo-datascience-project

📊 Analysis of CS:GO grenade usage patterns and their impact on match outcomes using data science and statistical methods.
https://github.com/paulinhok14/csgo-datascience-project

matplotlib mlflow numpy python scikit-learn scipy seaborn

Last synced: 6 months ago
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📊 Analysis of CS:GO grenade usage patterns and their impact on match outcomes using data science and statistical methods.

• Host: GitHub
• URL: https://github.com/paulinhok14/csgo-datascience-project
• Owner: paulinhok14
• Created: 2023-06-15T18:51:00.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2025-06-27T13:21:07.000Z (12 months ago)
• Last Synced: 2025-07-16T05:14:26.961Z (11 months ago)
• Topics: matplotlib, mlflow, numpy, python, scikit-learn, scipy, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 33.4 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# CS:GO Grenade Usage Analysis

![CSGO Banner](docs/csgo.png)

## Overview

This project analyzes the impact of grenade usage in Counter-Strike: Global Offensive (CS:GO) matches, focusing on the relationship between grenade damage and match outcomes/players rank across different maps.

## Key Findings

1. **Grenade Damage and Match Victory**

   - Statistical analysis with 90% confidence level shows that teams dealing more grenade damage tend to win matches.

   - The correlation varies significantly across different maps.

   - This relationship is not focused on causality analysis (Higher Damage > Wins or Wins > Higher Damage).

2. **Map-Specific Analysis**

   - Maps like de_overpass and de_cache show stronger correlation (~66.67%) between grenade damage and victory

   - Analysis includes heat maps of grenade landing positions for strategic insights



  

  



## Methodology

- Data analysis using Python (Pandas, Matplotlib, Seaborn)

- Statistical tests including:

  - Proportion comparison tests

  - Confidence interval analysis

  - Distribution analysis across different player rankings

## Data Features Analyzed

- Grenade landing coordinates (X, Y)

- Damage dealt (HP + Armor)

- Match outcomes

- Player rankings

- Map-specific statistics

## Stack

- Python 3.10

- Pandas

- Matplotlib

- Seaborn

- SciPy

- Scikit-Learn

- MLFlow

- Statistical Methods