League of Legends Analytics

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A Python-based analytics project that leverages the Riot Games API to analyze League of Legends match data and generate Markov chain-based predictions for game outcomes.

Overview

This project provides tools and utilities for:

• Fetching and analyzing League of Legends match data
• Processing match timelines and game states
• Generating Markov chain transition matrices for game state prediction
• Analyzing player statistics and performance metrics

Features

• Riot API Integration: Comprehensive wrapper for the Riot Games API

  • Summoner information retrieval
  • Match history and timeline data
  • Champion mastery statistics
  • Rank information

• Match Analysis:

  • Detailed match timeline processing
  • Gold difference tracking
  • Kill difference analysis
  • Structure and objective control tracking
  • Team state comparison

• Markov Chain Modeling:

  • Game state transition analysis
  • State space modeling for game progression
  • Transition probability matrix generation
  • Game outcome prediction capabilities

Prerequisites

• Python 3.12 or higher
• Riot Games API key
• Required Python packages (see requirements.txt)

Installation

1. Clone the repository:

git clone https://github.com/yourusername/LoL-Analytics.git
cd LoL-Analytics

2. Create and activate a virtual environment:

python -m venv .venv
source .venv/bin/activate  # On Windows, use `.venv\Scripts\activate`

3. Install dependencies:

pip install -r requirements.txt

4. Set up your Riot Games API key:
   • Get an API key from the Riot Developer Portal
   • Add your API key to the configuration or environment variables

Project Structure

LoL-Analytics/
├── notebooks/
│   └── match_data.ipynb    # Jupyter notebook for data analysis
├── utils/
│   ├── riot_api.py        # Riot Games API wrapper
│   └── markov_utils.py    # Markov chain utilities
├── jsons/
│   ├── data/             # Processed data storage
│   ├── frames/           # Match frame data
│   └── timelines/        # Match timeline data
└── README.md

Usage

1. Basic summoner information retrieval:

from utils.riot_api import RiotAPI

api = RiotAPI(api_key)
puuid = api.get_puuid_by_name("summoner_name", "tag_line")
summoner_data = api.get_summoner_by_puuid(puuid)

2. Match analysis:

matches = api.get_ranked_matches_by_name("summoner_name", "tag_line")
for match_id in matches:
    timeline = api.get_match_timeline(match_id)
    info_dicts = api.extract_timeline_frame_info(timeline)

3. Markov chain analysis:

from utils.markov_utils import MarkovUtils

markov_util = MarkovUtils()
markov_util.convert_raw_info_list_to_markov_data(info_dicts)
transitions = markov_util.get_markov_transitions(info_dicts)

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Riot Games API for providing access to League of Legends data
• The League of Legends community for inspiration and feedback