UFC Predictor

Improved model and scraper

This repository is outdated
Reworked scraper and prediction model available at new repository Advanced UFC Analyzer

Table of Contents

• About
• Features
• Requirements
• Installation
• Current Model
• Contributing
• Contact

About

This project uses machine learning to predict the winner between 2 UFC fighters using historical fight data and fighter statistics. Using a supervised learning approach, the model is built on a neural network architecture that processes attributes of fighters, including height, weight, age, striking, grappling statistics and etc... By analyzing patterns and relationships in the historical matchups, it gives greater weight to features of fighters that have been more apparent in past winners.

We do some preprocessing on the data to make sure the quality of it is as good and not overfitted, then duplicate the fight data, swapping fighter1 for fighter2, balancing out the dataset, avoiding bias from the model so it focuses on the comparative features instead of the order of the fighters. When the data is ready, we train the model, we save it in the models/ directory so that we can reuse it without training the model every time a prediction is to be made.

For the dataset, I used jansen88's UFC data scraper, follow the instructions there.

Features

Data Handling:

• Extracted useful fighter statistics (height, weight, age, striking and grappling statistics)
• Calculated advantages (e.g. height/reach advantage, striking efficiency)
• Encoded fighter stances and weight classes numerically with LabelEncoder

Machine Learning Model:

• Built a neural network using TensorFlow/Keras in Python to predict fight outcomes
• With the preprocessed data, train the model and valide its performance to make sure we get reliable predictions
• Saved the model in models/ and load it from there

Prediction:

• Using the trained model, it predicts the favored fighter based on their statistics, providing a confidence score

API:

• I used FastAPI to make the backend to handle requests for predictions.

UI:

• Created the responsive frontend using Vue, TypeScript and TailwindCSS
• Users can input the names of two fighters and send the request to the API from the site

Requirements

Prerequisites

1. Python 3.10 or higher: Install from python.org.
2. pip: Python package manager (comes with Python installations).
3. Node.js (v14 or later): Install from nodejs.org

Python Dependencies

Install the required Python packages from requirements.txt found in the root folder.

pip install -r requirements.txt

Installation

1. Clone the repository

    git clone https://github.com/markbakos/ufc-predictor.git
    cd ufc-predictor
   

2. Install dependencies for frontend

   cd client
   npm install
   

3. Start the development server

• Frontend:

  cd client
  npm run dev
  

• Backend:

  # Make sure you are in root folder
  source venv/bin/activate
  uvicorn app.server:app
  

4. Include dataset

Place your dataset into the app/data/ folder. Make sure it is called complete_ufc_data.csv

5. Open the app in your browser
   

Navigate to http://localhost:5173 or the address provided in your terminal to use the app.

Current Model

The model currently shows accuracy of around ~66.5% for blind data.

Planned Features

The current model only compares the fighters' physical and fighting statistics, it still lacks many features, including:
(These might not improve the accuracy of the model, only a theory)

Implemented

• Fighter's win streaks, win rates and momentum (v1.1) ✔️
  Improved overall accuracy by about ~0.5% - ~0.8%.

Planned

• ⚠️ Checking betting odds
  Tried comparing advantage of past favorite/underdog status in fights, that lead to a ~-0.5% decrease in accuracy, have to try a different approach.
• Categorizing fighters and both checking IF they have fought against someone in that category, and if they did, how they competed
• Octagon time for fighters
• Improve the model to include more in the prediction (Predicted time, Outcome of DEC/SUB/KO)

Contributing

Feel free to fork this repository, make changes, and submit a pull request.

📧 Contact

For any inquiries, feel free to reach out:

Email: [email protected]
GitHub: markbakos