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About This Project

Technical overview of the tennis predictions platform

Project Overview

This tennis predictions platform is a full-stack web application that combines machine learning with modern web development to provide data-driven tennis match predictions. The system scrapes current tennis data, processes it through multiple machine learning models, and serves predictions through an interactive Django web interface.

🧠 Machine Learning
Scikit-learn Pandas NumPy Joblib
🌐 Backend
Django Python SQLite REST APIs
🎨 Frontend
HTML5 CSS3 JavaScript Bootstrap 5
🛠️ DevOps
AWS EC2 Playwright Web Scraping Git

System Architecture

1
Data Collection

Automated web scraping using Playwright to collect match statistics, player rankings, and historical performance data from TennisAbstract

2
Feature Engineering

Processing raw data into meaningful features including player form, surface performance, head-to-head records, and recent match statistics

3
Model Training

Multiple ML models including Logistic Regression and Random Forest classifiers trained on historical match data with cross-validation

4
Prediction Serving

Ensemble model combining predictions from multiple algorithms to generate win probabilities and convert to American moneyline odds

5
Web Interface

Django-powered frontend with responsive design, real-time updates, and interactive match analysis features

Machine Learning Implementation

Logistic Regression
Statistical Model

Classical statistical approach that models the probability of match outcomes using a logistic function. Handles linear relationships well and provides interpretable coefficients.

Key Features: Player rankings, recent form, surface performance
Random Forest
Ensemble Method

Ensemble of decision trees that reduces overfitting and captures complex non-linear relationships. Robust to outliers and handles feature interactions effectively.

Key Features: All available features with feature importance analysis
Ensemble Model
Meta-Learner

Combines predictions from multiple models using weighted averaging. Leverages strengths of different algorithms while mitigating individual weaknesses.

Approach: Weighted average of Logistic Regression and Random Forest predictions

Database Design

Player
id (PK)
name
ranking
country
1 → ∞
PlayerMatch
id (PK)
player_id (FK)
opponent_id (FK)
tournament_id (FK)
date
surface
round
score
won
1 → 1
PlayerMatchServeStats
match_id (FK)
ace_pctg
df_pctg
fs_pctg
fs_w_pctg
ss_w_pctg

Performance & Optimization

~65%
Prediction Accuracy
<2s
Page Load Time
500+
Players Tracked
10000+
Matches Analyzed
Key Optimizations:
  • Lazy Loading: Implemented pagination and AJAX loading for match data
  • Caching: Static file caching and database query optimization
  • Responsive Design: Mobile-first CSS with optimized asset delivery
  • Efficient Scraping: Rate-limited concurrent requests with error handling