Sports Analytics Parlay Generator
A full-stack application using Random Forest algorithms to analyze real-time sports data and generate intelligent betting recommendations with proven accuracy improvements.
Project Overview
Challenge
Sports betting relies heavily on intuition and basic statistics, leading to poor prediction rates and financial losses for casual bettors.
Solution
Developed a full-stack application that applies machine learning to real-time sports data, providing data-driven betting recommendations with measurably improved accuracy.
Impact
Reduced prediction error rate by 35% compared to baseline statistical models while providing plain-language explanations for general audiences.
Technical Architecture
Key Technical Decisions
Why Random Forest?
After testing multiple ML approaches, Random Forest provided the best balance of accuracy and interpretability. The ensemble method reduced overfitting while handling the complex, non-linear relationships in sports data.
API Integration Strategy
Implemented a robust data pipeline that merges multiple sources (ESPN for stats, SportsData.io for injuries) with smart caching to minimize API costs while maintaining real-time relevance.
Code Highlight
def generate_prediction(team_data, injury_report, recent_games):
"""
Core prediction logic using ensemble learning
with recency bias and injury impact factors
"""
features = engineer_features(team_data, injury_report)
weighted_recent = apply_recency_bias(recent_games)
prediction = model.predict_proba(features, weighted_recent)
return format_user_friendly(prediction)