Global Football Match Results Analysis Using Big Data

Objective:

Analyze match results across major football leagues worldwide to identify patterns, regional differences, and develop predictive models for outcomes.

Key Leagues:

European Big Five (Premier League, La Liga, Serie A, Bundesliga, Ligue 1), MLS, J-League, and others.

Key Goals:

Identify factors influencing match results (home/away advantage, goal distribution).

Compare regional league characteristics (Europe, Americas, Asia).

Build predictive models for match outcomes and standings.

Offer data-driven insights for league management and strategy.

Data Sources:

Kaggle International Football Results (1872–2025, 48,000+ matches).

Football-Data.co.uk historical league results and stats.

Methods:

Python (Pandas, Scikit-learn) for data cleaning and analysis.

Machine learning (Random Forest, Neural Networks) for predictions.

Focus on post-2010 data (10,000+ matches).

Expected Deliverables:

Comparative report on global leagues.

Open-source prediction model.

Interactive data dashboard.

Timeline: 12 months