Bayesian Research for Sport Analytics - Web Scraping Project Overview

Project Description:

This project aimed to apply Bayes' Theorem to the analysis of the New England Patriots' performance, focusing on the impact of night games on their winning probability. Utilizing sophisticated web scraping techniques with Bash and Python, data was extracted from www.sports-reference.com to support our Bayesian statistical analysis.

Key Responsibilities and Achievements:

• Data Extraction and Preprocessing: Employed Bash for initial data collection and Python for detailed scraping, parsing, and data structuring from Pro Football Reference, ensuring a robust dataset for analysis.
• Bayesian Analysis Implementation: Applied Bayes' Theorem to calculate the prior, likelihood, and posterior probabilities, elucidating the relationship between night games and winning outcomes for the Patriots.
• Strategic Insights Development: Translated complex statistical findings into actionable strategies, suggesting adjustments in game scheduling to potentially enhance the team's winning probabilities.
• Technical Skill Enhancement: Advanced proficiency in web scraping, data analysis, and Bayesian statistics, demonstrating a seamless integration of technical skills to address real-world problems.

Outcomes:

The analysis provided nuanced insights into the Patriots' performance dynamics, suggesting a minimal impact of night games on overall winning probabilities. However, the project highlighted the potential of data-driven approaches in sports analytics and strategic planning.

The methodology and findings pave the way for further research and have implications for scheduling strategies in professional sports.

Technical and Leadership Skills Demonstrated:

• Advanced Web Scraping with Bash and Python
• Bayesian Statistical Analysis
• Data-driven Strategic Planning
• Effective Project Management and Team Leadership
• Clear Communication of Complex Analytical Concepts