Projects

No Edge No Chance: The Impact of Setting the Edge on Zone Running Plays

Led an analytical project for the 2024 NFL Big Data Bowl, focusing on the defensive strategy of 'setting the edge' in football and its impact on zone run plays. In collaboration with NCAA DIII Carnegie Mellon University's Head Coach and Defensive Coordinator, we integrated their coaching insights into our research. Developed a comprehensive research paper, applying innovative data analysis methodologies, including the creation of the Edge Intensity Rating (EIR) to evaluate the real-time effectiveness of edge-setting.

Implementing Markov Chain Monte Carlo (MCMC) for Text Decryption

Developed an innovative text decryption tool using Markov Chain Monte Carlo (MCMC) methods, advancing computational cryptography by integrating mathematical algorithms and computer science principles.

How to Increase Annual Health Checkups in Vietnam: Recommendations for the Vietnam Ministry of Health

Worked collaboratively to develop a comprehensive analysis for Vietnam's Ministry of Health to identify factors influencing public participation in health check-ups, resulting in strategic recommendations to enhance their uptake, including prioritizing information quality, empathy, and reliability.

The Red Bull Way: Using Data to Define the "Red Bull Style of Play"

Presentation analyzes Red Bull Football (soccer) and explores what aspects of its company philosophy contribute to the distinct "Red Bull Style of Play". A metric was developed using Red Bull statistic percentiles from the last 6 seasons compared to MLS teams. This "Red Bull Style of Play" metric encapsulates the essence of their dynamic and appealing approach to the game while displaying how they differ from other teams. Also investigates the team performance from a famous NYRB game.

Predicting NFL Draft Prospects Success: Analyzing Techniques of Imputing Missing NFL Scouting Combine Data

This paper presents a methodology with machine learning for predicting the potential success of NFL players by combining NFL Combine data, college football statistics, and scouting grades. It addresses the challenge of missing NFL Scouting Combine data through four different data imputation techniques, showing that thoughtfully and intelligently imputing the missing data greatly enhances the accuracy of career success predictions.

Comparing Machine Learning Methods for Predicting Wins Above Replacement (WAR) in Major League Baseball

Study analyzing machine learning methods for predicting player Wins Above Replacement (WAR) in Major League Baseball (MLB). The study highlights the superiority of the Regularization method, specifically Lasso regression, over Random Forests and Neural Networks, offering practical insights for precise player performance forecasts and informed decision-making in MLB teams.

Data Science Mastery: Developing Comprehensive Education Modules for Effective Learning

Creating two educational introductory data science modules in R, one focusing on joining datasets with a Formula One database and the other on data visualization techniques using NFL Combine data, to provide learners with hands-on experience and practical skills in data manipulation and visualization.

Organizing For Success: A Tactical analysis of defensive structure in soccer

Comprehensive analysis of how coaches and analysts can utilize soccer tracking data analysis to improve a team's defensive performance. The paper provides practical insights into using hierarchical clustering to define defensive formations and make informed decisions on tactical strategies.

Soccer Scouting SQL Database

A comprehensive SQL database adhering to Boyce Codd Normal Form, utilizing publicly available data from Europe's top 5 leagues, which served as a centralized repository for efficient and analytical statistical research on players, and developed an interactive program tailored for scouts, streamlining the scouting process and enabling informed decision-making.

ENGLISH PREMIER LEAGUE ADVANCED PLAYER STATISTIC VISULAIZER

A Shiny application allowing users to visualize and compare advanced player statistics from the English Premier League. Various different variables are used to most accurately evaluate players quality and their style of play.

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