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shane Hauck

Data science professional and collegiate soccer player interested in combining passions of data science and sports to provide analytical intelligence and actionable insights to drive industry success. Experienced in using data to solve complex problems and communicating results to a variety of audiences.

Projects

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.

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.

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.

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.

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.

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.

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.

how defensive shape effects offensive shot quality (tracking data exploration)

My submission to Stathletes 2022 Big Data Cup where tracking and play-by-play data from the women's hockey games during the 2022 Beijing Olympics was examined to develop a strategical analysis of defensive shape and offensive possession tactics.

Shorter Soccer Players: Better Suited for the Sport?

A research paper using FIFA data to explore the physiological benefits that shorter soccer players gain in regard to their taller counterparts.

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.

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.

Modeling Student Athlete's Mental Health

A group research project focusing on analyzing how being a student-athlete affects a student's mental health. Created multilevel models predicting the mental and physical health of students.

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.

Contact Me

If you want to know more or have any questions please feel free to reach out!!!