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Hello, my name is Peter Majors, and I'm currently an Systems Engineer @ Marsh McLennan.

I am a recent graduate of Fordham University in New York City, where I was the founder and president of the Fordham Sports Anaytics Society. I work with Python, R, SQL, and Javascript - and my interests sit at the intersection of sports, traditional statistics, and machine learning. Within machine learning & stats, my interests include supervised learning, feature engineering, and bayesian methods. In my current role, I work primarily with SQL databases (PostgreSQL) and REST APIs (FastAPI). For now, this account will store personal projects related to my independent sports analytics research and previous case competitions.

Case Competitions:

1. 2023 NFL Big Data Bowl Kaggle Submission

• Developed OLIZ, an orientation-responsive zone for pass blockers used to detect the presence of an oncoming pass rusher, to develop measurements related to the performance of pass blockers
• Leveraged Python, R, and data visualization tools to develop an XGBoost model to analyze the effect of movements in and around the OLIZ and predict player performance

2. Spring 2022 March Data Cruch Madness Fordham University Graduate Gabelli School of Business Presentation

• Received third-place honors (of 20+ teams) as junior undergraduate student in a graduate-level competition
• Utilized XGBoost to predict every possible result of every possible NCAA 2022 March Madness matchup
• Used advanced college basketball statistics from kenpom.com to develop custom feature and algorithm

3. Spring 2022 Syracuse Football Analytics Blitz Presentation

• Placed in top 4 of 17 teams, created and presented on the optimal game plan to defend the Kansas City Chiefs
• Utilized Logistic Regression to predict type of plays run by Kansas City Chiefs offense and develop a defensive playbook

Twitter: @petermajors LinkedIn: petermajors