jdpipping/summer-lab
Jun 2025 – Jun 2025
1 Month active
Languages Used
RStan
Technical Skills
Bayesian InferenceData AnalysisData VisualizationEmpirical BayesMachine LearningR Programming
Noahsonnenklar
PROFILE
Noahsonnenklar
Developed a suite of five reproducible R Markdown labs for the jdpipping/summer-lab repository, advancing sports analytics education through data-driven projects. Focused on NBA, NFL, and NCAA topics, the work integrated Bayesian inference, empirical Bayes, and statistical modeling to analyze player performance, team strengths, and game outcomes. Leveraged R and Stan to implement linear and logistic regression, splines, and confidence interval comparisons, emphasizing robust data analysis and visualization. The labs provided scalable, classroom-ready templates that facilitate deeper insights and data-driven decision-making in sports analytics, demonstrating a strong command of statistical methods and reproducible research practices within a one-month period.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
6Total
Bugs
0
Commits
6
Features
5
Lines of code
1,676
Activity Months1
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Work History
June 2025
6 Commits • 5 Features
Jun 1, 2025June 2025 — Delivered a cohesive set of reproducible R Markdown labs in jdpipping/summer-lab that advance sports analytics education and analytics capabilities. Implemented five data-driven labs spanning NBA/NFL/NCAA topics, empirical Bayes trajectories, and Bayesian team-strength modeling, with an emphasis on data loading, visualization, and robust statistical methods. These efforts provide scalable, classroom-ready templates that enable data-driven decisions and deeper insights for sports analytics.
6 Commits • 5 Features
Jun 1, 2025June 2025 — Delivered a cohesive set of reproducible R Markdown labs in jdpipping/summer-lab that advance sports analytics education and analytics capabilities. Implemented five data-driven labs spanning NBA/NFL/NCAA topics, empirical Bayes trajectories, and Bayesian team-strength modeling, with an emphasis on data loading, visualization, and robust statistical methods. These efforts provide scalable, classroom-ready templates that enable data-driven decisions and deeper insights for sports analytics.
June 2025
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Quality Metrics
Correctness81.6%
Maintainability80.0%
Architecture80.0%
Performance70.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
RStan
Technical Skills
Bayesian InferenceData AnalysisData VisualizationEmpirical BayesMachine LearningR ProgrammingStan ModelingStatistical Modeling
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