jdpipping/summer-lab
Jun 2025 – Jun 2025
1 Month active
Languages Used
RSQLStan
Technical Skills
Bayesian InferenceBootstrap MethodsClusteringConfidence IntervalsData AnalysisData Manipulation
kennyws
PROFILE
Kennyws
In June 2025, Landon Watts developed a cross-sport analytics platform within the jdpipping/summer-lab repository, delivering end-to-end data science workflows for basketball, football, baseball, and Spotify datasets. He engineered features, built predictive models, and created visualizations using R, Stan, and SQL, applying techniques such as Bayesian inference, clustering, and regression analysis. Landon established a formal experimentation lab framework to support rigorous model evaluation and reproducible research, consolidating analytics scripts for maintainability. His work enabled data-driven decision-making and accelerated experimentation cycles, providing a cohesive foundation for sports analytics initiatives and music data exploration while emphasizing reproducibility and business value.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
17Total
Bugs
0
Commits
17
Features
3
Lines of code
2,900
Activity Months1
Work History
June 2025
17 Commits • 3 Features
Jun 1, 2025June 2025 monthly summary for jdpipping/summer-lab: Delivered a comprehensive cross-sport analytics platform, established a formal experimentation lab, and advanced data science experiments. The work delivered end-to-end analytics—from feature engineering to predictions and visualizations—across basketball, football, baseball, and Spotify data, enabling data-driven insights and faster decision-making. Built a cohesive framework to evaluate models and ensure reproducibility, maintainability, and business value across sports analytics initiatives.
17 Commits • 3 Features
Jun 1, 2025June 2025 monthly summary for jdpipping/summer-lab: Delivered a comprehensive cross-sport analytics platform, established a formal experimentation lab, and advanced data science experiments. The work delivered end-to-end analytics—from feature engineering to predictions and visualizations—across basketball, football, baseball, and Spotify data, enabling data-driven insights and faster decision-making. Built a cohesive framework to evaluate models and ensure reproducibility, maintainability, and business value across sports analytics initiatives.
June 2025
Activity
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Quality Metrics
Correctness83.6%
Maintainability81.2%
Architecture81.2%
Performance69.4%
AI Usage20.0%
Skills & Technologies
Programming Languages
RSQLStan
Technical Skills
Bayesian InferenceBootstrap MethodsClusteringConfidence IntervalsData AnalysisData ManipulationData PreprocessingData VisualizationFeature EngineeringLinear RegressionMachine LearningPermutation TestingRR ProgrammingRegression Analysis
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