jdpipping/summer-lab
Jun 2025 – Jul 2025
2 Months active
Languages Used
RStan
Technical Skills
Bayesian AnalysisBayesian StatisticsBootstrap MethodsClusteringConfidence IntervalsData Analysis
Tianshu Feng
PROFILE
Tianshu Feng
Developed a suite of sports analytics and machine learning tools within the jdpipping/summer-lab repository, delivering end-to-end pipelines for basketball, baseball, NFL, MLB, diving, and Spotify data. Leveraged R and Stan to implement Bayesian hierarchical models, regression analyses, and permutation tests, enabling robust statistical inference and reproducible workflows. Built modular R scripts for NBA analytics, park effects, and NFL win probability, while also creating a Spotify song features prediction pipeline using XGBoost and cross-validation. Addressed model reliability through targeted bug fixes and established visualization-ready outputs, supporting data-driven decision making for analysts, teams, and competition-based machine learning tasks.
Overall Statistics
Feature vs Bugs
80%Features
Repository Contributions
19Total
Bugs
2
Commits
19
Features
8
Lines of code
2,170
Activity Months2
Your Network
20 peopleShared Repositories
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aiwenli123Member
Arnab ChaudhuriMember
bucknerm28Member
Dawei SunMember
dbukasaMember
Maximilian J. GebauerMember
Henry GaudetMember
Henry HuangMember
Work History
July 2025
1 Commits • 1 Features
Jul 1, 2025Month: 2025-07 — Delivered a new Spotify features prediction workflow in R, leveraging XGBoost and robust cross-validation. The work is located in jdpipping/summer-lab and centers on predicting a 'Added by' label from song features and metadata, supporting competition scoring and data-driven attribution insights.
1 Commits • 1 Features
Jul 1, 2025Month: 2025-07 — Delivered a new Spotify features prediction workflow in R, leveraging XGBoost and robust cross-validation. The work is located in jdpipping/summer-lab and centers on predicting a 'Added by' label from song features and metadata, supporting competition scoring and data-driven attribution insights.
July 2025
June 2025
18 Commits • 7 Features
Jun 1, 2025June 2025 highlights for jdpipping/summer-lab: Delivered cross-domain sports analytics capabilities across basketball, baseball, NFL, MLB/diving data, and Bayesian labs; established end-to-end pipelines, reproducible scripts, and visualization-ready outputs that enable data-driven decision making for teams, analysts, and partners. Also stabilized model behavior with targeted bug fixes to improve reliability of Bayesian updates and scoring logic.
June 2025
18 Commits • 7 Features
Jun 1, 2025June 2025 highlights for jdpipping/summer-lab: Delivered cross-domain sports analytics capabilities across basketball, baseball, NFL, MLB/diving data, and Bayesian labs; established end-to-end pipelines, reproducible scripts, and visualization-ready outputs that enable data-driven decision making for teams, analysts, and partners. Also stabilized model behavior with targeted bug fixes to improve reliability of Bayesian updates and scoring logic.
Activity
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Quality Metrics
Correctness81.2%
Maintainability81.2%
Architecture77.0%
Performance69.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
RStan
Technical Skills
Bayesian AnalysisBayesian StatisticsBootstrap MethodsClusteringConfidence IntervalsData AnalysisData PreprocessingData SimulationData VisualizationFeature EngineeringLinear RegressionMachine LearningPermutation TestsR ProgrammingSimulation
Repositories Contributed To
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