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
Tianshu Feng developed a suite of sports and music analytics tools in the jdpipping/summer-lab repository, delivering end-to-end pipelines for basketball, baseball, NFL, MLB, and Spotify data. Using R and Stan, Tianshu implemented Bayesian hierarchical models, regression-based park effects, and XGBoost-driven machine learning workflows. The work included robust data preprocessing, feature engineering, and visualization-ready outputs, supporting reproducible research and competition scoring. Tianshu addressed model reliability through targeted bug fixes and established modular scripts for NBA analytics, NFL win probability, and Spotify song attribution. The depth of engineering enabled data-driven insights for analysts, teams, and partners across multiple domains.
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
9 people
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
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