PranayN23/NFLResourceAnalysis
Oct 2024 – Apr 2025
4 Months active
Languages Used
CSVPythonJupyter NotebookCSSJavaScript
Technical Skills
Data AnalysisData EngineeringMachine LearningPandasPythonScikit-learn
ktandon05
PROFILE
Ktandon05
Pranay developed a suite of data-driven features for the NFLResourceAnalysis repository, focusing on player analytics, forecasting, and resource allocation. Over four months, he engineered end-to-end pipelines for wide receiver performance prediction, implemented RNN-based models with TensorFlow and Keras, and automated NFL cap space data extraction using Python and BeautifulSoup. He also built a player rankings system with a Flask backend and React frontend, integrating MongoDB for data aggregation. Pranay’s work emphasized robust data engineering, clear visualization, and reliable API development, resulting in stable, maintainable tools that support scouting, benchmarking, and decision-making across multiple aspects of NFL analytics.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
6Total
Bugs
0
Commits
6
Features
5
Lines of code
9,757
Activity Months4
Work History
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025: Delivered end-to-end Player Rankings feature for PranayN23/NFLResourceAnalysis, adding a backend API and frontend UI to rank players by position, year, and snap counts. The backend aggregates data across teams from MongoDB; the frontend provides a search-based UI and a results table. Also fixed API route issues to ensure accurate, reliable rankings. The work enables data-driven scouting, faster decision-making, and a clearer view of player performance.
1 Commits • 1 Features
Apr 1, 2025April 2025: Delivered end-to-end Player Rankings feature for PranayN23/NFLResourceAnalysis, adding a backend API and frontend UI to rank players by position, year, and snap counts. The backend aggregates data across teams from MongoDB; the frontend provides a search-based UI and a results table. Also fixed API route issues to ensure accurate, reliable rankings. The work enables data-driven scouting, faster decision-making, and a clearer view of player performance.
April 2025
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025: Delivered the NFL Cap Space Data Scraping Script in PranayN23/NFLResourceAnalysis to automate gathering of Spotrac cap space data across multiple years. The script generates per-year CSV reports showing cap space by player position, enabling rapid assessment of team resource allocation and scenario planning. Work was anchored by commit 9542665dd3d6c6dc633d56275a9a6c57fe0e388a: Completed Scraping Cap Data. No major bugs reported this period; MVP is stable and ready for recurring seasonal updates. Business impact includes accelerated data-driven budgeting, improved visibility into positional cap allocations, and a foundation for ongoing analytics pipelines. Technologies demonstrated include Python scripting, web scraping, CSV generation, and data extraction from Spotrac; demonstrated ability to deliver business-value features on a tight timeline.
February 2025
1 Commits • 1 Features
Feb 1, 2025February 2025: Delivered the NFL Cap Space Data Scraping Script in PranayN23/NFLResourceAnalysis to automate gathering of Spotrac cap space data across multiple years. The script generates per-year CSV reports showing cap space by player position, enabling rapid assessment of team resource allocation and scenario planning. Work was anchored by commit 9542665dd3d6c6dc633d56275a9a6c57fe0e388a: Completed Scraping Cap Data. No major bugs reported this period; MVP is stable and ready for recurring seasonal updates. Business impact includes accelerated data-driven budgeting, improved visibility into positional cap allocations, and a foundation for ongoing analytics pipelines. Technologies demonstrated include Python scripting, web scraping, CSV generation, and data extraction from Spotrac; demonstrated ability to deliver business-value features on a tight timeline.
November 2024
3 Commits • 2 Features
Nov 1, 2024Concise monthly summary for 2024-11 focusing on key accomplishments, business value, and technical excellence for the NFLResourceAnalysis repo. Highlights reflect delivered features, stability improvements, and the resulting impact on data-driven decision-making in player scouting.
3 Commits • 2 Features
Nov 1, 2024Concise monthly summary for 2024-11 focusing on key accomplishments, business value, and technical excellence for the NFLResourceAnalysis repo. Highlights reflect delivered features, stability improvements, and the resulting impact on data-driven decision-making in player scouting.
November 2024
October 2024
1 Commits • 1 Features
Oct 1, 2024October 2024 monthly summary for PranayN23/NFLResourceAnalysis. Focused on delivering end-to-end WR analytics capabilities and strengthening data integrity for predictive modeling. 1) Key features delivered: - Wide Receiver Data Integration and Prediction Pipeline: Created Combined_WR.csv by merging data from data.csv and wrPFF.csv, refactored WR_Input_Combine.py to support the new data model, and added WR_predictions.py and WR_team_predictions to enable both player- and team-level predictions. - Baseline predictions established with commit 764b0b4fbf1b9f9324927dba68df4e1b462d0fdb (Base Predictions). 2) Major bugs fixed: - No major bugs were reported this month. Minor robustness improvements were made around data merging to ensure stable generation of Combined_WR.csv. 3) Overall impact and accomplishments: - End-to-end WR analytics pipeline now supports data-driven performance forecasting for players and teams, enabling more reliable scouting, benchmarking, and decision-making. - The work lays a scalable foundation for future feature expansion and model enhancements across the NFLResourceAnalysis repository. 4) Technologies/skills demonstrated: - Python data engineering (ETL-style data merging and scripting) - Data modeling and feature integration (Combined_WR.csv, WR_Input_Combine.py) - Predictive modeling workflow (WR_predictions.py, WR_team_predictions) - Version control and collaborative development (commit traceability with a Base Predictions commit)
October 2024
1 Commits • 1 Features
Oct 1, 2024October 2024 monthly summary for PranayN23/NFLResourceAnalysis. Focused on delivering end-to-end WR analytics capabilities and strengthening data integrity for predictive modeling. 1) Key features delivered: - Wide Receiver Data Integration and Prediction Pipeline: Created Combined_WR.csv by merging data from data.csv and wrPFF.csv, refactored WR_Input_Combine.py to support the new data model, and added WR_predictions.py and WR_team_predictions to enable both player- and team-level predictions. - Baseline predictions established with commit 764b0b4fbf1b9f9324927dba68df4e1b462d0fdb (Base Predictions). 2) Major bugs fixed: - No major bugs were reported this month. Minor robustness improvements were made around data merging to ensure stable generation of Combined_WR.csv. 3) Overall impact and accomplishments: - End-to-end WR analytics pipeline now supports data-driven performance forecasting for players and teams, enabling more reliable scouting, benchmarking, and decision-making. - The work lays a scalable foundation for future feature expansion and model enhancements across the NFLResourceAnalysis repository. 4) Technologies/skills demonstrated: - Python data engineering (ETL-style data merging and scripting) - Data modeling and feature integration (Combined_WR.csv, WR_Input_Combine.py) - Predictive modeling workflow (WR_predictions.py, WR_team_predictions) - Version control and collaborative development (commit traceability with a Base Predictions commit)
Activity
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Quality Metrics
Correctness80.0%
Maintainability76.6%
Architecture76.6%
Performance73.4%
AI Usage33.4%
Skills & Technologies
Programming Languages
CSSCSVJavaScriptJupyter NotebookPython
Technical Skills
API DevelopmentBackend DevelopmentBeautifulSoupData AnalysisData EngineeringData PreprocessingData VisualizationDatabase IntegrationDeep LearningFeature EngineeringFlaskFrontend DevelopmentKerasMachine LearningMatplotlib
Repositories Contributed To
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