League Player Ranking Report Generator
About Project
- Type: End-to-End Sports Analytics Automation
- Deliverables: Coach-ready Excel ranking reports generated directly from raw evaluation data
- Turnaround Time: Full roster reports generated in under 1 minute with zero manual intervention
Objective
Design a scalable, fair, and transparent player evaluation system that converts raw tryout and performance data into standardized rankings coaches can trust for draft decisions, roster balancing, and player development.
Tools & Features
Python, Pandas, Excel Automation, Statistical Normalization, Z-Score & Robust Scaling, Weighted Scoring Models, Outlier Handling, Data Quality Checks
Key Work & Impact
Built a fully automated Python pipeline that ingests raw evaluation files and outputs standardized Excel ranking reports with consistent formatting and scoring logic.
Implemented age-group–specific normalization so players are evaluated only against comparable peers, preventing unfair comparisons across groups.
Mapped all skill and overall scores to an intuitive 0–10 scale, where 5 represents the group average, making rankings easy to interpret for non-technical stakeholders.
Designed a flexible weighted scoring framework that allows leagues or coaches to customize how skills contribute to overall rankings without rewriting the pipeline.
Handled missing and incomplete data explicitly by re-normalizing weights, ensuring players are not unfairly penalized due to unavailable measurements.
Reduced the influence of extreme outliers through capped normalization, producing more stable and defensible rankings while preserving meaningful performance differences.
Structured reports for real-world use: clean layouts, side-by-side rankings, clear labels, and transparent breakdowns explaining why each player ranks where they do.