WolfStats | SKN St. Pölten Performance Analytics

WolfStats was a specialized scouting and performance evaluation platform developed as a university project for the Austrian football club SKN St. Pölten.

The project was designed to modernize the club's scouting department by providing a data-driven dashboard to compare player metrics across the Austrian football leagues. In recognition of its technical implementation and analytical depth, WolfStats was highlighted at the TDWI 2025 conference in the "Student’s Corner."

Project Overview

The primary objective of WolfStats was to move beyond traditional scouting methods. It utilized advanced metrics to identify undervalued talent and benchmarked SKN St. Pölten squad members against the rest of the league.

The application served as a proof-of-concept for how "Small Market" clubs can leverage data to compete with larger financial powers in the Admiral 2. Liga and the Austrian Bundesliga.

Key Features

• Player Comparison Engine: Provided side-by-side radar charts and percentile rankings that compared SKN players with league rivals.
• Performance Metrics: Tracked advanced statistics such as Expected Goals (xG), Expected Assists (xA), progressive passes, and defensive duel success rates.
• League-Wide Scouting: Featured a searchable database of players across the Austrian professional tiers, filterable by age, position, and contract status.
• Custom Rating System: Implemented a proprietary "WolfScore" that weighted specific attributes based on SKN St. Pölten’s tactical profile (e.g., high-pressing triggers).
• Visual Dashboards: Utilized interactive heatmaps and pass maps to visualize player tendencies.

Tech Stack

• Frontend: Next.js / React
• Styling: Tailwind CSS
• Deployment: Vercel
• Data Visualization: Recharts / D3.js
• Data Processing: Python / SQL for implementing the ETL pipeline
• Infrastructure: AWS (S3, RDS, Lambda) for data warehousing

Recognition: TDWI 2025

WolfStats was selected for presentation at the TDWI 2025 (Student’s Corner), a premier conference for Data & Insights. It was showcased as an exemplary use case for:

• Integrating external data sources into a cohesive BI dashboard.
• Translating complex statistical models into actionable insights for non-technical stakeholders (coaches and scouts).
• Applying data science principles within the niche domain of sports analytics.

Project Structure

├── components/          # Reusable UI components (Radars, Tables, Player Cards)
├── lib/                 # Data fetching logic and math utilities for metrics
├── pages/               # Next.js routing (Dashboard, Player Profile, Scouting)
├── public/              # Static assets (Club branding, icons)
└── styles/              # Global CSS and Tailwind configurations