🏃 Running Performance Analytics

Data-Driven Insights from 5 Months of Training | July - November 2025

346
Total Miles
36
Total Runs
8:33
Avg Pace (min/mi)
51.2
Longest Run (mi)

📈 Volume Progression: 5x Growth in 5 Months

Key Insight: Increased weekly mileage from 10 miles to 52 miles over 5 months, demonstrating consistent progression and disciplined training buildup. The upward trend shows systematic volume increases while managing injury risk.

📅 Training Pattern: Friday Long Runs

Key Insight: 47% of total mileage (160+ miles) completed on Fridays, with an average Friday run distance of 32 miles. This reveals a structured weekly training pattern focused on weekend long-run preparation.

📊 Run Type Distribution

Key Insight: Bimodal distribution shows balanced training approach: majority of runs are 5-10 miles (easy/moderate efforts) with strategic ultra-distance runs up to 51 miles for building endurance.

⏰ Optimal Training Windows

Key Insight: 44% of runs occur late night (12AM-6AM) for schedule flexibility, but afternoon runs (12PM-6PM) yield fastest average pace (7.9 min/mi) - a 5% performance improvement.

📋 Methodology & Data Sources

Data Collection: Activity data extracted via Strava API, covering 36 runs from July 18 - December 6, 2025.

Tools Used: Python (pandas, requests), Plotly.js for interactive visualizations, GitHub Pages for hosting.

Analysis Focus: Identified key performance trends including volume progression, temporal patterns, and distance distribution to optimize training strategy.

Business Application: This analysis demonstrates data-driven decision making - the same analytical approach used by fitness apps like Strava, Garmin, or Nike Run Club to provide user insights.

Created by Jack Noble | Data Analytics Portfolio Project

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