🚣 Laat Lopen

Eduardo's rowing data — from the Amstel to the erg

📊 Descriptive Statistics Aggregation functions (mean, sum, min/max) compute KPIs from raw data. 💼 Executive dashboards · Operational KPIs · Anomaly detection
Business case: Just as these cards track rowing KPIs (total distance, avg pace, streak), the same approach builds executive dashboards that monitor revenue per customer (ARPU), conversion rates, and operational efficiency ratios — alerting when metrics breach thresholds.

Last Workout

05 Oct 2026

Days Since Last Workout

4 days

Current Streak

0 days

Total Workouts

173

Total Distance

1255.0 km

Avg Distance

7.25 km

Longest Row

24.0 km

Last 30 Days

35.02 km

Total Time

101.62 hrs

Avg Pace /500m

2:22.4

Avg Stroke Rate

23.9 spm

Avg Calories

422.0 cal

🔍 Latest Workout Detail

5000m in 24:49.6 on 2026-10-05 — pace and stroke rate over time.

📉 High-Resolution Time Series Per-stroke telemetry from the PM5 monitor reveals pacing strategy, fatigue patterns, and stroke rate consistency within a single workout.

Personal Bests

DistanceTimePace /500mDate
2000m 8:30.7 2:07.7 2025-09-17
5000m 20:45.3 2:04.5 2025-12-10
6000m 23:21.5 1:56.8 2026-02-17
10000m 42:04.5 2:06.2 2025-12-11
📈 Time Series Analysis Temporal aggregation groups data into weekly/monthly buckets using Pandas groupby to reveal volume trends and seasonality. 💼 Revenue trending · Demand forecasting · Seasonal planning
Business case: Monthly/weekly volume charts here mirror how businesses track monthly revenue trends and seasonal demand patterns. Spot dips before they become problems — forecast next quarter’s demand and optimize inventory levels accordingly.

📅 Training Heatmap

A GitHub-style calendar showing my daily rowing volume. Darker green = more meters.

🗓️ Matrix Transposition & Heatmap Visualization NumPy matrix transposition maps daily values into a weeks × weekdays grid. Custom colorscale encodes intensity. 💼 User engagement patterns · Website traffic analysis · Activity monitoring
Business case: This heatmap reveals when I train most. For a business, the same visualization shows daily/weekly active users (DAU/WAU), peak session times by channel, and feature adoption rates — answering “when and how often do customers engage?”

📈 Pace Trend Analysis

📉 Linear & Polynomial Regression OLS linear regression and degree-3 polynomial fit model trends over time. R² measures goodness of fit. Rolling average smooths noise. 💼 Sales forecasting · Price prediction · Performance trajectory modeling
Business case: The regression line predicting my pace trend is the same math behind sales forecasting and price prediction models. R² tells you how reliable the forecast is. Use it to project growth trajectories and set data-backed targets.

Regression analysis reveals whether my pace is improving over time. Improving 1.5s /500m per month · Linear R² = 0.095 · Poly R² = 0.263

🎯 Workout Clusters (K-Means)

🎯 K-Means Clustering (Unsupervised ML) K-Means algorithm with StandardScaler feature normalization discovers natural workout groupings from distance, pace, and duration. Elbow method evaluates optimal K. 💼 Customer segmentation · Market basket analysis · User behavior profiling
Business case: K-Means groups my workouts into Sprint, 5K, 10K, etc. The same algorithm segments customers by CLV and behavior, builds RFM scores (Recency, Frequency, Monetary), identifies churn-risk cohorts, and powers market basket analysis to find cross-sell opportunities.

Machine learning groups my workouts into 5 categories based on distance, pace, and duration.

Sprint

39 workouts

Avg 1530m · 2:04.6 /500m · 7 min

5K Steady-State

49 workouts

Avg 5122m · 2:30.1 /500m · 26 min

Mid-Distance (5-10K)

14 workouts

Avg 7461m · 2:10.7 /500m · 32 min

10K Steady-State

41 workouts

Avg 9924m · 2:38.9 /500m · 53 min

Endurance 10K+

30 workouts

Avg 14434m · 2:15.8 /500m · 66 min

📊 Training Balance

🥧 Distribution Analysis Proportional analysis of cluster assignments reveals how training effort is allocated across categories. 💼 Portfolio allocation · Resource distribution · Market share analysis
Business case: The pie chart shows how my training is distributed. For business, this same analysis drives budget allocation efficiency, compares channel ROI, measures market share by segment, and identifies where capacity is over- or under-utilized.

What percentage of my workouts fall into each category?