Athlete Recovery Synthetic

By shrijeetverma13 · May 23, 2026

The Athlete Recovery & Biometric Performance Dataset is a comprehensive, longitudinal synthetic dataset that tracks the daily training habits, biometrics,…

A bar chart has been generated showing the average recovery scores for each sport type in your dataset. The visualization uses a color gradient (Viridis scale) to make it easy to compare performance across sports, with score values displayed directly on each bar.

Sleep duration has a clear positive impact on both recovery score and energy level. Athletes who sleep more tend to recover better and feel more energized. Two visualizations were generated: a grouped bar chart showing average recovery and energy scores across sleep duration bins, and a scatter plot showing individual data points colored by energy level.

Across all three metrics — mood, HRV, and recovery — there is a clear and consistent decline as stress levels increase from Low to High. Low-stress individuals average a mood score of 6.30, HRV of 82.87 ms, and recovery of 73.62, while high-stress individuals score significantly lower at 4.00 mood, 65.55 ms HRV, and just 25.09 recovery. Two visualizations were generated: a grouped bar chart showing average values by stress level, and box plots showing the full distribution of each metric across stress segments.

Recovery scores don't follow a steady upward or downward trend across the 28-day training cycle — instead, they fluctuate day-to-day and week-to-week. Three charts and supporting data tables were generated to visualize these patterns clearly.

Yes, higher caffeine intake does correlate with slightly worse sleep and recovery scores, though the relationships are modest rather than dramatic. The analysis found a Pearson correlation of -0.17 between caffeine and sleep duration, and -0.20 between caffeine and recovery score — both weak negative relationships. Two visualizations were generated: a grouped bar chart comparing average sleep hours and recovery scores across caffeine intake groups (None/Low, Moderate, High, Very High), and a scatter plot showing individual data points colored by sleep duration.

Two visualizations were generated to explore how training intensity relates to muscle soreness and recovery score. The first chart is a grouped bar chart showing average muscle soreness and average recovery score across five intensity ranges (Very Low to Very High). The second is a scatter plot of training intensity vs. recovery score, with each point color-coded by muscle soreness level, making it easy to spot patterns across the full dataset.

A grouped bar chart and detailed stats table compare Recovery Score, Energy Level, and Mood Score across 5 training types. Training types are ranked from highest to lowest average Recovery Score: Rest (64.67), Yoga (56.43), Cardio (48.52), Strength (46.22), and HIIT (43.59). Rest and Yoga clearly outperform the more intense training types in recovery, while HIIT and Strength score lowest. Energy Level and Mood Score follow a similar pattern, with Rest and Yoga leading both metrics as well.

Using the IQR method across four key physiological metrics, the analysis found that most values fall well within normal bounds. Resting Heart Rate had the most outliers (39 athletes, 0.47%), followed by HRV ms with 18 outliers (0.21%). Notably, Recovery Score and Muscle Soreness had zero outliers — their IQR ranges were wide enough to encompass all values. Since there were no Recovery Score outliers, the overlap analysis with HRV ms and Resting Heart Rate outliers was not applicable. Two box plots were generated to visualize the distributions and highlight outlier points, along with a grouped bar chart comparing outlier overlap rates.

The Pearson correlation matrix was successfully computed for all 10 numeric columns, and two visualizations were created: a full heatmap showing all pairwise correlations color-coded from red (negative) to blue (positive), and a horizontal bar chart highlighting the top 5 strongest positive and negative correlations with Recovery Score specifically.

A summary table was generated comparing athletes across three recovery tiers — High Recoverers (Top 25%), Average Recoverers (Middle 50%), and Low Recoverers (Bottom 25%) — based on their average Recovery Score. The table breaks down key metrics including Sleep Duration, Training Intensity, HRV, Resting Heart Rate, and Caffeine Intake for each tier, giving a clear picture of what separates top recoverers from the rest.