Data Lab — Deep Training Analysis | Ride Cave

Your training, analyzed

17 interconnected analysis modules powered by your ride history. Fitness trends, power curves, training distribution, recovery signals, and more — all computed in your browser.

17 modules

Everything in one report

Every report opens with a summary strip — total rides, time, training load, energy output, and your current fitness, fatigue, and form scores. Below that, 17 modules break it all down.

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Fitness & Form

Track Fitness (CTL), Fatigue (ATL), and Form (TSB) plotted daily. See ramp rate, training monotony, and Bannister strain alongside separate trend lines for aerobic, glycolytic, and neuromuscular fitness.

Power Analysis

Your full power-duration curve with a Critical Power and W’ model fitted from your data. A phenotype radar classifies you across sprint, anaerobic, VO₂max, threshold, and endurance axes — Sprinter, Time Trialist, Climber, and more. Each key duration is graded against population benchmarks.

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Training Distribution

Weekly zone time in a stacked bar chart. Polarization index and sweet spot ratio per week. Volume ramp percentage vs. the previous week. Automatic training phase detection — base, build, peak, recovery, and taper — from volume and intensity patterns.

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Recovery & Efficiency

Warmup and max heart rate trends reveal accumulated fatigue. Aerobic efficiency factor plotted over time with a regression line shows whether your fitness is improving. Aerobic decoupling and cadence efficiency round out the picture.

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Activity Patterns

A TSS heatmap across your full date range. Current and longest ride streaks. Ride frequency and average TSS by day of week. Hourly distribution showing when you train and how time of day affects your power output. Weekly climbing volume.

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Intervals & Sprint

Best efforts across seven categories: sprint, anaerobic, VO₂max, threshold, sweet spot, tempo, and endurance. W’ balance depth per ride shows how deep into your anaerobic reserve each session went. Sprint type classification and peak power trends over time.

Your data

Three ways to generate a report

Data Lab works with your Ride Cave history, imported FIT files, or CSV exports from other platforms. The more data you bring, the richer the analysis.

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Ride Cave history

Select a date range and Ride Cave analyzes all your completed rides in that window. Every metric was captured natively — power streams, heart rate, zone times, strain decomposition, decoupling — so all 17 modules have full data.

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FIT files

Upload activity files from Garmin, Wahoo, or any device that records in the FIT format. Ride Cave reads the second-by-second stream, not just the session summary, so peak powers, zone times, normalized power, decoupling, and energy system strain are all computed. The more rides you upload, the better the analysis.

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CSV exports

Import ride summaries from Strava, TrainingPeaks, Intervals.icu, and other platforms. CSV files contain per-ride totals, not raw streams, so power zone times, peak powers, decoupling, and energy system metrics cannot be computed. Fitness trends, training volume, activity patterns, and HR-based modules will still populate. FIT files produce richer reports.

Keep your work

Save, revisit, and export

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Named snapshots

Save any report with a name and it appears on your Data Lab home screen. Loading a saved report is instant, since the full analysis is stored with it. There is no limit on saved reports.

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Export to PDF

Export any report to PDF directly from the report header. The PDF captures the full layout — all modules, charts, and data — in a clean print-ready format you can share with a coach or keep for your records.

Part of Cave Crew

Data Lab is included with Cave Crew: every date range from 14 days to all-time plus custom ranges, unlimited saved reports, and FIT and CSV imports. Longer ranges produce more accurate CP and W' models, more stable trend lines, and clearer training phase detection.

Join Cave Crew

Ready to dig in?

Open Data Lab from the main menu, pick a date range, and your report is ready in seconds.

Open Data Lab