Rowing Split Predictor
Model-based estimate tool for logging and planning: choose power-law or Paul's rule and project splits, power, and finish times.
Known Performance
Minutes
Seconds
Prediction Model
Both models are estimate-based planning tools for session journaling.
Athlete Profile
How to Use This Tool
Enter your known result, choose a prediction model, and compare estimated outcomes for standard rowing distances.
Enter Your Performance
Enter a known distance and time to predict your performance at other distances.
Complete Guide to Rowing Split Prediction
Learn how to predict rowing times at different distances using either a power-law model or Paul's split rule heuristic.
What Model Is Used Here?
This page uses a distance-time power law often referred to as a Riegel-style model. In rowing communities, a separate rule-of-thumb called "Paul's Law" is also commonly discussed. Both approaches are best treated as estimate tools for pacing and planning.
The Formula
- T₂ = Predicted time for target distance
- T₁ = Known time for reference distance
- D₂ = Target distance
- D₁ = Reference (known) distance
- Exponent = Fatigue factor (typically 1.04-1.08 for rowing)
The exponent accounts for the fact that pace changes as distance increases. A higher exponent means more pace drop-off, while a lower exponent means better pace retention over longer distances.
Understanding Athlete Profiles
In power-law mode, athlete profile selects the exponent used in predictions. Treat these as planning presets and choose the one that best matches your own logged results over time:
Sprinter (1.08)
- Built for shorter distance estimates
- Typically shows larger pace change at longer distances
- Useful when short tests are much stronger than long pieces
Balanced (1.06)
- General-purpose starting option
- Moderate pace change between short and long distances
- Use this when you do not have enough history yet
Endurance (1.04)
- Built for longer distance estimates
- Typically retains pace better as distance increases
- Useful when long pieces are strong relative to short tests
How to tune profile: Compare predictions against your own recent pieces (for example 1K, 2K, 5K, 6K). Keep the profile that produces the smallest repeat prediction gaps in your log.
Understanding Prediction Confidence
Confidence is based on how far the target distance is from your known distance. Use it as a planning signal, not as a guaranteed result:
High
Distance ratio 0.5x to 2x
Example: 2K to 1K or 4K
Medium
Distance ratio 0.25x to 4x
Example: 2K to 500m or 8K
Low
Outside 0.25x to 4x
Example: 2K to marathon
For tighter estimates, use a known performance close to your target distance and update results with recent logs.
Frequently Asked Questions
How accurate are these predictions?
Predictions are model-based estimates for planning and journaling. Confidence labels indicate how far you are extrapolating from your known result, and larger extrapolation generally means wider variation.
Should I use my PR or recent performance?
Use a recent representative piece from your log. Older peak results can overstate current estimates.
Why do my predictions seem off?
Common reasons: profile mismatch, large distance extrapolation, or using a non-representative source piece. Re-check with recent logged efforts and compare both model options.
Can I predict marathon times from a 2K?
Yes, but this is typically low-confidence due to the large distance jump. Use a longer known piece (for example 10K) when planning longer events.
How does this differ from the pace chart?
The Pace Chart shows finish times at a constant split. This predictor applies a selected distance model to generate estimate-based pacing and finish-time projections.
Scientific References
Riegel PS. "Athletic Records and Human Endurance."American Scientist. 1981
Secher NH. "Physiological and biomechanical aspects of rowing."Sports Med. 1983
Ingham SA, et al. "Determinants of 2,000 m rowing ergometer performance in elite rowers."Eur J Appl Physiol. 2002
Concept2. "Training Guide and Performance Charts."Concept2.com
Prediction Models
The Riegel model (T2 = T1 × (D2/D1)^1.06) is a general endurance prediction formula validated across running, cycling, and rowing. Paul's Rule adds a 5-second-per-500m adjustment for each doubling of distance, reflecting the specific fatigue patterns of ergometer rowing. Comparing both models helps you bracket a realistic target range.
Formula details: Methodology.
Related Tools
- Race Pace Calculator — build a complete race plan
- Pace Chart — reference table for all distances
- Performance Calculator — full session analysis
Standards & Guides
- 2000m Rowing Standards — predict your 2K from shorter tests
- 10000m Rowing Standards — long-distance benchmarks
- How to Improve Your 2K Time — use predictions to set training targets