Analytics Methodology
This page documents the formulas, thresholds, and scientific literature behind every analytics metric in IronCoaching. Use it as a reference for understanding how your athletes’ data is calculated and interpreted.
Estimated One-Rep Max (e1RM)
Formula: Epley Formula
e1RM = weight × (1 + reps / 30)
reps = 1, the actual weight lifted is returned directly.
IronCoaching uses the Epley formula because it provides accurate estimates across a wide rep range (1–15) and is one of the most widely validated prediction equations in strength science.
Scientific References
- Epley, B. (1985). Poundage chart. Boyd Epley Workout. Lincoln, NE.
- Brzycki, M. (1993). Strength testing — predicting a one-rep max from reps-to-fatigue. Journal of Physical Education, Recreation & Dance, 64(1), 88–90.
- LeSuer, D. A., McCormick, J. H., Mayhew, J. L., Wasserstein, R. L., & Arnold, M. D. (1997). The accuracy of prediction equations for estimating 1-RM performance in the bench press, squat, and deadlift. Journal of Strength and Conditioning Research, 11(4), 211–213.
The Epley formula tends to slightly overestimate e1RM at higher rep ranges (>12). For competitive athletes peaking for a meet, actual 1RM testing is more reliable than any prediction equation.
Training Volume
Formula:
Volume = Σ (weight × reps) for all completed sets
Scientific References
- Schoenfeld, B. J., Ogborn, D., & Krieger, J. W. (2017). Dose-response relationship between weekly resistance training volume and increases in muscle mass: A systematic review and meta-analysis. Journal of Sports Sciences, 35(11), 1073–1080.
- Wernbom, M., Augustsson, J., & Thomeé, R. (2007). The influence of frequency, intensity, volume and mode of strength training on whole muscle cross-sectional area in humans. Sports Medicine, 37(3), 225–264.
Compliance / Completion Rate
Formula:
Completion Rate = (completed sets / prescribed sets) × 100
targetSets value from the program; “completed sets” is the number of set logs with completed = true.
A compliance rate above 85% generally indicates good program adherence. Below 70% may signal the program is too demanding or the athlete is disengaged.
Pearson Correlation (Exercise Comparison)
Formula:
r = Σ((xᵢ - x̄)(yᵢ - ȳ)) / √(Σ(xᵢ - x̄)² × Σ(yᵢ - ȳ)²)
Interpretation
| Coefficient (r) | Label |
|---|
| > 0.6 | Strong positive correlation |
| 0.3 to 0.6 | Moderate positive correlation |
| -0.3 to 0.3 | Weak / no correlation |
| -0.6 to -0.3 | Moderate negative correlation |
| < -0.6 | Strong negative correlation |
Scientific References
- Pearson, K. (1895). Note on regression and inheritance in the case of two parents. Proceedings of the Royal Society of London, 58, 240–242.
- Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates.
RPE Accuracy
What it measures: How closely an athlete’s self-reported RPE (Rate of Perceived Exertion) matches the coach’s prescribed RPE.
Method: For each exercise with a prescribed RPE, IronCoaching compares the prescribed value against the athlete’s actual reported RPE on each completed set. Only RPE values in the valid range of 6–10 are included.
The chart shows the average prescribed RPE vs. average actual RPE per exercise, making it easy to spot exercises where athletes consistently under- or over-report effort.
Scientific References
- Borg, G. (1982). Psychophysical bases of perceived exertion. Medicine and Science in Sports and Exercise, 14(5), 377–381.
- Helms, E. R., Cronin, J., Storey, A., & Zourdos, M. C. (2016). Application of the repetitions in reserve-based rating of perceived exertion scale for resistance training. Strength and Conditioning Journal, 38(4), 42–49.
- Zourdos, M. C., Klemp, A., Dolan, C., Quiles, J. M., Schau, K. A., Jo, E., … & Blanco, R. (2016). Novel resistance training–specific rating of perceived exertion scale measuring repetitions in reserve. Journal of Strength and Conditioning Research, 30(1), 267–275.
If an athlete consistently reports RPE 1–2 points below prescribed, the program may need to be more challenging — or the athlete may need RPE calibration.
Trend Detection
Method: The date range is split in half. The average e1RM for the first half is compared to the second half:
change = (secondHalfAvg - firstHalfAvg) / firstHalfAvg
| Change | Trend |
|---|
| > +2% | Improving |
| -2% to +2% | Stable |
| < -2% | Declining |
PR Prediction Accuracy
Formula:
Accuracy = (actual value / predicted value) × 100
Volume Zones (Frequency & Volume Chart)
Weekly set volume per muscle group is color-coded based on evidence-based training volume landmarks:
| Zone | Sets per Week | Color | Interpretation |
|---|
| Optimal | 10–20 | Green | Within the hypertrophy dose-response sweet spot |
| Low | < 10 | Yellow | May be insufficient for maximum growth |
| High | > 20 | Yellow | Approaching overreaching threshold |
| Excessive | > 25 | Red | Risk of exceeding recovery capacity |
Scientific References
- Schoenfeld, B. J., & Grgic, J. (2018). Evidence-based guidelines for resistance training volume to maximize muscle hypertrophy. Strength and Conditioning Journal, 40(4), 107–112.
- Krieger, J. W. (2010). Single vs. multiple sets of resistance exercise for muscle hypertrophy: A meta-analysis. Journal of Strength and Conditioning Research, 24(4), 1150–1159.
- Israetel, M., Hoffmann, J., Davis, M., Feather, J., & Serafini, P. (2019). Scientific Principles of Hypertrophy Training. Renaissance Periodization.
Volume zone thresholds are guidelines based on trained populations. Individual recovery capacity varies based on training age, sleep, nutrition, and stress. Use these zones as starting points, not hard rules.
Retention & Churn Metrics (Business Dashboard)
Churn Rate
Churn Rate = (ended clients / total non-pending clients) × 100
status = 'ended' against all clients who have been active at some point (excludes pending invites).
Average Tenure
Avg Tenure = mean(days since linked_at) for all active clients
linked_at timestamp of each active client relationship. Longer tenure indicates stronger client retention.
Sessions per Week
Sessions/Week = (total sessions last 30 days / 4.3) / active athlete count
Muscle Group Mapping
IronCoaching maps exercises to 16 muscle groups using the built-in exercise library (202+ exercises):
chest, back, shoulders, biceps, triceps, forearms, quadriceps, hamstrings, glutes, calves, core, traps, hip_flexors, adductors, abductors, full_body
Exercises not found in the library default to full_body. Using library exercises (rather than free-text names) ensures accurate muscle group tracking in the Frequency & Volume chart.
How It Works
- The athlete logs an exercise in IronLedger (e.g., “Barbell Back Squat”)
- IronCoaching matches the exercise name (case-insensitive) against the library
- The library entry’s
muscleGroups array is used (e.g., ['quadriceps', 'glutes', 'hamstrings', 'core'])
- Volume from that exercise is distributed across all mapped muscle groups
Add custom exercises to your library with correct muscle group tags to keep volume tracking accurate for non-standard exercises.
Training Streak
Method: Count consecutive calendar days with at least one logged session, working backwards from today. The streak resets if there’s a gap of more than 1 day from the most recent session.
Session timestamps are deduplicated to unique calendar days before counting.
Further Reading
For coaches who want to dive deeper into the science behind these metrics:
- Haff, G. G., & Triplett, N. T. (Eds.). (2016). Essentials of Strength Training and Conditioning (4th ed.). NSCA / Human Kinetics.
- Zatsiorsky, V. M., & Kraemer, W. J. (2006). Science and Practice of Strength Training (2nd ed.). Human Kinetics.
- Helms, E. R., Morgan, A., & Valdez, A. (2019). The Muscle and Strength Pyramid: Training (2nd ed.).
