training radar
The training radar is a compact public-profile summary. It is not a readiness score, coaching prescription, or ranking system. Each axis converts one observable part of a user's logged training history into a 0-12 score so visitors can scan training shape at a glance.
1. Axis definitions
| Axis | Input | Scoring rule |
|---|---|---|
| Strength | Highest estimated one-rep max from any weighted set. | The estimate is capped at 700 lb, then scaled to 0-12. |
| Consistency | Logged training days compared with expected active split days since joining logit. | 100% consistency maps to 12/12. |
| Frequency | Average workouts per week across the last eight weeks. | Six workouts per week maps to 12/12. |
| Volume | Average weekly training volume across the last eight weeks. | 80,000 lb per week maps to 12/12. |
| Variety | Distinct exercises logged across public workout history. | Sixty distinct exercises maps to 12/12. |
| Experience | Account age and total workout count. | Half of the score comes from days on logit, half from workouts logged. |
2. Normalization
Every axis is rounded to the nearest whole number and clamped between 0 and 12. This keeps the chart stable and comparable while avoiding false precision. Values above an axis cap remain at 12 rather than stretching the scale for everyone else.
3. Recent-window metrics
Frequency and volume use the most recent eight Monday-first training weeks. That keeps those axes responsive to current behavior without erasing longer history for strength, variety, consistency, and experience.
4. Profile consistency
Consistency is a profile-level attendance ratio. logit counts how many distinct calendar days have at least one logged workout, then compares that against the number of active training days implied by the user's public split and account age. This avoids rewarding someone for simply having an old account, while also avoiding the impossible standard of training every calendar day.
If no public split is available, logit uses seven active days per week as the fallback. That makes the score conservative until the profile exposes enough schedule context.