New Update! Supercharge Your Load – Velocity Profiles

In this new Spleeft update, we have focused on two fundamental aspects: improvements in the user experience regarding execution velocity measurement and an expansion of analytics for both velocity trends and the load-velocity profile.

¡DESCARGA LA APLICACIÓN SPLEEFT AHORA PARA iOS, ANDROID Y APPLE WATCH!

1. Debug Mode: Toward a More Robust Algorithm

The democratization of Entrenamiento basado en la velocidad (VBT) using the internal sensors of the iPhone and Apple Watch presents complex technical challenges, especially regarding accelerometry. Although we always recommend following our best practices guide, we are aware that specific scenarios exist where repetition detection may vary compared to other reference devices.To solve this, we have introduced Debug Mode.If you detect that Spleeft is not correctly recording a repetition or a jump height in a particular situation, you can activate this mode. By doing so, you will send us the raw data so our development team can analyze the case and adjust the algorithm. The goal is to reduce the learning curve and make Spleeft infallible in any environment.Activate it before configuring each set on the iPhone. On the Apple Watch, tap the settings icon under the performance modes. Please note that activating this mode may compromise your device’s performance, as it consumes a significant amount of memory.

Improvement in Static-Start Exercises

Thanks to data collected during the beta phase, we have optimized the detection of the concentric phase start in exercises beginning from a static position (without previous countermovement), such as the peso muerto or paused press variants. Now, the algorithm identifies the “initial impulse” more accurately, ensuring that the mean velocity faithfully reflects the effort made from the very first millimeter of displacement.

2. Range of Motion (ROM) Filters and Temporal Grouping

The trends section is critical for evaluating long-term progression. However, a change in technique can generate noise in the data.

• ROM Filter in Trends: Just like in the load-velocity profile, you can now filter your evolution charts by range of motion. This is vital, as an increase in ROM can increase time under tension and alter the mean velocity without necessarily indicating a change in neuromuscular performance. Increasing the ROM with heavy loads can cause you to pass through the sticking point; with light loads, it may mean you spend more time applying force and accelerating.
• Temporal Grouping: We have added the option to group data by weeks or months. This allows you to clear daily variability and more clearly observe if the programming is having the desired effect.

3. Measurement Criteria: Average of the Top 3 Repetitions

Responding to requests from many coaches looking to maximize test reliability, we have included the option to show both charts and KPIs based on the average of the top 3 repetitions for each load. This adjustment is available in both the trends view and the load-velocity profile, allowing you to eliminate the bias of an isolated, atypical repetition.

4. Evolution of the Load-Velocity Profile

The load-velocity profile analysis has received a deep update to facilitate the professional’s workflow:

Test Day Detection

For those who use Spleeft in both daily training and specific evaluation sessions, identifying tests among the entire volume of data could be tedious. Now, the system allows you to filter and specifically list the “test days” detected within a date range, allowing you to jump directly to the peak performance data.

Contexts: Team and Age Groups

Comparison options by context are now available:

• Team Context: Allows you to compare an athlete’s profile with their team’s average profile under the same scale filters (absolute load, relative to 1RM, or body mass).
• Age Group Context: Compares data with the average of users in the same age range, ideal for athletes who use iCloud synchronization and are not integrated into a specific team.

5. Precision Adjustments and UI

• Automatic Profile Detection: We have made the criteria for generating automatic profiles stricter. A greater dispersion in the lifted loads (0.4 m/s) is now required to ensure that the estimated 1RM calculation and the profile slope are statistically reliable.
• Interactive Charts: Now, the load-velocity and load-power charts are clickable across their entire spectrum, not just at the selected loads. This allows you to see the velocity or power estimation for any given load, facilitating prescription.

Conclusión

Our commitment remains the same: to offer a professional precision tool with minimal friction. With these improvements, the coach receives a higher quality service with less administrative effort, allowing them to focus on what truly matters: data interpretation and decision-making.