25 | Sport Scientist @rfeciclismo | Biomechanics @entrenociclismo | VBT at @SpleeftApp Dev
Segovia, España
Joined March 2014
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Notes from today's load-velocity profile review: be careful with the range of motion! ⬇️
@SpleeftApp
So, what explained the difference in the squat load-velocity profile?
Range of motion.
A 3-inch difference in ROM.
Shorter ROM with heavy loads = avoiding the sticking point = higher barbell velocity.
Apple Watch: bar or wrist?
In 14/15 deadlift reps, the bar-mounted Watch's velocity curve was closer to the LPT.
Exploratory, reference-assisted comparison. Setup, practical takeaway and limitations in the images.
spleeft.app/apple-watch-bar-…
Our last update stats are amazing 🤩
instagram.com/reel/DdRyiKkNX…
Your Apple Watch can now work as a VBT sensor.
Attach it to the bar, configure the athlete and set from your iPhone, and get real-time velocity feedback — without adding an encoder.
New in Spleeft: Smart Zero for more reliable rep detection, remote Watch control, raw IMU data, MPV and Time to Peak Velocity.
Would you use this in your coaching workflow?
spleeft.app/smart-zero-apple…
A practical example of why prescribing velocity targets can be more useful than prescribing absolute loads in the gym.
I’m introducing VBT with a cyclist I coach. We recently tested using @spleeftapp—he lives far away, so an in-person session with an encoder isn’t practical.
I don’t think we need to obsess over velocity or suddenly base every prescription on it.
But simple velocity metrics can help us fine-tune training prescriptions with very little extra friction—and add a genuinely professional layer to coaching decisions.
That’s one of the reasons I created @spleeftapp: so my athletes can train smarter in the gym without buying external devices—just using their phone or Apple Watch
Proud coach with this improvements and proud to develop useful tools to me and my athletes! @SpleeftApp
Iván de Lucas Rogero retweeted
A researcher (username: Kygoapp) on the subreddit QuantifiedSelf compiled the most comprehensive breakdown of wearable accuracy I've seen.
If you want to read the original post you can go here: reddit.com/r/QuantifiedSelf/…
Here's what the data actually says:
What an impressive performance progression thanks to #VelocityBasedTraining 🫠 @spleeftapp
New version of @SpleeftApp in progress! Improving the LVP tool for the incoming training camps!
- Find just test days, leaving workout data.
- Get context from group averages, helping with benchmarking.
- ROM filtering and 3-rep avg, to improve reliability.