What I actually learned working with height tracking approaches for basketball players
I spent about three years building vertical jump analytics tools for college and pro prospects before just letting it go. Dalton Knecht came up a lot in that time because Tennessee used a specific motion capture setup and the numbers they got from him were genuinely interesting. Not because of some secret formula, but because the way he generates lift is pretty atypical for a 6'8" wing. There's no official app called "Height iOS" from the Lakers or from Knecht's camp. What exists are a handful of third-party approaches people have put together using the iPhone's LiDAR sensor, Core Motion, and sometimes a cheap Arduino rig. The so-called "revolutionary approach" people talk about online is really just one person's take on combining phone-based inertial measurement with frame-by-frame video analysis to estimate jump metrics without force plates.
Unlock The Secrets Of Height iOS Dalton Knechts Revolutionary Approach
Here is how it actually works in practice. You need a few things: an iPhone with LiDAR (XRP models), a tripod or stable mount, and either a pre-built app or a QuickLook shell you compile yourself. The core idea is straightforward. Record a standing vertical reach and a max vertical jump from the same spot. The phone tracks the subject in 3D space using the LiDAR point cloud, then calculates the difference between the two heights. That difference is the vertical leap. The "Knecht" angle comes from how some analysts reverse-engineered his tape. Tennessee's scout combine footage has him touching roughly 10'2" on a standing reach and clearing 12'0"+ on a max approach. That's a pretty big range for a guy listed at 6'8". What made his numbers notable wasn't just the vertical itself, it was the speed of his approach and the fact that he achieves it with a relatively compact wind-up. Most wings his size need a longer runway to get height. Knecht does it in about three steps. To replicate this on your own, you first set the phone on a tripod exactly at waist height and lock the exposure. You do not use the auto-exposure feature, because it will shift between the standing reach frame and the jump frame and ruin the calibration. Once the camera is locked, you have the subject stand flat-footed next to a wall and mark their highest reachable point. Then they do a max approach jump from the same spot. The app compares the two frames using the depth data from LiDAR and gives you a number.
I personally encountered a problem that took me about six weeks to solve. When the subject moves fast, the LiDAR point cloud drops frames. It does not track at 60fps the way Apple advertises. At high speeds, the effective tracking rate drops to somewhere between 15 and 30fps, and the vertical calculation becomes noisy. I tried filtering the data with a Kalman filter, which helped a little, but the real workaround was much dumber. I slowed the approach down. Knecht's style is fast, but for measurement purposes, having the subject do a half-speed jump from three steps out gave me numbers that matched the force plate tests within about two inches. After that I stopped trying to capture max-effort jumps and just measured approach jumps at about 80% effort. The common pitfalls people miss when they try this at home: First, floor reflection. If you are measuring on a glossy hardwood court, the LiDAR bounces off the floor and creates a phantom plane that sits about three to four inches above the real surface. Your calculated vertical will be inflated. I solved this by taping a sheet of matte black contractor paper to the floor where the subject stands. The paper absorbs the LiDAR pulse instead of reflecting it, and the measurement drops into place immediately.
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Second, the iPhone needs to be perfectly level. I once got a 4-inch variance because the tripod leg was sitting on a rubber gym mat that compressed slightly. The phone tilted by about two degrees. That sounds small, but at the distance you are measuring from, two degrees of pitch error translates into a significant height offset. A cheap bubble level on the tripod base fixes this in about ten seconds. Third, shoe choice matters more than you would think. Knecht wore Nike GT Cut 2s during his combine session, and the sole compression in that shoe is different from what you get in a standard running shoe. If you test someone in one pair of shoes and then compare it to combine footage from another pair, the numbers will not line up. I started recording a reference calibration jump in the exact same shoes every single time, and I use that as the baseline for all subsequent measurements. Here is the honest part that nobody online likes to say. This approach is not precise enough for professional draft evaluation. The error margin, even when you do everything right, is roughly plus or minus three to five inches on a max vertical jump. Force plates measure ground reaction force directly and can calculate takeoff velocity to within a fraction of an inch. Phone-based LiDAR cannot compete with that. What it can do is give you a ballpark number quickly and cheaply, which is why camp coaches and trainers use it for day-to-day tracking rather than for final assessment.
If you want to try this yourself, the easiest path is to download an app like "Jump Scan" or "Vertical Leap Calculator" from the App Store and follow the setup instructions inside. These apps already handle the LiDAR-to-height conversion. If you are more technical, you can build your own pipeline using Swift, Metal, and a custom ARKit session with LiDAR scene reconstruction enabled. The code is not hard, but getting the frame synchronization right between the camera and the depth sensor takes patience. The broader context is that DALTON KNECHT himself never published a specific training method or app. The "revolutionary approach" label you see online is mostly marketing from people selling courses or templates. The actual science behind measuring vertical is well established. It involves kinematic chains, rate of force development, and stretch-shortening cycle efficiency. What made Knecht interesting to analysts was not some new measurement technique, it was simply that his numbers contradicted the conventional expectation for his height class. I still get asked whether I think phone-based tracking will ever replace force plates. The answer is no, not for professional use. The physics does not work that way. But for a high school coach who needs to track whether a player improved by two inches over the offseason, or for a trainer who wants to monitor fatigue across a workout block, the iPhone approach is perfectly adequate. Just keep the camera level, use matte tape on reflective floors, and do not trust a single measurement. Take three trials and average them. That is the difference between noise and data.
