Recently, I pitted my “eyeballing” analysis of a speed climber against a computer. My analysis identified 6 “lag points” – or areas where I thought the climber slowed down. An individual with experience in collecting quantitative performance data using video used an algorithm to find 8 points of reduced speed. All 6 that I found overlapped with the quantitative results but I missed 2. Each purple arrow indicates an area of reduced speed in the picture below.
In the coder’s words: “The coloring coding is based purely on the vertical movement of the center of gravity; it doesn’t take into account any lateral motion. The red dots are in the slowest 25th percentile of measurements, the green are the top 25th percentile, and the yellow is the 50 percent in between.” In other words, the computer compares relative speeds across the climb using the Center of Gravity (CoG) as its tracking point.
My failures were in conflating the third and fourth lags (from the bottom). This is one single move between two holds with two parts to the momentum loss. I also missed the first lag point (again – from the bottom), which was a straight up analytical error on my part.
Unfortunately, it would take a lot of work to make this program commercially available. But the upside is that the process I went through should tell you is that it’s possible for coaches to manually eyeball some egregious examples of reduced speed in a faster climber (roughly 10 seconds on the 15 meter route).
My initial concern that the program may over-emphasize areas that are “inherently slower” may have some validity. However, the fact that: (a) my initial analysis and sub-sequent re-analysis with the benefit of the program were in concurrence, and (b) a sub-sequent analysis with a world cup climber model identified potential for significant improvement at all identified lag points suggests that this type of analysis has merit. Put another way, it is likely possible to “speed” up the moves that lend themselves to faster movement but improvement at this particular climber’s lag points has strong potential. Additionally, faster climbers using alternative beta showed different lag points.