Analyzing PSI Data with TYPE-R

Hello, this is Hunter Allen from Peaks Coaching Group! Today I’d like to introduce you to PSI data analysis with the LEOMO TYPE-R. PSI stands for pedaling score intelligence which is one of the data points shown on LEOMO’s activity analysis web app.

In a collaborative effort between Dr. Michael Coco, myself, and the team at LEOMO, we have created PSI that help to explain where dead spots occur and at what intensity and the best combinations of gearing to reduce these dead spots.

Figure 1: Pedaling Stroke Intelligence with no data filtered.

LEOMO TYPE-R PSI Data Analysis

Figure 2: Pedaling Stroke Intelligence Plot from the same ride in figure 1, but now with DSS filtered, along with cadence below 81 rpm eliminated from the plot.


The difference between figure 1 and figure 2 is significant and important as one might make a training decision based on figure 1, but this could be an erroneous decision without looking at the data in Figure 2. It’s important when using the PSI scatter plot that you look at it in total.

Let’s take a look at some examples of rides and how the PSI scatter plot helps to define the athlete’s pedaling print, so you can familiarize yourself with these plots and also begin to recognize patterns in your own plot.

Figure 3: the rider is same as Figure 1 & 2 but completed a nearly 100-mile ride that included two steep climbs, each over 25 minutes long.


Due to a lower cadence while standing, we can identify the blue points in this chart as dead spots from when the rider was climbing. Based on this, we can immediately notice how concentrated they are in sections around 5 to 6 o’clock on the left leg and 10 to 11 o’clock on the right leg. This suggests that the rider’s left leg has a difficult time pulling up and out of the bottom of their pedal stroke while standing. Conversely, the right side shows dead spots when bringing the right foot over the top of the pedal stroke.

Further analysis of the PSI chart allows coaches to analyze pedaling data while the rider is seated vs standing. While climbing cadences will generally be lower than when a rider is seated, the PSI chart may show different clusters of dead spots at the same power output. This is one way to review the pedaling economy of a rider in various positions.

Observations from PSI Analysis:

This is a classic example of a rider standing out of the saddle, using too big of a gear for the steepness of the climb and really struggling to keep the pedals turning over.

There is also a marked difference between the left and right feet and their transition across the top of the pedal stroke. The movements that occur in the 5 to 6 o’clock range show the angle of the bike is in when climbing, so they are shifted somewhat to the left on the scatter plot.

These points represent the “transition” of forces from purely pushing straight down on the pedal stroke to beginning to pull up on the stroke, most likely moving from a foot horizontal position to a toe down position. As the rider brings the pedal stroke over the top of the circle, he also has to transition the foot from a toe down back to a more horizontal position in order to most effectively create downward force on the pedal, and this occurs in the 10 to 2 o’clock section for the left leg and the 10 to 11 o’clock section on the right leg. Again, this is shifted a bit to the right from top dead center, as the bicycle itself is on a steep angle.

The concentration of these lower cadence (blue points) highlights the transitions in a low cadence, climbing situation. In the future, this athlete should consider using easier gears on the climbs to eliminate these dead spots.

Benefits of PSI Analysis:

Reviewing data with Pedal Score Intelligence allows coaches to view a rider’s pedaling form at specific cadences and wattages. This type of data may be used to suggest an optimal cadence for a rider at their FTP wattage, or may be used to inform pedaling drills at lower wattages which focus on smoothing the pedal stroke and reducing dead spots. Additionally, the PSI chart allows coaches to compare left to right leg data, which can show imbalances that a rider may look to fix over the course of 3–4 months.

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