Recently, in a sport science chat group on Facebook someone asked for an example of how other practitioners are visualizing their Vald Force Frame data. For those that are unfamiliar, Vald is a company that originally pioneered the Nordbord, for eccentric hamstring strength testing. The Force Frame is their latest technological offering, designed to help practitioners test abduction and adduction of the hips for performance and return-to-play purposes.
The Data
I never used the Force Frame, personally, so the author provided a screen shot of what the data looks like. Using that example, I created a small simulation of data to try and create a visual that might be useful for practitioners. Briefly, the data is structured as two rows per athlete, a row representing the force output squeeze (adduction) and a row representing pull (abduction). My simulated data looks like this:
### Vald Force Frame Visual
library(tidyverse)
set.seed(678)
dat <- tibble( player = rep(c("Tom", "Alan", "Karl", "Sam"), each = 2), test = rep(c("Pull", "Squeeze"), times = 4) ) %>%
mutate(l_force = ifelse(test == "Pull", rnorm(n = nrow(.), mean = 305, sd = 30),
rnorm(n = nrow(.), mean = 360, sd = 30)),
r_force = ifelse(test == "Pull", rnorm(n = nrow(.), mean = 305, sd = 30),
rnorm(n = nrow(.), mean = 360, sd = 30)),
pct_imbalance = ifelse(l_force > r_force, ((l_force - r_force) / l_force) * -1,
(r_force - l_force) / r_force))
In this simplified data frame we see the two rows per athlete with left and right force outputs. I’ve also calculated the Bilateral Strength Asymmetry (BSA), indicated in the percent imbalance column. This measure, as well as several other measures of asymmetry, was reported by Bishop et al (2016) in their paper on Calculating Asymmetries. The equation is as follows:
BSA = (Stronger Limb – Weaker Limb) / Stronger Limb
Additionally, if the left leg was stronger than the right I multiplied the BSA by -1, so that the direction of asymmetry (the stronger limb) can be reflected in the plot.
The Plot
Prior to plotting, I set some theme elements that allow me to style the axis labels, axis text, plot title and subtitle, and the headers of the two facets of tests that we have (one for pull and one for squeeze). Additionally, in order to make the plot look cleaner, I get rid of the legend since it didn’t offer any new information that couldn’t be directly interpreted by looking at the visual.
Before creating the visual, I first add a few variables that will help me give more context to the numbers. The goal of this visual is to help practitioners look at the tests results for a larger group of athletes and quickly identify those athletes that may require specific consideration. Therefore, I create a text string that captures the left and right force outputs so that they can be plotted directly onto the plot and a corresponding “flag” variable that indicates when an athlete may be below some normalized benchmark of strength in the respective test. Finally, I created an asymmetry flag to indicate when an athlete has a BSA that exceeds 10% in either direction. This threshold can (and should) be whatever is meaningful and important with your athletes and in your sport.
For the plot itself, I decided that plotting the BSA values for both tests would be something that practitioners would find valuable and comprehending the direction of asymmetry in a plot is also very easy. Remember, the direction that the bar is pointed represents the stronger limb. To provide context for the asymmetry direction, I created a shaded normative range and whenever the bar is outside of this range, it changes to red. When it is inside the range it remains green. To provide the raw value force numbers, I add those to the plot as labels, in the middle of each plot region for each athlete. If the athlete is flagged as having a force output for either leg that is below the predetermined threshold the text will turn red.
theme_set(theme_classic() +
theme(strip.background = element_rect(fill = "black"),
strip.text = element_text(size = 13, face = "bold", color = "white"),
axis.text = element_text(size = 13, face = "bold"),
axis.title.x = element_text(size = 14, face = "bold"),
plot.title = element_text(size = 18),
plot.subtitle = element_text(size = 14),
legend.position = "none"))
dat %>%
mutate(left = paste("Left =", round(l_force, 1), sep = " "),
right = paste("Right =", round(r_force, 1), sep = " "),
l_r = paste(left, right, sep = "\n"),
asym_flag = ifelse(abs(pct_imbalance) > 0.1, "flag", "no flag"),
weakness_flag = ifelse((test == "Pull" & (l_force < 250 | r_force < 250)) |
(test == "Squeeze" & (l_force < 330 | r_force < 330)), "flag", "no flag")) %>%
ggplot(aes(x = pct_imbalance, y = player)) +
geom_rect(aes(xmin = -0.1, xmax = 0.1),
ymin = 0,
ymax = Inf,
fill = "light grey",
alpha = 0.3) +
geom_col(aes(fill = asym_flag),
alpha = 0.6,
color = "black") +
geom_vline(xintercept = 0,
size = 1.3) +
annotate(geom = "text",
x = -0.2,
y = 4.5,
size = 6,
label = "Left") +
annotate(geom = "text",
x = 0.2,
y = 4.5,
size = 6,
label = "Right") +
geom_label(aes(x = 0, y = player, label = l_r, color = weakness_flag)) +
scale_color_manual(values = c("flag" = "red", "no flag" = "black")) +
scale_fill_manual(values = c("flag" = "red", "no flag" = "palegreen")) +
labs(x = "% Imbalance",
y = NULL,
title = "Force Frame Team Testing",
subtitle = "Pull Weakness < 250 | Squeeze Weakness < 330") +
facet_grid(~test) +
scale_x_continuous(labels = scales::percent_format(accuracy = 0.1),
limits = c(-0.3, 0.3))
At a quick glance we can notice that 3 of the athletes exhibit a strength asymmetry for Pull and two exhibit a strength asymmetry for Squeeze. Additionally, one of the athletes, Karl, is also below the strength threshold for both Pull and Squeeze while Sam is exhibiting strength below the threshold for Squeeze only.
Wrapping Up
There are a lot of ways to visualize this sort of single day testing data. This is just one example and would be different if we were trying to visualize serial measurements, where we are tracking changes over time. Hopefully this short example provides some ideas. If you’d like to play around with the code and adapt it to your own athletes, you can access it on my GitHub page.