{"id":2250,"date":"2022-03-10T04:17:33","date_gmt":"2022-03-10T04:17:33","guid":{"rendered":"http:\/\/optimumsportsperformance.com\/blog\/?p=2250"},"modified":"2022-03-10T14:08:49","modified_gmt":"2022-03-10T14:08:49","slug":"weakley-et-al-2022-velocity-based-training-from-theory-to-application-r-workbook","status":"publish","type":"post","link":"https:\/\/optimumsportsperformance.com\/blog\/weakley-et-al-2022-velocity-based-training-from-theory-to-application-r-workbook\/","title":{"rendered":"Weakley et al. (2022). Velocity-Based Training: From Theory to Application – R Workbook"},"content":{"rendered":"

Velocity-based training (VBT) is a method employed by strength coaches to prescribe training intensity and volume based off of an individual athlete’s load-velocity profiles. I discussed VBT last year when I used {shiny} to build an interactive web application for visualizing and comparing athlete outputs<\/a><\/strong><\/span>.<\/p>\n

Specific to this topic, I recently read the following publication: Weakley, Mann, Banyard, McLaren, Scott, and Garcia-Ramos. (2022). Velocity-based training: From theory to application. Strength Cond J; 43(2): 31-49<\/span><\/a>.<\/p>\n

The paper aimed to provide some solutions for analyzing, visualizing, and presenting feedback around training prescription and performance improvement when using VBT. I enjoyed the paper and decided to write an R Markdown file to provide code that can accompany it and (hopefully) assist strength coaches in applying some of the concepts in practice. I’ll summarize some notes and thoughts below, but if you’d like to read the full R Markdown file that explains and codes all of the approaches in the paper, CLICK HERE>><\/strong> Weakley–2021—-Velocity-Based-Training—From-Theory-to-Application—Strength-Cond-J<\/a><\/strong><\/span>.<\/p>\n

If you’d like the CODE<\/a><\/span><\/strong> and DATA<\/a><\/span><\/strong> to run the analysis yourself, they are available on my GitHub page<\/a><\/span><\/strong>.<\/p>\n

Paper\/R Markdown Overview<\/strong><\/span><\/p>\n

Technical Note:<\/strong> I don\u2019t have the actual data from the paper. Therefore, I took a screen shot of Figure 3<\/strong> in the text and used an open source web application<\/a><\/strong><\/span> for extracting data from figures in research papers. This requires me to go through and manually click on the points of the plot itself. Consequently, I\u2019m not 100% perfect, so there may be subtle differences in my data set compared to what was used for the paper.<\/em><\/p>\n

The data used in the paper reflect 17-weeks of mean concentric velocity (MCV) in the 100-kg back squat for a competitive powerlifter, tested once a week. The two main figures, which, along with the analysis, I will recreate are Figure 3 <\/strong>and Figure 5. <\/strong><\/p>\n

Figure 3 <\/strong>is a time series visual of the athlete while Figure 5<\/strong> provides an analysis and visual for the athlete’s change across the weeks in the training<\/em> phase.<\/p>\n

Figure 3<\/strong><\/p>\n

The first 10-weeks represent the maintenance<\/em> phase for the athlete, which was followed by a 7-week training phase<\/em>. The maintenance phase sessions were used to build a linear regression model which was then used to visualize the athlete’s change over time along with corresponding confidence interval around each MCV observation. The model output looks like this:
\n\"\"<\/a><\/p>\n

The standard (typical) error was used to calculate confidence intervals around the observations. To calculate the standard error, the authors\u2019 recommend one of two approaches:<\/p>\n

1) If you have group-based test-retest data, they recommend taking the difference between the test-retest outcomes and calculating the standard error as follows:<\/p>\n