When a weightlifting coach begins building an athlete’s training plan, they typically have a direction that will hopefully lead to a desired conclusion (e.g., greater squatting focus in weeks 4-8 to enhance peak force production, three jerk sessions a week to build skill in the split jerk, taper strategy to peak athlete, etc.). Essentially, they see a problem and then they formulate a plan to solve the problem. A similar approach is necessary when implementing sport science principles within the weightlifting setting; without direction, data is being collected just for the sake of it, ultimately wasting time and resources. An approach I have found useful for guiding sport science decisions at P&G is the PPDAC cycle. Figure 1 illustrates the PPDAC cycle before I dive into a case example, however, simply searching “PPDAC” online will provide plenty of articles more deeply detailing this framework. I first heard about it from sport scientist Dr. Patrick Ward; he recently discussed using the PPDAC cycle on a podcast I would recommend giving a listen here.
Briefly, I will provide a case example of the PPDAC cycle being used at P&G when we first began implementing tests to monitor athlete performance.
Firstly, a problem must be defined that generates a question. Typically, these questions arise through discussions regarding an athlete’s performance that is either initiated by a coach or me.
“Is there a way to provide evidence that Jane Doe’s training plan has successfully peaked her physical state for the upcoming competition?”
As the team’s sport scientist, it is an essential responsibility to investigate whether an athlete’s training plan is altering their physical state at time points the coach has deemed ideal or optimal. The coach would not want to see the athlete’s neuromuscular performance during a taper to be identical to a high-volume phase of training!
Now that a question has been generated, a plan should be created in search of an answer. The coach is curious if the taper is working (i.e., decay of accumulated fatigue while retaining fitness qualities pertinent to competitive success) (1). Previous work has provided evidence that temporal measures, such as net impulse (force x change in time), of the countermovement jump (CMJ) may be useful for monitoring weightlifting performance (2). Weightlifting performance is characterized by the ability to produce large quantities of force as quickly as possible, thus a measure such as CMJ impulse may be useful for answering whether the taper has peaked Jane Doe’s physical state. Hence, the plan moving forward is to implement routine CMJ testing for Jane Doe to track impulse.
To minimize the invasiveness of testing, Jane Doe will perform a weekly CMJ test session throughout the macrocycle. I won’t go into the details of the data management pipeline I use to clean and store the data because most anyone reading this blog isn’t interested in talking about SQL or R. Nonetheless, I pull the data from our account with the force plates vendor, store it in a database, and then have code scripts that clean and wrangle the data for use. Thereafter, the data can be analyzed and visualized in a unique approach or through the web application I have built to quickly examine athlete data (maybe a topic for another time). It is extremely important to have a system in place for the workflow of how data is collected, where it goes, and how it can be accessed. There are many ways to go about this, so long as a hard and fast approach is used!
The fun part! In our case example, let us fast forward to the week prior to the competition. Jane Doe has trained for several months and has been tested throughout training (with a few time points missing – either absentness by the athlete or instrument malfunction). Figure 2 displays the longitudinal CMJ impulse data, our metric of interest. Concentric impulse during the taper was/is likely greater than throughout the rest of the macrocycle. The lower end of the error bars for concentric impulse during the taper are all greater than 54.5 Ns, which is the higher end for the final data point before the start of the taper, thereby indicating a greater concentric impulse at each test session during the taper compared to the early phases of training. Additionally, concentric impulse during the taper looks to be above the macrocycle average (except for the most recent data point, although the lower end of its error range barely includes the average so it would be erroneous to definitively say it is not above average) indicating an enhanced impulse.
Where to go from here? The analysis of the data indicates to Jane Doe’s coach that the taper has been successful, and she is likely physically peaked for the competition. This could be extremely beneficial insight for the coach as they craft Jane Doe’s subsequent training plans. Due to the missing data points, it is unfortunately difficult to draw strong conclusions how Jane Doe may be responding to training dosage throughout the training plan; however, in this case example, some tempered postulations would likely be beneficial for developing future questions. Nonetheless, it is vital that these findings be communicated with the coach, ideally in a way that is most digestible/useful for the coach. Additionally, due to the unique coach/athlete relationship in weightlifting, I highly recommend having an additional discussion with the coach where the athlete is included. Weightlifters are often very interested and involved in their training process; communicating how data collection is used to improve training plan development, and subsequently, their weightlifting capabilities, will likely lead to increased buy-in from the athlete.
And that is the PPDAC cycle and how it has been used at P&G! Being cyclical, it never ends – conclusions and data communication lead to more questions (i.e., problems) and the cycle starts over. When reading future blogs with real-world findings from our data, it is a safe assumption that a PPDAC framework was used to solve problems and arrive at conclusions!
- Bompa, TO and Buzzichelli, CA. Peaking for Competition. In: Periodization. Human Kinetics, 2019. pp. 207–225
- Suarez, DG. An Investigation into the Use of Biomechanical and Performance Data from Vertical Jump Testing to Monitor Competitive Weightlifters. East Tennessee State University, 2022.Available from: https://dc.etsu.edu/etd/4108/