RTP Interview #3 Jacob Rauch

RTP is delighted to be able to conduct this interview with Jacob Rauch, Performance Scientist at Peak Performance Project (P3) and lead author of the paper entitled Different Movement Strategies in the Countermovement Jump Amongst a Large Cohort of NBA Players. During this interview, Jacob will give us an in depth explanation of all the work behind this paper, including details and practical applications, as always seeking to bring scientific knowledge to the field that will enable us to improve our training processes.

RTP: Road to Performance always follow the work you carry out at P3, which we feel is of maximum quality and attempts to unite research with practice. What is the objective of this study?

Jacob: First off, thank you for having me I appreciate all of the work you guys do at RTP. I’d also like to mention that P3 has been performing great work long before I arrived, but I am honored to be a part of their current team, nonetheless.

As P3 has often discussed the utility of “braking” in basketball we decided to home in on the braking/downward phase of the CMJ for our fist publication. Thus, the main objective of our study was to characterize different downward phase movement strategies amongst a large cohort of NBA players. Afterwards, we wanted to take a deeper look and examine how different downward phase movement strategies impacted common temporal, kinetic and kinematic variables.

RTP: The sample consists of elite basketball players from the highest levels. What advantages and disadvantages does this sample have for the study?

Jacob: I think for the most part researchers and practitioners would agree that there is a limited amount of biomechanical data available on top level athletes. Under that context we think it is an advantage of our study to add kinetic and kinematic jumping data of a large cohort of NBA athletes into the literature.

Regarding disadvantages, the data was collected over several years on individual athletes from different teams. Though we made sure that athletes were healthy and fresh prior to data collection we were not able to control for any previous strength or jump training prior to assessing them.

RTP: The study is carried out through the CMJ (Countermovement Jump), a widely used test in the literature and, in practice, by many trainers and sports scientists. Why have you used this test? What information/variables does it offer you? What data does it provide at each phase of the CMJ?

Jacob: Before diving into all of the different applications of the CMJ I think it’s important to mention that the CMJ is not an all-inclusive one stop shop. Depending on the resources available practitioners should generally aim to select multiple valid and reliable assessments that measure independent qualities. For example, at P3, our traditional battery incorporates assessments from various planes under different loading conditions in an attempt to paint a complete picture of an athlete’s physical abilities.

That being said, the CMJ is an attractive test for us on many levels. For starters, it is a motor skill commonly performed in basketball and one in which players are willing to provide maximal intent on repeatedly. (The importance of getting maximal intent when testing elite athletes on ballistic movements cannot be overstated).

Regarding the information it offers us, on the simplest level the CMJ is a great way to examine an athlete’s ballistic capabilities. As ballistic performance is integral to a number of sporting actions, it is of no surprise that when exploring the underpinning qualities of this movement it can provide practitioners with a lot of valuable information.

For a detailed review on the different phases of the CMJ I recommend checking out the article Understanding the Key Phases of the Countermovement Jump Force-Time Curve by McMahon and colleagues published in the Strength and Conditioning Journal in 2018.

Nevertheless, when examining the temporal aspects of the CMJ on a force-platform you gain insight into how effectively an athlete unloads their mass, decelerates or “brakes” and then accelerates their mass prior to take off.

Athlete’s abilities to accelerate have long been admired and studied for their contributions in sport performance. However, it is only recently that an athlete’s ability to effectively slow themselves down or “put the brakes on” has gained attention.

Selecting key metrics from the braking and propulsion phase of the CMJ force time curve is a great way to measure both of these qualities.

RTP: When it comes to measuring the CMJ in athletes, what methodological criteria should be kept in mind so that measurements and post-evaluation are as valid and reliable as possible?

Jacob: The same methodological criteria that is to be applied in any laboratory atmosphere should be kept in mind. The first step is being aware of the specs of your device. More specifically, when measuring the CMJ on force-platforms you want to be aware of the sampling rate, as the sampling rate may influence any filters needed to smooth the data.

Next, you’re going to want to be aware of the devices error rates. Understanding the error rates will help you identify the difference between actual change and noise. Several companies have this information available through internal & external research, but it is always worthwhile to examine these values for yourself and get comfortable with the metrics.

Lastly, it’s important to have the right context for the information you’re trying to collect with your force platforms. If you’re using a CMJ as a daily readiness/monitoring tool, then taking the average of three trials performed with hands on hips after a standardized warm up may provide you with valid information. However, there are more detailed factors to consider if you’re looking to assess neuromuscular changes following a strength training intervention. Some factors include standardizing the time of day, nutritional status, and level of fatigue prior to an assessment (though this is not always feasible in real world scenarios).

RTP: You describe threemain clusters into which you divide the sportspeople according to kinetic and kinematic variables obtained in the jump.  What are these variables, and what are the main features that define each cluster?

Jacob:  Again, the main purpose of the paper was to characterize differences in downward phase movement strategies.  In order to do this, we had to come up with a method to select the “best” set of variables to characterize these patterns.

As downward phase movement strategies between athletes tends to differ via “range of motion traveled” and/or “rate of travel” we started with four sets of variables around each joint (Delta flexion, maximum flexion, average flexion velocity, maximum flexion velocity) in an attempt to account for potential differences in these factors.

Delta flexion (I.e total range of motion at the hip, knee and ankle during the downward phase) ended up creating the most “stable” clusters and as such was used to form the final clusters for the paper.

The clustering algorithm ultimately recommended three clusters, and upon further inspection of the data, the clusters could be clearly characterized as “stiff flexors”, “hyper flexors” and “hip flexors”.

Below I’ll summarize some of the defining features of each cluster, however it’s important to note up front that jump height was similar between clusters. Thus, despite having drastically different downward phase strategies athletes are able to achieve similar jump outcomes.

Stiff flexors- Travel through the least angular displacement at each joint. Stiff flexors also have the shortest total movement time and produce the greatest amount of relative concentric and eccentric force.

Hyper flexors- Travel through an above average angular displacement at each joint. Though hyper flexors demonstrate lower force outputs compared to stiff flexors they have the highest extension velocities.

Hip flexors- Travel through an average angular displacement at the ankle and knee joints but above average at the hip joint. Hip flexors take the longest time to complete the jump and express moderate amount of force outputs and extension velocities.

RTP: When analyzing the CMJ and all the data obtained, if you had to pick two or three main variables, which would be considered the most important? Which can offer the greatest percentage of information?  

Jacob: Again, context is key, and it depends on the information you’re trying to glean from the jump.

If you’re using the CMJ as a daily monitoring assessment, then metrics like contraction time and flight time can provide you with valuable information for assessing changes in neuromuscular fatigue.

If you’re implementing the CMJ to gain a better understanding of the underpinning neuromuscular capabilities of an athlete I would consider selecting a couple of metrics from each phase of the jump.

For the downward phase you may consider

  • Relative peak braking force
  • Peak braking velocity
  • Braking Impulse

For the propulsion phase you may consider

  • Relative peak concentric force
  • Peak concentric velocity
  • Concentric Impulse

Specific to the contents of our paper regardless of what cluster the athlete was in concentric relative force and knee extension velocity explained the largest amount of variance in CMJ performance.

Thus, if you’re looking for a couple of key force-plate & 3D motion capture metrics related to CMJ performance these are two great ones to track.

However, one of the main findings of our paper was that even though a majority of the variance in CMJ performance can be explained by the same set of “primary” predictor variables (Relative concentric force, knee extension velocity, knee extension acceleration, height). Each cluster had its own unique set of cluster specific “secondary” predictor variables that explained a portion of the variance of CMJ height.

This is intriguing on multiple levels. For starters this finding demonstrates that movement strategy does in fact impact which variables drive CMJ performance.

Furthermore, from a practitioner’s standpoint developing a better understanding of the “secondary” predictor variables for a given athlete may enable them to more effectively individualize training prescription when CMJ height is a primary target.

For example, the secondary predictor variables for hyper flexors which account for 22.73% of the R2 value include (more) delta knee flexion, (more) time between peak hip and knee flexion, (earlier) percentage of movement in which peak dorsi flexion occurs.

Thus, if one truly wants to individualize training prescription to improve jump height for athletes with this movement strategy, they may want to orient their training to further emphasize these qualities.

All in all, these findings highlight that some key metrics for CMJ performance do exist irrespective of movement strategy. However, when working in the context of elite sport where minor improvements can make a big impact, taking into account the cluster specific secondary predictor variables may also be worthwhile.

RTP: Does an ideal kinematic model really exist for the vertical jump or, conversely, do various ways exist to attain good performance in this action?

Jacob: As mentioned above, our data suggest that athletes can use a variety of jumping strategies and still reach impressive jump heights! (average jump height between clusters was 69 centimeters).

Furthermore, as each strategy has its own set of “secondary” predictor variables that explain 14.29-30.77% of the R2 value, it’s clear that there is not one ideal set of kinematic variables that drive CMJ performance.

What would be interesting to examine in the future is A.) The best way to develop further improvements in CMJ performance for each cluster and B.) Examine how improvements in CMJ performance are realized. As Cormie, McGuigan & Newton demonstrated in their staple 2010 paper that enhancements in CMJ performance following a strength training intervention are likely the result of a more efficient utilization of the eccentric phase (greater flexion velocity & depth). It would be interesting to see if each cluster responded differently to improvements in CMJ performance.

RTP: What practical day-to-day applications can this work obtain? Will it help to focus on and optimize strength training with our athletes?

Jacob: As we stated in the paper it is difficult to make precise training recommendations as our study was observational in nature and we did not investigate different training strategies.  However, as this platform does not have the same constraints as a scientific manuscript, I would be happy to expand on some current theories we’ve been tossing around at P3.

If practitioners are looking to optimize for jump height, they may want to orient their training towards developing relative concentric force production and knee extension velocity outputs, as these predictors’ variables were present in each cluster.

Furthermore, though any cluster would likely see improvements in CMJ performance if significant improvements are made in either relative concentric force or knee extension velocity, practitioners are left with a choice if they attempt to implement cluster specific training recommendations.

They can A.) Double down and continue to develop the quality which the athlete possesses sufficient amounts of or B.) Orient training to develop the quality which they may express lower amounts of.

 For example, stiff flexors have the highest force outputs, but lower extension velocities compared to the average. Thus, practitioners can decide to incorporate exercises that target further improvements in force production capabilities or lean into developing greater extension velocities.

Regarding hip flexors, though they express moderate amounts of force and extension velocities, practitioners may consider adding in additional posterior chain work to account for the need to control for extra flexion at the hip.

A final thought here is that practitioners are tasked with developing robust athlete’s capable of satisfying the demands of their sport, and not simply maximizing jump height. As sports such as basketball have time constraints to perform a given action, practitioners often focus on developing the ability to produce force rapidly. In that context, our data may lend insight into which style of jumpers may need more training emphasis on how to produce force quickly.

In the end of the day, it is clear that a lot more work is needed in this area! We hope our data can provide practitioners with more insight into how different movement strategies impact common CMJ variables. Practitioners can take this information, add it to their current assessment of the athlete and individualize their training accordingly.

RTP: Iffew trainers do not have access to the technology to carry out this kind of evaluation and, after learning about the types of clusters into which you group your sample, would it be possible to analyze the CMJ through video and to achieve the grouping of our athletes in one of these clusters?

Jacob: As always practitioners have to make the most out of the resources, they have available to them and simple “slo-mo” features on common smart phones may be a great place to start. However, practitioners should be weary of using phone-based apps that have not been validated to provide accurate biomechanical information.

RTP: Should we keep in mind the strategies used by our athletes during the downward phase when evaluating the potential deficits and their potential injury risk?  

Jacob: Regarding deficits, though it is likely that factors such as mobility restrictions impact CMJ strategy we have not investigated if this impacts risk of injury, so I can’t comment on that directly.

It’s also important to highlight again that in the context of sporting scenarios it is not always about achieving maximum jump height.

Thus, if qualities assessed in the jump can provide practitioners with the habitual strategy an athlete uses to generate power and further insight into some underlying neuromuscular performance qualities, all of this can be taken into account when creating a development strategy for an athlete.

RTP: What practical limitations are there to the application as regards extrapolating the results obtained in a developing real-life open context (training, competition, etc.) and on a day-to-day basis?

Jacob: Our project has several practical limitations. For starters, it would be beneficial to explore factors that may explain why athletes are more likely to appear in one cluster as opposed to another (I.e mobility restrictions, training history, fiber type).

Secondly, athletes were not given any time constraints when performing the jump and just because an athlete takes a long time to complete the jump habitually does not necessarily mean they don’t possess the ability to jump faster in game like scenarios.

Additionally, it would be beneficial to explore if CMJ strategy has any relationship to on-court outcomes or style of play. We are currently seeking to answer each of these questions.

However, as it currently stands our project supports the notion that athletes perform the CMJ very differently from one another and should be analyzed individually whenever possible. Additionally, movement strategy does impact the expression of common temporal, kinetic and kinematic outputs of the CMJ and this should be taken into account when profiling athletes.

RTP: Thank you very much for agreeing to grant us this interview on your paper, as well as for the work carried out and your contribution to the improvement of knowledge within our field.

Jacob: Thank you again for the opportunity!

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