By Ash Buckman and Matt McInnes Watson
Plyometric performance dictates key movement actions within team, combat, invasion, and court-based sports. A soccer player outjumping a defender to win a header, the quick feet of a boxer to dodge an incoming punch, a winger in rugby cutting to evade a fullback, and a basketball player leaping up for a game-defining dunk all require the presence of plyometric competency. Proficient landing/takeoff mechanics in sports can be the 1% difference between winning and losing.
The use of plyometric training can improve these KPIs to enhance athletic performance. Effective long-term plyometric training can produce one of two responses:
- An athlete who can produce force quicker.
- An athlete who can produce more force over time.
If we can produce an athlete who adapts in one of these two ways, they have the potential to better execute locomotive movements by jumping higher, running faster, and being more agile. With research showing variations in plyometrics leading to more significant increases in performance, the recent use of plyometric training has increased, and development in the area is also growing. Thus, to account for the surge in popularity, work must be done to improve monitoring, coaching, and training.
As mentioned in our previous article*, the reactive strength index (RSI) is a valuable testing measure to identify an athlete’s ability to produce vertical force through flight time or jump height (FT/JH) over the time spent on the floor (GCT). When using RSI as a testing measure, we can assess peak performance output from protocols such as a drop jump or a 10/5 repeated jump, yet that is the extent of its use.
Furthermore, we believe RSI as a performance output value is fundamentally flawed. RSI intends to express the plyometric work performed by an athlete and how they have utilized their reactive strength. The issue with RSI lies in the fact that it does not consider the approach into the landing, in which an athlete must react to produce a performance output.
The issue with RSI lies in the fact that it does not consider the approach into the landing, in which an athlete must react to produce a performance output, say @AshBuckman & @mcinneswatson. Share on XBy creating a reactive strength ratio (RSRatio) to monitor plyometric momentum in horizontal movements, we have been able to appreciate the work that goes into producing an RSI value. If we break down the term “reactive strength,” we can gain a better understanding of what the term means and what is required to utilize reactive strength in given movements.
- Reactive – Acting in response to a stimulus rather than absorbing/controlling it.
- Strength – The capacity of an object or substance to withstand great force or pressure.
- Reactive Strength – The ability to withstand eccentric loading from a stimulus and reproduce with maximal concentric force into subsequent takeoff.
Breaking down and comprehending the definition makes it easier to understand why RSI might not be the best measure of reactive strength since it does not consider the person’s ability to be reactive to an incoming landing.
Approaches to landings vary in plyometric movements based on variables such as flight times, fall height, and speed. Thus, the levels of reactive strength needed to reproduce force will differ even if RSI values are the same. This is not a criticism of RSI, which is highly useful as a monitoring and tracking tool for coaches and athletes. However, the value has a strong focus on the performance outcome rather than the process.
RSI is more of a direct measure of the stretch-shortening cycle, with RSI having been adapted to account for CMJ with the RSI-Mod equation. Thus, it is arguably closer to a measure of impulse than reactive strength. If we want to measure an athlete’s reactive strength capabilities, we must consider not only the outgoing performance (RSI) but also the incoming momentum and how that will affect the outcome.
*Note: This article is a follow-up to “Reactive Strength Ratio: A New Way of Evaluating and Monitoring Plyometrics” by Matt McInnes Watson and myself—we would highly recommend using that article as your starting point.
Introducing Reactive Strength Output
As practitioners, we aim to quantify and analyze as much as possible to ensure we provide our athletes with the best coaching possible. RSI has been fundamental in measuring athlete progress and neuromuscular readiness, but we believe there is more to be derived from data collected in plyometric movements. By introducing RSRatio, we can now analyze plyometric movements’ fluidity and provide coaching cues to manipulate variables to enhance performance. Still, we believe there is even more scope for analysis. In many other exercise types, it is possible to measure the value of work done, whether that is force or power. This is an element of plyometrics that we can add to RSI to assist in exercise monitoring.
RSOutput was a metric created to add to the RSI value to identify the level of work performed during plyometric movements. This new metric aimed to consider the varying approaches into plyometric landings as part of the overall value to determine the neuromuscular strain and effort experienced during movements.
You can see a practical example of this when comparing two drop jumps from different box heights, where the higher box may produce a lower RSI value. However, by considering the load in the increased approach to ground contact, the actual work performed may be greater than the lower box height that produced a greater output. By just considering the RSI scores, coaches and athletes may be led to believe that the athlete experienced increased work during the lower box height. Using RSOutput, we can quantify the body’s ability to tolerate incoming load and produce an output measure (RSI).
Video 1. A comparison of two drop jump performances with similar RSI values of a 15-centimeter and 45-centimeter box height.
Measuring Reactive Strength Output
When creating a method to calculate reactive strength, we felt it was important to understand the impact of the approach on the movement and account for incoming RSI—this allows us to consider the athlete’s ability to manage eccentric landing forces from the incoming FT and then reproduce it. We can account for the mean workload needed to complete the plyometric movement by taking the average value of both incoming and outgoing RSI. A lower incoming RSI will reduce the mean compared to outgoing RSI, whereas a higher incoming RSI will increase the mean.
When an exercise has an RSRatio of 1, incoming RSI will not affect mean RSI due to a balance of incoming and outgoing plyometric momentum. By using this mean value, large incoming RSI values that lead to smaller outgoing RSI values aren’t solely dependent upon the output value and will account for the increased loading of the approach. However, by only collecting the mean of incoming and outgoing RSI, values may be equal in two plyometric movements even though one may have a much larger incoming RSI leading to increased loading to overcome.
Therefore, we must divide the value by RSRatio to account for the more significant neurological stimulus attained from increased incoming FT or drop heights. By dividing the average by the RSRatio, we can account for this increased loading of plyometric movements where RSRatio <1 generates a reactive strength value that reflects the movement’s demands.

When using RSOutput to measure a drop jump movement (fall from a box), you must double the incoming flight time to determine the box height as the apex of a full jump (the 0.6-meter box fall time is 0.350, but it is 0.700 in the equation). An example of the calculation process for a drop jump can be seen below.
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RSOutput = ((Outgoing RSI + Incoming RSI) / 2) / (RSRatio)
Box Height: 0.3m = 0.247s Fall Time
Incoming RSI: Approach time – (2 * 0.247) / 0.185 GCT = 2.68
Outgoing RSI: Flight time – 0.550 / 0.185 GCT = 2.97
RSRatio: 2.97 / 2.678 = 1.11
RSOutput: ((2.68 + 2.97) / 2) / 1.11 = 2.54
In the above example, the fall into the ground contact is lower than the next flight time from the jump; thus, the incoming RSI will be lower than the outgoing RSI. This has reduced the overall mean RSI, as it would be perceived as easier than reproducing the same outgoing RSI from a higher box height. By dividing by the RSRatio score, we further reduce the RSOutput score due to the reduced eccentric loading experienced from the movement. This example has an increased concentric workload compared to eccentric, and the strain on the body would be less than if the athlete had fallen from a greater height.
While RSOutput isn’t an objective measure of work done like force or power, in the absence of force plates, it provides a useful metric to understand the athlete’s level of reactive strength. Share on XWhile RSOutput is not an objective measure of work done like force (Newtons) or power (Watts), with the absence of force plates, it provides a useful metric to understand the level of reactive strength utilized by the athlete.
Implications for Training and Coaching
It is crucial to understand the purpose of RSOutput in terms of when we should use it, what it means for training, and how we can optimize athlete performance from the values we obtain. By combining RSOutput with RSI rather than replacing it, we can create a broader picture of the athlete’s performance and genuinely understand how the body works. With all three values (RSOutput, RSRatio, and RSI), we can obtain all the information required to assess plyometric movements.
By combining RSOutput with RSI rather than replacing it, we can create a broader picture of the athlete’s performance and genuinely understand how the body works. Share on XWe need to collect RSI, as it provides us with the performance output measure; however, by collecting RSOutput, we can identify training zones and exercises to help improve plyometric performance. And, finally, by including RSRatio, we can measure plyometric momentum in locomotive movements and determine whether the plyometric movement has a concentric or eccentric focus. We can adjust extensive plyometric exercises by adapting elevation and incoming FT during approaches to force an RSR of less than 1 and increase the level of reactive strength utilization while similarly controlling the approach to induce an RSR of more than 1 to work on mechanics and concentric effort.
Reinventing Drop Jump Testing
Identifying optimal height for peak RSI values has been part of research and coaching debates for many years, not just as a method of tracking athlete progress but also profiling them based on their ability to handle increased fall heights. Coaches and athletes have used tests such as drop jump profiling and the 10/5 RSI test to determine an athlete’s optimal RSI score through controlled and self-regulated fall heights. The aim from training, then, is to see improvements in RSI scores from self-regulated or set drop heights or to see increases in fall heights with a similar RSI depending on the required adaptation from training.
However, when looking to train athletes to see improvements in drop jump performance, coaches often train at a supramaximal height to increase the eccentric loading. Identifying the increase in drop height is random and uncalculated. Yet, by calculating values such as RSRatio and RSOutput, it is possible to increase the control over exercise prescription and intensity for plyometric exercises, especially when vastly differing box heights could have similar RSI values.

When looking to profile athletes using varying drop jump heights, coaches can obtain RSOutput values to provide a bigger picture of an athlete’s reactive strength, which can aid in the programming of plyometric exercises. The graph above shows a drop jump profile with RSRatio and RSOutput values plotted against RSI across varying drop heights. This allows us to identify optimal drop height for RSI as traditionally used but also to see the height at which an athlete is maximally working their reactive strength through RSOutput. This highlights that, as drop height increases, the level of reactive strength increases even when RSI has decreased, until a point where RSOutput will plateau and then eventually drop.
So, what does this tell us?
By pinpointing fundamental values such as peak RSI, RSRatio = 1, and peak RSOutput, we can start to identify potential training zones for not only drop jumps but locomotive extensive plyometric exercises. In the adapted graph below, we have plotted these key values and corresponded them to one another. We calculated predicted peak RSI using trendlines on Excel, which would suggest that for this athlete, an optimal box height would be 0.34 meters and was at an RSRatio of 0.97.
To generally improve RSI, we would suggest training at an RSRatio of around 1 in drop jumps and locomotive plyometrics due to the close nature of peak RSI to RSRatio = 1. Peak RSOutput was achieved at 0.5 meters; thus, when looking to maximally stimulate the neuromuscular system, we would suggest this particular athlete train around this value, and in extensive plyometrics, an RSRatio of 0.8 should be the target.
Every athlete will be different, and although most would follow a similar trend, it is worth profiling your athlete and getting accustomed to their profile and training based on their reactive strength to get the best adaptations from the training.

To Conclude
RSOutput is a practical value that can help identify an athlete’s level of reactive strength and provide guidelines to help with training zones within plyometric exercises. In conjunction with RSI and RSOutput, breaking down an athlete’s plyometric capacity and increasing plyometric training specificity depending on the athlete, block of training, and sport is possible.
RSOutput is a practical value that can help identify an athlete’s level of reactive strength and provide guidelines to help with training zones within plyometric exercises, say @AshBuckman & @mcinneswatson. Share on XRSRatio and RSOutput are metrics that can be recorded through simple technologies and linked into many exercises to look to enhance sporting performance. We recommend that coaches explore their use with the athletes they train.
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“We calculated predicted peak RSI using trendlines on Excel, which would suggest that for this athlete, an optimal box height would be 0.34 meters and was at an RSRatio of 0.97.”
and
To generally improve RSI, we would suggest training at an RSRatio of around 1 in drop jumps and locomotive plyometrics due to the close nature of peak RSI to RSRatio = 1. Peak RSOutput was achieved at 0.5 meters; thus, when looking to maximally stimulate the neuromuscular system, we would suggest this particular athlete train around this value, and in extensive plyometrics, an RSRatio of 0.8 should be the target.
Question: When RSR is 0.8, rsoutput is the largest, so why not train around 0.8 but need train at 0.97 mentioned earlier?