By Jesse Green
Force plates can be useful in many facets of human performance, including baseline assessments, return to performance cases, load monitoring, and power, strength, and stability testing. Yet the more sophisticated such systems become, the greater the number and variety of outputs they provide, with some vendors now offering over 200 metrics. This can make it difficult for practitioners to know which ones to focus on, what the results mean, and how they can be applied. To help find the signal amidst the noise, I consulted with Jesse Green, Director of Performance and Sport Science for the Pittsburgh Penguins. In this article, we’ll highlight several specific metrics, explain what they reveal about athletes’ capabilities, limitations, and development opportunities, and suggest how monitoring force plate data inside an athlete management system (AMS) like Smartabase can be beneficial.
Concentric impulse – which we often report as jump height because of how intuitive for athletes and coaches to understand – is a useful force plate output measure. With jump height, we’re looking at how high an athlete can jump off the force plates, which is the result of the amount of concentric force they’re capable of producing and the time they take to produce it. It’s also simple to contextualize using some of the other strategy metrics we’ll mention later, as they provide deeper insights into how an athlete produces a given output . You can also add in asymmetry metrics to identify any left-right imbalances that may be tied to previous injuries, mobility or stability issues, or range of motion limitations. Further context can be added to this metric by expressing relative to bodyweight, which can also help when comparing between-athletes, within-athletes, or between positions. Additional output metrics include relative peak power and concentric peak force, which can be useful depending on the sport and position.
Eccentric peak velocity is the first strategy metric that can help put outcome metrics into context. In other words, why is an athlete jumping this high, and how are they producing force over time to get off the floor? The eccentric phase is going to have a large knock-on effect into how an athlete decelerates their body mass, slows their downward momentum, and then reverses that into upward momentum to jump (if that’s the test that you’re conducting). For someone who is extremely elastic and reactive – as a lot of basketball players, sprinters, and other explosive athletes are – the faster they descend and unload their body mass, the greater the response from their tendons. This rapid loading of the tendon facilitates the storage and release of energy via the stretch shorten cycle (SSC), resulting in a high rates of force development (RFD) and a rapid reversal in momentum. Eccentric duration and relative force at minimum displacement are also useful strategy metrics that can help to explain outputs
A global strategy metric that can also be used as a performance benchmark in jumping assessments is the flight time to contraction time ratio (FT:CT). This measure takes into consideration the ratio of the duration that an athlete is in the air (flight time) to how long they’re on the ground, from the moment they begin the downward phase to when their feet leave the force plates (contraction/contact time). We also use FT:CT as a performance indicator to assess the elasticity of an individual, with more elastic individuals displaying higher FT:CT values.
Combining Output and Strategy Metrics to Inform Programming
Once you have established a stable baseline of 1-2 output metrics plus 2-4 strategy metrics to help explain or describe them, a common method is to assign your athletes to sub-categories, with the goal of creating programs that target the biodynamic needs of the individual athlete (i.e force deficient or velocity deficient). An additional method to categorize athletes is by the global force-time trace itself, and how they apply force across the entire movement. For example there are three predominant global strategies that arise when analyzing the countermovement jump (CMJ): unimodal, bimodal-primary, and bimodal-secondary. Among unimodal jumpers, there’s a single force peak that occurs in close proximity (~50ms) to amortization (minimum displacement), this strategy is the most biomechanically efficient way to perform a CMJ, and is typically associated with high RFD and large FT:CT values. In bimodal-primary jumpers, there are two force peaks, the first occurs right around the minimum displacement (similar to unimodal), and the second lesser peak occurs later during the concentric phase. Bimodal-primary jumpers often utilize a greater countermovement to allow more time to produce impulse. Bimodal-secondary jumpers have two force peaks, similar to that of a bimodal-primary jumper, however the second later peak is higher than the first peak. Bimodal-secondary strategies commonly have poor FT:CT ratios resulting from longer contact times. Regardless of the observed global strategy, it must be mentioned that these approaches can change in response to fatigue, adaptation, and injury, so ensuring an adequate sample size is collected (>5 trials) is imperative.
Once the global strategy has been established, you’re better informed to target and develop specific strengths and/or deficiencies of the athlete. Accompanied by a complete profile and a comprehensive review of the sport demands, a simple but effective goal for a basketball athlete for example is to build upon the existing efficiencies of unimodal jumpers, while transitioning the bimodal-secondary strategies to bimodal-primary and moving bimodal-primary jumpers to the unimodal strategy. Zooming out, analysis of CMJ data collected on a force plate can tell you a lot about how an athlete moves with respect to applying vertical force in a bilateral task which can set the stage for making better informed programming decisions.
In addition to providing output and strategy metrics, force plates are also a great tool for assessing imbalances and asymmetries. Many of the metrics previously mentioned can be re-examined through this lens to see if there are any meaningful discrepancies between an athlete’s left and right sides. One of the more sensitive asymmetry metrics to look at is eccentric deceleration impulse. In a CMJ, you start with your hands on your hips, unload your bodyweight, and as you descend, there’s a point in which you start to apply force to put on the brakes and reverse your momentum. The eccentric deceleration phase begins at the moment you reach your eccentric peak velocity, and ends when you reach the bottom of the countermovement. Given it is an impulse measure, we’re looking at the product of mean force and time through this phase, as a means to quantify the athlete’s ability to reverse their downward momentum. During this phase, we want to see what they can produce on each side – are they producing higher impulse on one side versus the other? How might this be a problem if the difference is too great? What is the explanation for this difference? Is this normal for this athlete?
It can also be beneficial to look at the concentric impulse asymmetry, which includes the moment from when the athlete is at the bottom of the countermovement to when they leave the plates. Unlike in the eccentric deceleration phase when the athlete is applying force to slow their body mass, concentric impulse captures the impulse produced to accelerate their body mass . Concentric impulse asymmetry is typically more stable than eccentric deceleration impulse asymmetry, which can be a result of many factors such as strength level, injury history, and global strategy.
Whether it’s a chronic, or acute issue like an ankle sprain for example, we typically see changes between a player’s injured and non-injured side during the return to performance (RTP) process. Asymmetries are key measures to monitor as the athlete returns to individual work, practices, and games. Asymmetry measures are particularly important in the earlier RTP phases when there may be more pronounced compensatory patterns at play.
This approach also applies to healthy athletes, that is, an abrupt change in symmetry may indicate compensation, or something deeper worth investigating. When there’s an acute issue present, such as tendon pain or a minor contact injury, quite often we see an altered global strategy with unchanged output metrics. It is also common for some form of mobility or joint range of motion restriction to influence movement asymmetries via force plate testing. For example, an athlete who has had a history of ankle sprains on one side may have reduced ankle dorsiflexion. This unilateral loss of mobility could lead to an altered movement strategy, leading to an asymmetry that is relatively stable. Further, recent work in this space has alluded to the fact that asymmetries are normal, and that narrow absolute thresholds may not provide as much value as monitoring the magnitude and direction of asymmetries over time.
Using an AMS to Make Force Plate Metrics Actionable
With the help of Smartabase, you can begin looking for deeper answers when you observe a statistically meaningful change in an athlete’s asymmetry over time, whether they’re injured or healthy. For example, performing dynamometer muscle testing could reveal that one contributing factor for a player producing more deceleration impulse with their right leg is that their internal rotators around their left hip are comparatively weak, resulting in a shift to the right leg when force is applied. Further, you might look at an FMS score for example or a result from another movement assessment conducted and see that they also have a stability limitation on the left side. An AMS makes such investigations faster and more comprehensive than when player data is stored in separate silos and allows you to go down to a more granular level with your analysis.
Another primary benefit of using a platform like Smartabase to manage force plate metrics is that it allows for automating the input and customizing how you choose to calculate the reliability and account for the inherent variability of these tests. This is extremely important as if you don’t consider the reliability of your data, it becomes very difficult to know if the changes that you see in an athlete’s scores over time are meaningful, or fall within the normal variation expected for that specific test. Integration with an AMS is crucial for getting consistent, reliable data in the first place, and then automating the analysis it to facilitate your decision-making. The ability to customize calculations for each test helps ensure that force plate data is both accurate and applicable to the performance program.
Utilizing an AMS such as Smartabase enables a strength and conditioning coach, sports scientist, or any other performance professional to evaluate all of these force plate metrics and more, together with data sets from other devices and systems. For example, you could bring in external load data from a GPS or LPS player tracking system along with internal load data to investigate dose-response relationships with the information gathered from force plate testing. Often this is more about asking better questions than it is about finding specific answers, although you could investigate a certain hypothesis using the reporting features in Smartabase. You might want to know if subjective reported fatigue correlates to a statistically significant decrease in FT:CT, or if an increase in external load is associated with a drop in relative peak power. If the initial results suggest that there is a connection between two variables, then you can start bringing in more data from additional sources or run different reports to investigate further. Should it be necessary, you could then consider a change in programming or emphasizing different recovery strategies with your athletes.
Working on Athletes’ Strengths and Weaknesses
Smartabase can also prove useful when categorizing your athletes based on their physical characteristics. Earlier, we saw how this can be achieved using counter-movement jump metrics alone, but these can be consolidated with results from other force plate-based assessments, such as isometric mid-thigh pulls, belt squats, and drop jumps. Bringing the results of these together with counter-movement jump metrics can produce a more comprehensive profile for each player. A simple method for interpreting this information is via plotting each test on a quadrant. Perhaps one athlete is strong but not very explosive, a second vice versa, and a third both.
From there, you can begin to decide how best to further develop their strengths or raise the ceiling of their weaknesses. Often this isn’t an either-or, but rather a case of doing both at different times. Perhaps early in the offseason you assign a program that helps a player work on a weakness such as power, which you use force plates to test and re-test. Then later in the off-season, you have them switch their focus to sharpening one of their strengths. When the competitive season kicks in, it becomes a matter of maintaining the new levels of both characteristics, and occasionally improving depending on the global demands on the athlete.
To go one step further in your reporting and analysis of force plate information, you could also overlay additional data sources that add further context to the athlete readiness equation. For example, the Penguins are starting to utilize Smartabase to evaluate some of the metrics already mentioned alongside their daily difficulty index (DDI). This incorporates practice and travel schedules, game density, and other data points that might have an impact on variations in athlete readiness. It will be interesting to see how playing four or five games in seven days affects one athlete but might not move the needle for another, and to see if there are any differences between high impact players and those who are lower down in the rotation. This kind of additional context helps make output, strategy, and asymmetry information more relevant and actionable within teamwide and individualized performance programming.