VISUALIZING PERIODIZATION AND ITS EFFECTS ON AN IMBALANCE OF MUSCLE INPUT TO OUTPUT RATIO
INTRODUCTION
Periodization in sport is important. Seasons can be long and grueling, and an organization always needs to be aware of the fatigue status of its athletes. With the influx of wearable tech, the increasingly common way to monitor load status in athletes is to obtain an external load metric (traditional player load) and monitor it over the course of a season. This has worked well for visualizing periodization of athlete training. With an external workload quantified, teams now have a better idea of what a “normal” external workload is at an individual athlete level or a more general team level. This is a good start but is missing a key piece of information. When measuring external load, one is essentially measuring an estimation of the output of an athlete. The problem is that output does not always equal input. Running a seven minute mile well-trained, on a stress-free day, and a full nights rest feels very different to the body than running a seven-minute mile after chronically over training, on a day with a lot of stressors, and a poor night’s sleep the nights prior. An external load estimation would value these seven-minute miles the same.
External load essentially becomes a hopeful estimation of a way to visualize the periodized longitudinal training plans. Traditional player load (TPL) becomes a good tool to evaluate the result of careful planning of biomechanical output in exercises throughout a season, fails to evaluate the actual results of the programming. Internal load measurements can aid in completing the full visual of the programming and help teams analyze longitudinal load monitoring from a results-based perspective. Internal player load is a measurement that is based off EMG data collected from the left and right thighs, hamstrings, and glutes, which provides a scalable muscle output metric. Examining the ratio of input to output over a large amount of days that include similar activities (figures below are based off of a seven-day rolling average) should provide a good estimation of a fatigue ratio. The logic being that if an athlete or team of athletes’ external output remains equal, but their muscles have to work harder to achieve that same output, they are experiencing the symptom known as fatigue. From this point, a closer analysis can then be completed to determine the stressor. One stressor that can be a result of a long grueling season is chronic fatigue.
When considering the three figures below one can clearly see the periodization of external and internal workload and how they interact with one another. All these examples include only the regular seasons and immediate preseasons from each of the teams. As stated before, each point on the line represents the average internal and external loads for the entire team of that day and six days prior for a total of seven days. Using an average of the entire team as well as seven days data works to eliminate noise from the evaluations. One student-athlete having a bad night’s sleep resulting in a low external to internal load ratio (efficiency ratio) will have a minimal effect on the displayed result. However, coming back after an extended break in training where a majority of the team did little to no training will make a larger impact on the resulting display.
TEAM 1
Team 1 is an NCAA division 1 field hockey team. This particular dataset spans three and a half months. The longitudinal review of the team’s loading displays very little evidence of de-loading practices periodically throughout the season. The team’s internal and external load metrics remain at approximately a 1:1 ratio through the first three quarters of the season, at which point the internal load begins to steadily separate from the relatively more stable external load. This seems to be representative of fatigue. While it is difficult to draw absolute conclusions from de-identified data because of the lack of context, assuming that there was no major change in data collecting practices over the final weeks of Team 1’s season, the change of the internal to external load ratio suggests that the team was working harder internally to output similar external results. Because of the lack of periodical de-loading, the question must be asked, “Is the increase in inefficiency towards the end of the season due to chronic fatigue of the team?” Seasons can be long and grueling for athletes and chronic fatigue is always something that athletes and trainers should be cognizant of.
Figure 1 Division I Field Hockey Team
TEAM 2 AND 3
Figures 2 and 3 represent Teams 2 and 3 respectively. These figures display comparatively more deloading than the first team. Both of these datasets contain data spanning four months. Season scheduling presents different challenges when planning periodization periods. Both Teams 2 and 3, have a noticeable schedule break that resulted in a drop in the average load. Team 2 drops to approximately 100 while Team 3 drops to 0. Note that this is a seven-day average; 0 load represents a period where the team recorded no data for seven days in a row. The data keeping and recording practices were not verified as exactly the same between each team, therefore exact conclusions cannot be drawn from these analyses. However, it is easy to see the difference in differentiation of internal and external load when comparing before the break and after the break for each team. In both instances, the internal load is noticeably higher than the external load. This seemingly demonstrates the effect of an elongated de-loading period. When planning a training program, accounting for and evaluating the effects of schedule breaks such as bye weeks in football and December break in college basketball can be hard. These two visualizations clearly show some effect. Coming off a break lends itself to a period of less efficient (high internal to external load ratio) exercises. In each case, immediately following the break, the internal loads immediately were higher than the external load and then returned to an approximate 1:1 ratio. The difference is found after this return to “normal.” Team 2 remains seemingly “normal” for the rest of the season. Comparatively, Team 3 struggles to keep internal loads at a comparable level relative to the external load. While it does return 0 200 400 600 LOAD TIME (DAYS) TEAM 1 TRADITIONAL AND INTERNAL PLAYER LOAD IPL TPL briefly to an approximately 1:1 ratio during another brief de-loading period. Following this period, the internal load remains significantly higher than the external load to close the season.
Figure 2 Division I Basketball Team
Figure 3 Division 1 Basketball Team
CONCLUSION
It is important to note that this data, very similar to traditional load metrics, are unique to each team. Comparing workloads or workload ratios between teams is not as effective as comparing the more general trends over time of each team’s data. Each team’s “normal,” is unique to that team. This does not diminish the value of the data that is provided by measuring internal muscle load. Internal load is an important piece of the puzzle when visualizing and evaluating longitudinal periodization of a team’s training program. The Internal load metric aids in showing the results and removes the guess work when evaluating longitudinal programming. External load does a good enough job measuring how much an athlete does, which aids in evaluating expectations of output to actual output. However, internal load provides a good measurement of how hard that work actually was for the athletes. This knowledge can unlock the internal effects of programming as opposed to just measuring the external output of the training. Figures 1, 2, and 3 display one way in which the data can be easily used along with exploratory analysis in order to maximize an organization’s athletes throughout the entirety or the season.
WHITE PAPERS & CASE STUDIES
If you’re looking to dive deeper into the STRIVE Platform, review the literature below illustrating various use cases and research.
PURPOSE
Understanding game workloads allow coaches better insight into the demands of Women’s Basketball at the NCAA D1 Level. Coaches look to prescribe training loads in the gym and on the court through progressions that appropriately prepare athletes to perform during matches. Most team sport that require demands of intermittent exercise include the ability to perform through high-intensity bouts of high-speed running. The purpose of this case study was to capture, analyze and visually prepare data to better understand sprinting demands of Division 1 Collegiate Women’s Basketball.
BACKGROUND
Strive is a performance tracking wearable technology system seamlessly integrated into compression shorts for both female and male athletes utilize in all training settings. The garments are comfortable, can be embedded in whatever brand the team desires, and…
DESCRIPTION
Tracking metrics like speed, distance and accelerations can reveal patterns in practices and games that allow coaching staff to make adjustments. In addition to those metrics, one team wanted to understand the amount of effort players exerted throughout a week leading up to a game. The team employed STRIVE to track both the external metrics as well as the muscle EMG activity.
RESULTS
STRIVE worked with the team to analyze the results and found an interesting early correlation: The overall fatigue of the team, which compared how hard the muscles worked to produce the accelerations, directly correlated with the how well the team performed in the game. Essentially, the team performed below their potential when the players approached fatigue the week leading up to a game.
MONITORING INTENSITY OF GAME VS. PRACTICE
DESCRIPTION
How can coaches structure their weekly practices to better prepare for a game? One team wanted to replicate drills that produced similar game-time intensity that would allow them to structure their practices to optimize performance. With the help of STRIVE, they collected millions of data points across jumps, distance and accelerations to see what insights they could capture before games.
RESULTS
By analyzing the practices and non-conference games at the start of the season, the team identified how the opponent’s style of play impacted their player’s metrics. Using this information, the team made adjustments to their weekly practice schedule in an effort to get the same results in practice as on game day.
CHANGES IN PLAYER LOAD? SYSTEMS INTERROGATION
ABSTRACT
The purpose of this paper is to evaluate the use and utility of Sense3, a sensor system embedded in compression shorts that measure kinematic changes, muscle activation and physiology in elite athletes.
THE PROBLEM
Elite sports teams have been monitoring athlete loads through wearable technology for close to 10 years, yet most leagues and teams have yet to see a quantifiable reduction in athlete injury or a significant change in performance-based outputs. In many cases, the technologies provide a singular load metric “score” indicating a difference from game to game- or practice sessions. Practitioners are left to make “inferences” on why the score changed, without forming a direct rationale as to which biological system…
RETURN-TO-PLAY – INTERNAL VS EXTERNAL LOAD
DESCRIPTION
As the player started return-to-play protocol, the team asked STRIVE to re-assess his efficiency. The goal was to replicate the pre-injury practice session and identify any significant changes that could impact his recovery.
RESULTS
Before the injury, the player was found very efficient likely due to his conditioning to recovery balance. When STRIVE re-assessed the player post-injury, it showed that the internal load drastically increased even though the external load stayed consistent causing his efficiency to decrease nearly 40%.
USING STRIVE TO ASSESS RETURN TO PLAY FOR NFL PLAYERS
DESCRIPTION
There are an average of 176 hamstring injuries each season in the NFL. Once a player sustains an injury, they are more prone to re injuring the same muscle. Players with hamstring injuries miss an average of 13 days depending on the severity. While practicing return-to-play protocol for hamstring injuries, one NFL team used STRIVE. Taking into account body composition, position, left vs. right dominance and previous injuries, STRIVE discovered how certain exercises affect specific muscle groups differently on individual players.
RESULTS
With this finding, the team worked collaboratively with strength coaches, athletic…
STRIVE'S CAPTURES REPETITIONS OF 400 METERS, ALL APPROXIMATELY 90 SECONDS
INTRODUCTION
Currently, athletic organizations relate “Player Load” as a metric of output. IMU tech measures the output of an athlete’s session and then a load is provided for use in comparison with the athlete’s body of data to detect longitudinal trends and outliers. This number is then used as insight into how hard a session was for an athlete in relation to all other sessions, and sometimes even used as an injury risk indicator. In reality, the term Player Load is much broader than a simple movement score provided by an accelerometer. The amount of stress that an athlete’s body is under, influences the difficulty of a session. A movement score is not without value, and it plays an important role in the idea of a player’s load. However, there is additional context that is needed to fill out the picture that is true Player Load.
ANALYZING INTERNAL & EXTERNAL LOAD IN DIVISION I NCAA BASKETBALL TEAM
DESCRIPTION
Electromyography (EMG) is a diagnostic technique that evaluates and measures the electrical activity of skeletal muscles. The resultant amplitude of the muscles can help provide an approximation of internal load or how much work the muscles have done during an exercise period. With the noticeable uptick in the usage of wearable technology that measures external load, most strength and conditioning practitioners, athletic trainers, and other athletic organizational professionals are aware of the usefulness of an external load measurement. While the external load metric is useful in approximating the output of an athlete within an exercise session there is no accounting for the workload felt internally, by the muscles. One popular purpose of measuring external load is to reduce general fatigue and chronic stress. This is a reasonable…
VISUALIZING PERIODIZATION AND ITS EFFECTS ON AN IMBALANCE OF MUSCLE INPUT TO OUTPUT RATIO
INTRODUCTION
Periodization in sport is important. Seasons can be long and grueling, and an organization always needs to be aware of the fatigue status of its athletes. With the influx of wearable tech, the increasingly common way to monitor load status in athletes is to obtain an external load metric (traditional player load) and monitor it over the course of a season. This has worked well for visualizing periodization of athlete training. With an external workload quantified, teams now have a better idea of what a “normal” external workload is at an individual athlete level or a more general team level. This is a good start but is missing a key piece of information. When measuring external load…
EFFECTS OF FATIGUE ON INDIVIDUAL’S PERFORMANCE AND MUSCLE COMPENSATION
INTRODUCTION
Fatigue is the key driver in exposing weaknesses and deficiencies in athletes’ performance. Traditional methods have been inadequate in location weakness points and ensuing compensation when athletes enter fatigue stages. To mitigate further injuries, coaches have been working with players strengthening their muscles in symmetric ways, whether left to right, or posterior and anterior. To understand this better, we will look at the athlete who conducted six extensive drills as a part of a daily workout. In this example, we will discuss how fatigue affects this athlete and his muscle response during the compensation.
STRIVE BLOG
SEPTEMBER 1, 2021 | STRIVE, the only platform proven to optimize muscle performance for elite athletes and teams, today announced a strategic partnership with Fairly Group, the premier insurance broker and risk management company with hundreds of clients across all major professional sports leagues. Through the partnership, STRIVE is now available to players and teams as a benefit. Numerous players are already using STRIVE to help their return to play from injuries suffered during games.
AUGUST 10, 2021 | STRIVE CEO and Cofounder Nikola Mrvaljevic sat down with the Driving Force Podcast to discuss STRIVE’s place in human performance. From playing professional basketball in Montenegro, to leading his team at STRIVE, Mrvaljevic discusses it all.
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