Sustainability: Can the Athlete Maintain Intensity Throughout the Duration of the Game?

Author: Dayannara Munoz

If you have been using KINEXON’s IMU, LPS, or GPS system, you have been introduced to the ​“Metric Trifecta,” a framework that simplifies athlete monitoring by focusing on three fundamental dimensions of external load: 

• Volume: How much work was performed? 
• Intensity: How hard was the work performed? 
• Density: How often was high-intensity work performed and how much recovery occurred between efforts? 

This is a great way to paint a picture of session stress across a specified time frame, whether that’s a single session or a full season. One phrase you’ll frequently hear from coaching staff is, ​“Today’s practice needs to look like a game.” This raises questions: Does matching total distance mean practice replicated a game? What about Accumulated Acceleration Load (AAL)? Or Exertions? Or is there something more important? 

However, I would also encourage coaches to go beyond just replicating game workloads. We want to understand whether players possess the physical capacity to meet those demands when fatigue accumulates repeatedly. 

In this article, we will talk about the following: 

• Preparing athletes beyond load 
• Choosing performance metrics 
• Comparing practice versus games 
• Preparing for peak demands / Worst-case scenarios

One action step we encourage coaches to take is to match loads and compare practice and game metrics. For example, an athlete accumulates 4,000 meters (about 2.49 mi) during a basketball game; the assumption is that practices should produce similar outputs to make sure they are ready. If AAL during competition averages 650 AU, practices should approach that value as well. 

Although this is a great starting point, it’s only one piece of the puzzle in preparing athletes. We also need to look at performance over the duration of the session as well. For example, consider two players (A and B) who both finish a game with identical AAL values. 

• Player A maintains a consistent work rate throughout the duration of a game. 
• Player B begins with a high output but gradually declines as fatigue accumulates. 

When we looked only at total workload, they looked identical. Based on this, it would lead us to coach and prepare both athletes the same way. However, after digging deeper and considering performance-specific metrics, we saw that they did not have the same capacity. Further, we should also look at these outputs in the context of the game, not just in isolation. Did the decline in output occur because fatigue reduced the athlete’s capacity, or simply because the game context (e.g., a blowout or tactical changes) no longer demanded sustained high intensity? Was it a necessity of the game or a physical limitation? 

The goal of training isn’t simply exposing athletes to game-like loads; it is developing the capacity to produce high-quality movement throughout the entire contest repeatedly. The question coaches ask should not just be, ​“Did practice match game volume?” They also need to ask, ​“Can the athlete continue producing high-intensity efforts late in the game?” This shifts the focus from cumulative workload toward fatigue resistance. 

Most KINEXON users have a strong foundation in workload metrics if they follow the ​“Metric Trifecta.” To add a layer of performance monitoring, coaches will need to select metrics based on several factors, including sport demands and qualities they feel are important for performance. 

For example, both Volleyball and Basketball are intermittent sports characterized by repeated explosive efforts separated by brief recovery periods. Athletes accelerate, decelerate, cut, jump, sprint, shuffle, and change direction hundreds of times each game. If coaches want to see whether high-intensity actions remained consistent or if athletes’ outputs steadily decline, some metrics they may want to look out for are: 

• Reduced acceleration frequency 
• Lower peak running speeds 
• Smaller explosive movement profiles 
• Longer recovery between high-intensity efforts 
• Decreased jump output 
• Reduced change-of-direction intensity 
• Lower movement efficiency 
• Declining work rate over successive quarters or sets

Knowing the averages and standard deviation of a game is important and a particularly useful benchmark, especially for workload and volume metrics. However, for performance metrics, we must also look at the variation within a session because performance is not constant. Performance fluctuates, and the average tells us only the central value of a data set while smoothing out variance. It does not tell us how the athlete’s outputs vary over time. 

Recommended approaches for volume and workload metrics: 

• Basic statistics such as average, range, and standard deviation for the full session or phases/​parts of a session. 
  • You can do this for the full team, by position, or high-minute vs. low-minute athletes. 

  • For phases, you can look at how the load varies by phase/​drill in a practice, or by quarter in a women’s basketball game, by half in a men’s basketball game, or by set in a volleyball match. 

• Area Under the Curve (AUC) 

  • Instead of total workload, examine the shape of workload over time. Two athletes may have identical totals, but one performs steadily while the other spikes early, then falls off. Their AUC curves look different. 

• Relative analysis (% of game benchmark) 

  • Compare each practice and game to that athlete’s own competitive benchmark. 

• Rolling Window Analysis to Identify:
  • The most physically demanding one-minute periods 
  • Peak AAL accumulation 
  • Maximum acceleration density 
  • Highest movement intensity 

  • The frequency with which these periods occur 

• Area Under the Curve (AUC) 
  • Repeatability Index 
  • Count how many times an athlete exceeds a threshold.

  • Example: Athlete A and Athlete B both reached 95% of their individual max speed during practice. Looking at this metric alone, we might assume they experienced a similar high-speed stimulus. However, Athlete A reached ≥95% of max speed 10 times, while Athlete B reached it only once. Although their peak intensity was identical, Athlete A accumulated substantially more exposure to high-speed running. This demonstrates why % of max speed should be contextualized alongside metrics such as the number of high-speed efforts, distance accumulated at high speeds, and duration of exposure. Reaching the same peak intensity does not necessarily mean experiencing the same training load or stimulus. 

• Coefficient of Variation (CV) 
  • Measures how consistently an athlete performs throughout a game or practice by comparing the amount of variability to their average output. A lower CV indicates that performance remains relatively stable over time. In contrast, a higher CV suggests greater fluctuations that may reflect fatigue, an inability to sustain game-level intensity, or the natural flow of the game. This is why we have to consider context as well. A higher CV does not necessarily indicate fatigue or an inability to sustain game-level intensity. Variability in an athlete’s output may also reflect competition demands of competition, including changes in score, opponent matchups, substitutions, tactical adjustments, or shifts in positional responsibilities. 
  • Example: an athlete may intentionally perform less high-intensity work when protecting a lead or may experience increased demands following a tactical change. Therefore, interpret CV alongside game context to determine why an athlete’s physical output fluctuated, rather than assuming the variability reflects a decline in performance. 

Recommended approaches for performance metrics: 

• Look at how the athlete’s metric varies over time within that session. Spline charts are especially helpful for visualizing the variance and fluctuations or conducting a Time-Series Trend Analysis across a set interval. 
• Example of AAL/​min per quarter: Here, you can see Athlete B demonstrates a much greater decline in work rate.

When evaluating athlete performance, here are some useful questions to consider: 

• Volume and Workload: 
  • Did today’s practice reach game-level external load? 

  • How did today’s workload compare with the demands expected from the upcoming opponent? 

• Performance: 
  • Did athletes experience the peak demands seen during competition? 
  • Did high-intensity output remain stable throughout practice? 
  • Which players showed the largest performance decline over time? 
  • Which players consistently exceeded or failed to meet competition demands? 

Question: Will my athletes still be able to produce game-winning performances when fatigue is highest? 

• Coaches must monitor both workload and sustainability. 
• Volume, intensity, and density describe the work completed. 
• Sustainability describes whether the athlete’s performance declines as fatigue accumulates. 

By comparing practice demands to game demands, identifying peak periods of play, monitoring how performance changes under fatigue, and individualizing workloads, coaches can move beyond just asking whether practice looked like a game.