How to Monitor Practice Workload Without GPS Technology

Introduction

Monitoring your athletes' training load is essential for optimizing their readiness, minimizing injury risk, and supporting long-term athletic development. While many elite programs rely on advanced technologies like GPS tracking and heart rate monitoring systems, you may not have the budget or infrastructure to use these tools.

Fortunately, fairly effective workload monitoring does not require expensive equipment. By leveraging principles of internal load monitoring—specifically, the Session Rating of Perceived Exertion (sRPE) model developed by Dr. Carl Foster—you can implement a validated, practical system to measure training workload and support your athletes’ health and performance.

Understanding Internal Load

Internal load is an athlete’s physiological and psychological response to a training stimulus. The session-RPE (sRPE) system provides a scientifically validated method for measuring internal load based on how hard a coach or athlete thinks a practice was. Although this is subjective, I can still be useful.

In this model, coaches and/or athletes assign a score to each session using a 10-point scale (pictured to the right), where 1 represents minimal effort and 10 indicates maximal exertion.

The primary metrics used in this model are:

• Session Load = Rating (RPE) × Duration (in minutes)
  • This can be broken down into RPE of each practice activity (see graphic below) and summed for a more accurate representation of workload
• Daily Load = Sum of all session loads completed within a day
• Weekly Load = Sum of all daily loads over the course of a week (Gazzano, 2019).

(In TeamBuildr Practice, you can build session blocks and add intensities in the description of each activity [on the left].)

Monotony & Strain

Beyond total load, it is critical to evaluate your training pattern to identify periods of elevated risk. Two useful metrics are Monotony and Strain:

• Monotony = Standard deviation of daily training loads across a week (see graphic below)
• Strain = Weekly Load × Monotony (Gazzano, 2019).

High training volumes combined with low day-to-day variability (i.e., a Monotony above 2.0) have been strongly associated with increased risk of overtraining, illness, and non-functional fatigue (Foster, 1998). Checking in on these variables provides valuable insight into the balance of stress and recovery across a training week.

Here is how you calculate monotony:

1. Calculate the mean daily workload for the week (300+485+145+600+130+650+0) / 7 = 330
2. Find the standard deviation by using an SD calculator = 224
3. Divide the mean daily load / standard deviation --- 330 / 224 = 1.47

Managing Workload Spikes

One of the most critical challenges in workload management is identifying sudden spikes in training load, which can significantly elevate injury risk. The Acute: Chronic Workload Ratio (ACWR) is way to track this:

• Acute Load = Total load from the current week (can be set as a rolling number for any 7-day period)
• Chronic Load = Average weekly load over the previous 4 weeks
• ACWR = Acute Load ÷ Chronic Load
  

The goal is to stay between 0.8 - 1.4 ACWR.

• 0.0 - 0.7 = Risk of Undertraining
• 0.8 - 1.3 = Optimal Load
• 1.3 - 1. 4 = Warning Zone
• 1.5 + = Excessive Load

Research has shown that when ACWR increases by more than 15% in a single week, the risk of injury can rise by up to 50% (Gabbett, 2016). By tracking this ratio, you can identify and adjust potentially dangerous increases in training intensity or volume.

Another useful metric is the Freshness Index, calculated as the difference between Chronic and Acute Load. This can provide you insight into an athlete’s current level of readiness and recovery. A positive number indicates when fatigue is low and quality performance is expected. 

Subjective Wellness Measures

Load metrics should be supplemented with subjective wellness questionnaires to gain a more holistic view of athlete readiness. Athletes can provide daily or weekly feedback on factors such as sleep quality, pain & soreness, enjoyment, mood state, stress levels, motivation, and more.

These surveys offer a reliable, low-cost method to monitor non-sport stressors and early signs of fatigue or burnout (Saw et al., 2016). When used consistently, this information enhances the accuracy of workload interpretation and allows for more individualized training adjustments.

Practical Applications

This system is especially useful in environments where technology or staffing resources may be limited. By using easily obtainable data (RPE scores, session duration, and wellness feedback), coaches can:

Identify excessive training loads or monotony early
    •    Adjust practice plans to optimize recovery and performance
    •    Minimize risk of injury and overtraining
    •    Improve communication between sport coaches, strength coaches, and sports medicine staff

Conclusion

Monitoring athlete workload does not require advanced technology or large budgets. A well-structured, subjective monitoring system—grounded in the sRPE model—can offer significant value to your program. You can work with your performance staff to nail down workload goals for each day so you have something to shoot for.

When implemented with consistency and interpreted thoughtfully, it enables coaches and support staff to make data-informed decisions that enhance athlete performance, resilience, and long-term development.

References

Foster, Carl (1998). Monitoring training in players with reference to overtraining syndrome, Medicine & Science in Sports & Exercise

Foster, Carl. et al. (2001). A new approach to monitoring exercise training. Journal of Strength and Conditioning Research.

Gabbett, T. (2016). The training-injury prevention paradox: Should athletes be training smarter and harder? British Journal of Sports Medicine.

Gazzano, Fancois (2019). A Practical Guide to Workload Management & Injury Prevention in Sport.

Saw, A. E., Main, L. C., & Gastin, P. B. (2016). Monitoring the athlete training response: Subjective self-reported measures trump commonly used objective measures. British Journal of Sports Medicine.

*This method can also be used with hardware that tracks workload. This will obviously give you a more accurate depiction of where your athletes are.