Has the Acute:Chronic Workload Ratio Been Debunked?

This post delves into the research on the Acute Chronic Workload Ratio and if it can be used to predict injury.

The acute:chronic workload ratio (ACWR) once promised a simple way to manage injury risk. But in recent years, its popularity has waned, and with good reason.

This post explores the research behind ACWR, how to calculate it, and the growing body of criticism surrounding its use. Whether you currently use the ratio or are simply curious about why it became controversial, this summary will help you make an informed decision about its place in your training load monitoring system.

📘 Want to go deeper? The Fundamentals of Load Monitoring course—developed in collaboration with Sports Horizon—covers training load theory, applied monitoring, and data visualisation in Microsoft Power BI. Check it out here.

Where Did ACWR Come From?

The concept of comparing short-term (acute) load to longer-term (chronic) load isn't new. It traces back to Eric Banister’s training impulse model from the 1970s, which aimed to explain how fitness and fatigue interact following a training stimulus. Acute load reflects an immediate, short-term fatigue effect; chronic load represents the longer-term, positive accumulation of fitness.

Fast forward to 2015–2016, when the term "acute:chronic workload ratio" burst into the research spotlight. Early studies linking ACWR to injury risk gained traction, but these were correlational, not causal. The distinction matters, and the authors have since expressed regret at included the term "predicts" in their study title and manuscript (Hulin and Gabbett, 2018). As I began discussing on the blog many years ago, there were quickly questions and concerns raised on the topic.

How Is the ACWR Calculated?

The ACWR is a simple calculation comparing short-term (acute) training load to longer-term (chronic) training load. It’s designed to give a snapshot of how prepared an athlete is for recent demands, based on what they’ve been exposed to over time.

The ratio is calculated by dividing the acute load by the chronic load. A value of 1.0 suggests the athlete’s recent load matches their longer-term load. A ratio above 1.0 indicates a spike in training—acute load exceeds the chronic load—while a value below 1.0 reflects a drop in load.

Because the acute and chronic timeframes differ in length (e.g. 7 days vs. 28 days), we typically use rolling averages to standardise the comparison - daily or weekly averages are common. For example, if the daily average sprint distance over the past week is 335 m, and the 28-day average is 549 m, the resulting ACWR is 0.6.

Alternatively, summing weekly loads gives the same result: an acute week of 2,026 m compared to a four-week average of 3,486 m (illustrated below) also results in a ratio of 0.6.

Weekly Chronic load = (3367+4579+3972+2026) / 4

ACWR = 2026 / 3486 = 0.6

While ACWR is simply acute load divided by chronic load, that simplicity masks complication. Practitioners must make a series of decisions in the calculations:

• Timeframes: The most common are 7 days (acute) and 28 days (chronic), but these may not be ideal for every sport.

• Load Metrics: ACWR can be applied to any internal or external load measure. We frequently see different ratios across different metrics, so which do we pay more attention to?

• Coupled vs Uncoupled: Does the chronic load include the acute period or exclude it? Including it (coupled) can inflate the correlation (Lolli et al., 2019).

• Rolling vs Exponentially Weighted Moving Averages (EWMA): Rolling averages treat all days equally; EWMA accounts for decay, giving more weight to recent sessions, which seems to make more sense in terms of physiology (Menaspà, 2017). Even with consistent calculations, the resulting ratio can look deceptively similar across athletes despite vastly different training histories (see ~7:50 in the video below) - another reason why interpretation must go beyond the number.

Take a deeper dive into each of these calculation decisions in my video below from the Global Performance Insights YouTube channel 👇

What About Thresholds and the “Sweet Spot”?

One of the most well-known visualisations of ACWR is the "sweet spot" figure. This suggested injury risk is lowest at a ratio between 0.8 and 1.3, and dramatically increases beyond 2.0. But this figure has been heavily criticised.

The issue? It grouped continuous data into buckets, creating artificial thresholds. When outliers were removed and data was treated as continuous, the relationship between ACWR and injury disappeared (Impellizzeri et al., 2019; Wang et al., 2020).

While this figure was intended as a simple visual aid, it underscores the risks of oversimplifying scientific concepts. Franco Impellizzeri and colleagues were so concerned by this oversimplification that they submitted a request for retraction or formal correction (errata corrige) to the British Journal of Sports Medicine, stating:

"This figure was presented with the intent to be “illustrative only” and “just a guide”. However, even if only provided for such purposes, this figure has since been republished 5 times in the BJSM and to our knowledge at least 7 times in total, including two consensus one of which (the IOC consensus) presented this flawed relation as validated. This has increased the reader’s trust, providing credibility to potentially misleading information and favoring its spread over social media and practitioners. Last but not least, several researchers have used this quadratic relation as a reference model to fit their data instead of examining the best model. The resultant problems created among practitioners and the scientific community are quite evident."

In practice, applying such thresholds can lead to overconfidence in what is ultimately a noisy, inconsistent signal.

Key Limitations of the ACWR

As well as the limitations of the so-called "sweet spot" figure, there are a number of major concerns with ACWR that have led many practitioners and researchers to move on:

1. Lack of Predictive Power: ACWR does not consistently predict injury across sports or contexts. Injury risk is multifactorial and complex: trying to reduce it to a single ratio oversimplifies reality.

2. Flawed Theoretical Foundation: The original rationale, linking fatigue and fitness to acute and chronic loads, makes intuitive sense but lacks robust evidence when applied in real-world athlete monitoring.

3. Mathematical Problems: Ratios can distort data, particularly when chronic load is low. This inflates the ACWR and introduces instability in the metric.

4. Inconsistent Findings: Some studies suggest a high ACWR increases injury risk. Others report the opposite, or no relationship at all. That inconsistency is a major concern.

5. Limited Practical Value: How do we plan with it? What do we do with missing data, return from injury, or after a planned taper - all of which confound the ratio.

   
   

Some studies have even shown that randomised chronic loads perform just as well as ACWR (Impellizzeri et al., 2021), highlighting its limited added value as it relates to injury risk.

For a deeper discussion of the criticisms of the ACWR, take a further look at the references cited below 👇

Learn More

We explore all of this in greater detail - plus how to build your own visualisation tools in Power BI - in the Fundamentals of Load Monitoring course, developed in partnership with Sports Horizon. The course blends theory, practical implementation, and data analytics to help you make smarter, evidence-informed decisions.

The course is now launched and available for £399.

💡Tip: Global Performance Insights subscribers are often sent offers for the course. Join our community to make sure you don't miss out.

We can provide a 3-month payment instalment plan. In addition, we offer course bundles at a discounted rate. Email Ciaran at info@sporthorizon.co.uk for to discuss these options.

To find out more about the course and sign up, visit:

https://www.sporthorizon.co.uk/fundamentals-of-load-monitoring

FAQs

What is the acute:chronic workload ratio?

The Acute Chronic Workload Ratio (ACWR) is a ratio comparing short-term (acute) training load to longer-term (chronic) load. It’s often used to monitor athlete preparedness and manage injury risk.

Is the ACWR a reliable predictor of injury?

No. While early studies suggested a link, this was based on associations between workload and sports injury, rather than casual evidence (Remember: Correlation does not equal Causation). More recent analyses highlight its limitations and lack of consistent predictive power.

Should I use ACWR in my monitoring system?

That depends on your context. If used, it should be just one part of a broader, multi-factor load monitoring strategy - not an injury predictor, nor the sole decision-maker of training load management.

What are the key takeaways from your Fundamentals of Load Monitoring course?

You'll learn both theoretical and practical aspects of load monitoring, develop data visualisation skills, and gain confidence in communicating insights to coaching staff and athletes.