In episode 383 of The Physical Performance Show, host Brad Beer sits down with Professor Franco Impellizzeri, one of the most influential and outspoken voices in athlete load management research, for a wide-ranging conversation on why “load management” is an old concept dressed up in new data, why the acute to chronic workload ratio (ACWR) never held up to scrutiny, and why context — not more metrics — is what separates good decision-making from noise.
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Professor Franco Impellizzeri’s work can be explored here:
https://profiles.uts.edu.au/Franco.Impellizzeri
https://scholar.google.com/citations?user=z35F1WAAAAAJ&hl=en
Listen in as we delve into the following:
What “load management” actually means, and why it’s a relabelling of concepts coaches have wrestled with since the 1930s–40s
The double-edged sword of the data revolution: from heart rate monitors to today’s GPS, wearables, and AI-driven analytics
Commercial and career incentives behind pushing metrics — both from tech companies and researchers
Confirmation bias and why interpreting data without a theoretical framework is “like reading tarot”
The case against the acute to chronic workload ratio (ACWR): its lack of scientific support despite IOC endorsement
A landmark runners’ study showing the opposite relationship between ACWR and injury risk — and what that reveals about the field
Why meta-analyses and systematic reviews in load research can be manipulated to “find whatever you like”
The difference between using a metric personally versus promoting it as scientifically validated
Why the most skilled clinicians tend to hold their conclusions with the least certainty
A real-world case study: a soccer training drill “failure” that was actually a video-monitoring blind spot, not a bad drill
The expanding role of data scientists in sport, and why interpretation — not collection — is the real skill gap
Whether AI and machine learning can (or should) replace the sports scientist’s judgement
Individualised and personalised training approaches — and why even these are still built on group-level generalisations
Distinguishing signal from noise, and why one “failed” intervention doesn’t mean an athlete won’t respond next time
Practical advice for coaches and practitioners: accept uncertainty, treat training like a “trial of therapy,” and reassess
If he could track only one metric: heart rate (or perceived effort) over power
The push for more honest scientific debate — including a proposed on-air “intellectual sparring” series with dissenting researchers
Quotes:
“Understanding the quantity of training for athletes has been a hot topic since training methodology started… managing load is not new.” — Professor Franco Impellizzeri
“When you don’t have a theoretical framework and an a priori idea strong and supported, it’s like reading tarot, to be honest.” — Professor Franco Impellizzeri
“There’s zero evidence that this metric can either help or can actually reflect some sort of mechanism.” — Professor Franco Impellizzeri, on the acute to chronic workload ratio
“Don’t take yourself too seriously.” — Professor Franco Impellizzeri
Timeline:
00:00 – Introduction: host’s specialist physio training and the load management “viva” question
01:04 – What is load management? An old concept, relabelled
02:31 – From heart rate monitors to GPS: the rise of training data
06:14 – Commercial interests and the risk of becoming “slaves of data”
08:05 – Why load management matters: the drug-dosing analogy
09:24 – Load management as a decades-old coaching concern
10:23 – Confirmation bias and interpreting data “like reading tarot”
13:17 – Championing methodological rigor in sports science
16:33 – Meta-research, honesty, and acknowledging uncertainty
18:22 – Why the most skilled clinicians express the least certainty
19:18 – Origins of the critique of the acute to chronic workload ratio (ACWR)
20:09 – Takeaways for contemporary practitioners on load management models
21:34 – Why the ACWR lacked scientific and conceptual support
23:02 – Training load, time-at-risk, and reframing injury risk
24:24 – The contradictory runners’ study and what it implies
26:43 – Cherry-picking and overfitting in load-injury meta-analyses
28:34 – Taking a stronger public stance on social media
30:34 – Science as a self-correcting process
31:31 – The discipline required to properly read and critique research
33:58 – Why data alone can’t tell you what to do
34:24 – Case study: the soccer drill that “failed” — until the video told the real story
36:49 – The role (and limits) of data scientists in sport
38:46 – Could AI replace the sports scientist’s job?
40:42 – Advice for coaches and practitioners: accept uncertainty
42:09 – Individualised approaches are still a form of generalisation
44:31 – Separating signal from noise — and why not to give up early
46:56 – If only one metric: heart rate over power
48:21 – One piece of advice: “don’t take yourself too seriously”
48:51 – This week’s physical challenge: swimming without pain
50:16 – What’s next: meta-research projects and replication studies
51:43 – A proposal for public scientific debate, and Ian Shrier’s work on consensus
56:32 – Why genuine debate is hard to find in science communication today
57:59 – Episode close
THE TEAM:
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Our goal is to get you back to your Physical Best. Find out more about Telehealth Consultations and book online at pogophysio.com.au.
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🏃 Brad Beer – Instagram & X @Bradbeer and YouTube @PogophysioAu
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