Training load monitoring for fighters

1. What session-RPE actually is

Session-RPE, usually shortened to sRPE, is a method published by Foster and colleagues in the Journal of Strength and Conditioning Research in 2001. The athlete gives a single rating of how hard the whole session was, on a modified category-ratio scale running to 10, and that number is multiplied by the duration of the session in minutes. The product is the session load in arbitrary units.

The 2001 paper's own cohort was small and mixed: six male and six female participants aged 23.0 ± 3.6 years doing steady-state and interval cycling, plus 14 male basketball players aged 20.2 ± 1.5 years across 15 sessions. That is the origin of a method now used across most of professional sport.

A 2017 narrative review in Frontiers in Neuroscience found 950 studies citing the origin paper and identified 36 that had actually examined the validity or reliability of the method using the modified CR-10 scale. That ratio is worth holding onto. It is not evidence that sRPE is invalid — the review is broadly supportive — but it does describe a method whose adoption ran a long way ahead of its testing.

Where the validity work exists, it is mostly correlation against heart-rate-derived load indices. Against Edwards' TRIMP, sRPE correlates at r = 0.56–0.97 in soccer, r = 0.77–0.85 in basketball and r = 0.88 in rowing, all at p < 0.01. In combat sports the same review reports r = 0.57–0.60 for taekwondo aerobic training and r = 0.61 for technical-tactical work, and r = 0.84–0.92 in karate against Banister's TRIMP at p < 0.001.

Those are the only combat-sport validity figures available here, they are taekwondo and karate rather than MMA or boxing, and — this is the important part — they are correlations with another measure of internal load. They are not correlations with injury, and nothing in this literature supports the claim that session-RPE predicts injury.

2. When to ask for the number

There is a small, specific and genuinely useful piece of combat-sport evidence on the mechanics of collecting it.

Uchida and colleagues, publishing in the Journal of Sports Science and Medicine in 2014, worked with eight male Olympic boxers, 18.8 ± 1.8 years old, 66.7 ± 16.5 kg, all with at least four years' experience. In a randomised matched-pairs crossover they ran six standardised 45-minute boxing sessions at three intensities and collected RPE either 10 minutes or 30 minutes after the session finished.

The timings did not differ. Effect sizes ranged from −0.15 to 0.39. The authors' conclusion was that "post-exercise RPE can be collected as fast as 10 minutes after training session with no loss of measurement quality, in contrast to initial recommendations of collecting after 30 minutes."

Practically, that means the rating can be taken while the athlete is still wrapping down, rather than needing to be chased by text message an hour later. It is a small finding and it removes the single most common reason load logs die: nobody remembers to fill them in.

3. Where the 0.8–1.3 range came from

The acute:chronic workload ratio divides a recent block of load — conventionally one week — by a longer rolling average, conventionally four weeks. A value near 1.0 means this week resembles the recent past. A value of 1.5 means this week is half again as heavy.

The "sweet spot" language entered the mainstream through Tim Gabbett's 2016 paper in the British Journal of Sports Medicine, "The training—injury prevention paradox: should athletes be training smarter and harder?" Its figure 6 carries the caption:

"The green-shaded area ('sweet spot') represents acute:chronic workload ratios where injury risk is low. The red-shaded area ('danger zone') represents acute:chronic workload ratios where injury risk is high. To minimise injury risk, practitioners should aim to maintain the acute:chronic workload ratio within a range of approximately 0.8–1.3. Redrawn from Blanch and Gabbett."

Two things about that caption deserve attention. The first is "redrawn" — the figure is a redrawing of a figure in an earlier paper by the same author, not an independent synthesis. The second is that the underlying data come from three sports: cricket, Australian football and rugby league.

The first ACWR–injury study was on elite cricket fast bowlers, with load quantified from session-RPE and balls bowled. As described in the 2016 paper, an ACWR at or below 0.99 was associated with roughly a 4% likelihood of injury in the following seven days, while a ratio of 1.5 or above was associated with a risk two to four times greater in the subsequent seven days. Those numbers are about fast bowlers. They have never been measured in any combat sport, and this article will not restate them as applying to a fighter.

Gabbett's own paper says so, in a sentence that got far less circulation than the figure:

"It is possible that different sports will have different training load–injury relationships; until more data is available, applying these recommendations to individual sport athletes should be performed with caution."

4. The IOC consensus, and its exact wording

Later in 2016, Soligard and colleagues published "How much is too much? (Part 1) International Olympic Committee consensus statement on load in sport and risk of injury" in the same journal. This is the document that turned a figure into a recommendation, and it is the reason the numbers reached coaching syllabi.

Its recommendation reads:

"While it is likely that different sports will have different load-injury profiles, current evidence from Australian football, cricket and rugby league suggests that athletes should limit weekly increases of their training load to <10%, or maintain an acute:chronic load ratio within a range of 0.8–1.3, to stay in positive adaptation and thus reduce the risk of injuries."

And on the model's status:

"The model has currently been validated through data from three different sports (Australian football, cricket and rugby league), demonstrating that injury likelihood is low (<10%) when the acute:chronic load ratio is within the range of 0.8–1.3."

Read those two passages carefully and the consensus is more careful than its reputation. It names its three sports. It says load-injury profiles likely differ between sports. What it does not do is stop anyone quoting "0.8 to 1.3" with none of that attached, which is how a fighter usually meets the number.

As of 2026-09-07, no superseding IOC consensus statement on load and injury risk was found. The 2016 statement stands as written. That is one of the two halves of this story, and dropping it in order to tell a cleaner sceptical story would be its own kind of dishonesty.

5. The statistical objection

The other half arrived from 2019 onward, and it is not a disagreement about effect sizes. It is an argument that the ratio's construction makes its apparent relationship with injury uninterpretable.

Mathematical coupling. In the conventional calculation, the acute load appears in both the numerator and inside the chronic average in the denominator. Dividing a quantity by something that contains it produces correlation whether or not any underlying relationship exists. Lolli and colleagues made this argument directly in the British Journal of Sports Medicine in 2019 under the title "Mathematical coupling causes spurious correlation within the conventional acute-to-chronic workload ratio calculations," and Impellizzeri and colleagues develop it at length in their 2020 Journal of Athletic Training paper. Wang, Trejo Vargas, Stokes, Steele and Shrier's review states the consequence as a recommendation:

"The acute:chronic workload ratio should exclude the acute load from the chronic load"

Arbitrary windows. Chronic windows of one to eight weeks and acute windows of one to two weeks have all been used, with no biological rationale offered for any of them. The one-week-over-four-weeks convention is a convention, not a physiological constant.

Binned endpoints. The figure that produced the sweet spot discretised its own data: ACWR values from 0 to 0.5 were binned as 0.5, and values of 2.0 and above binned as 2.0, "likely partially due to the relative lack of data at each value." The extremes of the curve everyone quotes are, in part, an artefact of sparse data being grouped before the model was chosen.

Interchangeable chronic loads. In a 2021 Sports Medicine paper titled "What Role Do Chronic Workloads Play in the Acute to Chronic Workload Ratio? Time to Dismiss ACWR and Its Underlying Theory," Impellizzeri and colleagues substituted the real chronic load with fixed or randomly generated values and reported injury associations as strong as those from the real ratio, calling for frameworks, recommendations and consensus statements to be updated. If a random denominator performs as well as the real one, the denominator is not carrying the meaning it was supposed to carry.

Wang and Shrier's review enumerates eight problems in total: the conflation of a proportion with a change, the use of unweighted averages, inapplicability to sports that taper, discretisation of the exposure before model selection, a model built on sparse data, bias in the ratios of already-injured athletes, unmeasured confounding, and application to subsequent injuries. The taper point lands squarely on combat sports: a fighter deliberately drives their ratio well below 0.8 in the last fortnight, and the model has nothing sensible to say about that. If you want what the taper literature actually supports, that is its own article.

6. The simulation that produced an effect from nothing

The single most instructive result in this dispute is a simulation.

Bornn, Ward and Norman presented work at the MIT Sloan Sports Analytics Conference in 2019 titled "Training Schedule Confounds the Relationship between Acute:Chronic Workload Ratio and Injury." They built datasets on two real schedules — Serie A soccer and the NFL — in which, by construction, there was no relationship between load and injury. They then computed the ACWR and looked for one anyway.

Impellizzeri and colleagues describe the result in their 2020 Journal of Athletic Training paper:

"Interestingly, this relationship existed despite the simulation's being designed to have no relationship at all. Once the training day was considered in the model, representing a function of the underlying training schedule, the relationship between the ACWR and injury no longer existed. Such results suggest that relationships with the ACWR in earlier studies might be nothing more than statistical artifacts."

The magnitudes: an ACWR outside 0.8–1.3 produced a 6.8% increase in injury in the Serie A data and 10.5% in the NFL data. In data built to contain nothing.

That does not prove the ACWR–injury relationship in real data is entirely artefact. It does show that a study of this design, run on a real squad, could produce exactly the published finding without any physiological effect existing, unless the training schedule is modelled. Most of the original literature did not model it.

7. What the critics actually conclude

The strongest statement comes from Impellizzeri, Tenan, Kempton, Novak and Coutts in the International Journal of Sports Physiology and Performance in 2020, in a paper subtitled "Conceptual Issues and Fundamental Pitfalls":

"There is no evidence supporting the use of ACWR in training-load-management systems or for training recommendations aimed at reducing injury risk. The statistical properties of the ratio make the ACWR an inaccurate metric and complicate its interpretation for practical applications. In addition, it adds noise and creates statistical artifacts."

And, on the causal question specifically:

"Because no studies have even tried to estimate causal effects properly, manipulating ACWR in practical settings in order to change injury rates remains a conjecture and an overinterpretation of the available data."

That last sentence is the precise shape of the problem. Even if the association were real, it would not follow that moving an athlete's ratio changes their injury rate. Association between a number and an outcome does not licence intervening on the number.

The same authors are equally clear that the field's response — inventing corrected variants, exponentially weighted versions, uncoupled versions — has not fixed the underlying gap: "The domino effect of the ACWR is the proliferation of new metrics, all characterized by the lack of any physiological explanation."

And they name a broader failure that applies well beyond this one ratio: "The lack of any conceptual framework also gives investigators too many degrees of freedom, which can dramatically increase the risk of false discoveries and confirmation bias by forcing the interpretation of results toward common beliefs and accepted training principles."

8. Where the pooled evidence stands in 2026

Three syntheses matter, and they do not say the same thing.

2020 systematic review. Maupin, Schram, Canetti and Orr reviewed 27 studies in Open Access Journal of Sports Medicine, in adults only — youth were excluded — across team and field sports. They found the lowest injury risk at 0.80–1.30 and described the relationship as parabolic. But they could not meta-analyse, and flagged the coupling problem. Their own summary line: "Given the high variability in studies, little statistical information can be constructed based on the current research."

2025 meta-analysis. Qin, Li and Chen pooled 22 cohort studies, 921 participants and 657 injuries in BMC Sports Science, Medicine and Rehabilitation. Overall pooled effect size 0.72 (95% CI 0.60–0.82), with I² = 92.9%. By band: below 0.8, 0.74 (0.68–0.80); the supposedly protective 0.8–1.3 band, 0.56 (0.14–0.94); above 1.3, 0.77 (0.58–0.92). Seventeen of the 22 studies were soccer, and only one included mixed sexes. Note the confidence interval on the sweet spot — 0.14 to 0.94 — which is wide enough to overlap almost everything it is supposed to be distinguished from. Their conclusion: "Although ACWR is associated with sports injury risk and may be useful in injury prevention strategies, it is necessary to use it with caution as a tool for measuring workload."

2026 multilevel meta-analysis. Ding, Weldon, Xu, Malone, Sampaio, Zhang and colleagues screened 10,187 records in Frontiers in Public Health, reviewed 41 studies and meta-analysed 16 covering 797 athletes. An elevated ACWR was associated with Hedges' g = 0.35 (95% CI 0.16–0.54), with I² = 95.8%. The only significant moderator was injury type: contact g = 0.54, non-contact g = 0.55, time-loss g = 0.01. Load metric, calculation method and time window were all non-significant — which is to say the parameters practitioners argue hardest about did not change the answer. No combat sports were included. Their statement:

"Elevated ACWR may be associated with increased injury risk in specific contexts, but current evidence does not support its use as a stand-alone causal or predictive model."

The honest summary of the position as of 2026-09-07 is this. A standing 2016 consensus recommends 0.8–1.3 and the sub-10% weekly increase and has not been withdrawn or superseded. A small positive pooled association survives in 2025 and 2026 data, with heterogeneity above 90% in both. And the current pooled statements and the metric's principal critics both decline to treat it as predictive or causal. A 2021 Frontiers in Physiology editorial convened specifically on the question concluded: "Overall, the literature on ACWR research is controversial, which is why more research is needed." That is where it sits.

9. What load monitoring is still for

The critics did not conclude that monitoring is worthless. They concluded something narrower and more useful, and they said it plainly in the same 2020 paper that dismantles the ratio:

"In essence, the TL can be used to see whether the TL that was planned was actually done by the athlete."

"Practitioners should rely on traditional training principles such as overload progression and adjust the TL based on the athletes' responses."

"Training-load measures cannot tell us whether the variations are increasing or decreasing the injury risk; we recommend that practitioners still rely on their expert knowledge and experience."

That is the supported use, stated by the people most opposed to the ratio. A load log does three things reliably.

It describes what happened. Most fighters cannot tell you within 30% how much they trained last month. A log can, and the gap between the plan and the record is usually the interesting part.

It makes a spike visible. Not as a risk score, as a fact. A week that was 40% heavier than the four before it is a week that was 40% heavier than the four before it, and knowing that is different from guessing it.

It gives the athlete and the coach the same page to argue from. This is the underrated one. "That week felt brutal" and "that week was your heaviest of camp by 600 AU" are the same observation, but only the second one can be checked against the plan, and only the second one survives the disagreement.

None of that requires the ratio. It requires RPE, minutes, and the discipline to write them down. If you want a structure for reading those numbers weekly rather than staring at them daily, see the weekly camp review.

10. What a fight camp actually looks like on paper

There is very little measured combat-sport load data. Here is what exists.

MMA. Kirk, Langan-Evans, Clark and Morton followed 14 UK MMA athletes — 22.4 ± 4.4 years, 71.3 ± 7.7 kg, 171 ± 9.9 cm, mostly high-level amateurs, seven with a booked bout and seven without — for eight weeks plus a familiarisation week, publishing in PLoS ONE in 2021. Weekly training duration ran 3.9–5.3 hours. Weekly sRPE ran 1,287–1,791 AU. Strain ran 1,143–1,819 AU, monotony 0.63–0.83 AU with between-week monotony 2.3 ± 0.7, and fatigue 16–20 AU. None of it changed within or between weeks.

Those figures describe mostly high-level amateurs training under five hours a week. They are not a picture of a professional camp and should not be read as one.

The intensity distribution in the same cohort was 47% low, 33% moderate and 20% high. MMA sparring and wrestling sparring were rated high, at RPE 7 or above; BJJ sparring, striking sparring and wrestling drills moderate, at RPE 5–6; striking drills and BJJ drills low, at RPE 4 or below. The authors' conclusion: "Periodisation of training load was largely absent in this cohort of MMA athletes, as is the case within and between weekly microcycles." There was an abrupt one-week taper rather than a graded one.

Boxing. Aydın, Altuğ, Yılmaz, Badau and Söyler followed 20 elite junior female boxers, 18.9 ± 1.2 years, 59.8 ± 6.1 kg, all with at least three years of structured training, through a 10-week preparatory phase, publishing in Life in 2026. Weekly sRPE peaked at 3,322.36 ± 487.43 in week 1 and bottomed at 1,239.48 ± 279.37 in week 7, running 3,024 (wk 2), 1,575 (wk 3), 1,933 (wk 4), 1,611 (wk 5), 2,856 (wk 6), 1,758 (wk 8), 2,130 (wk 9) and 2,261 (wk 10) in between. Monotony was highest in week 1 at 1.14 ± 0.08 and lowest in week 5 at 0.38. Strain peaked in week 2 at 3,734.96 ± 771.65 and bottomed in week 5 at 616.31 ± 23.99. Menstrual cycle status was not monitored.

Judo. Ouergui, Franchini, Selmi and colleagues followed 61 judo athletes, 37 of them male, aged 14 to 17 with more than seven years of experience, through four weeks of intensified training — four sessions a week, two of them HIIT — and a 12-day taper, publishing in the International Journal of Environmental Research and Public Health in 2020. Session-RPE was predicted by total quality of recovery during the intensified block (r² = 0.387) and by sleep and the Hooper Index during the taper (r² = 0.212). That is an adolescent cohort and should not be presented as adult data.

Two things stand out across all of it. The load swings in a real preparatory block are much larger than the ACWR literature's thresholds contemplate — the junior boxers went from 3,322 AU to 1,239 AU and back to 2,261 AU inside ten weeks. And in the only MMA cohort, nothing moved at all for eight weeks, which is arguably the more alarming finding and was only visible because someone was recording RPE × minutes. None of this data says how many weeks a camp should run in the first place; how long should a fight camp be works through that separate question directly.

11. The elegant detail

The clearest illustration of where the field has landed is not in a critique. It is in that 2026 boxing paper.

Aydın and colleagues calculated the acute:chronic workload ratio themselves, from session-RPE and from jump counts, across all ten weeks. And then they wrote:

"ACWR values are provided for contextual and descriptive purposes and should not be interpreted as indicators of injury risk or causal load–performance relationships."

They also wrote: "Causal inferences cannot be made within this observational design."

That is a combat-sport research group in 2026 computing the number, printing the number, and stating in the same paper that the number does not mean what it is popularly taken to mean. It is a better summary of the current settlement than any position statement: the ratio survives as a description of change, and has been retired as a forecast.

12. What this means for a working camp

The practical translation is short, and it is deliberately not a set of thresholds.

Log RPE and minutes for every session. Ask at 10 minutes post-session, not 30 — the boxing crossover found no difference, and the earlier ask is the one that actually gets answered. One number per session, not per drill.

Read the weekly total against the previous weeks as a description. If this week is 40% above the recent average, that is a fact about your week, not a probability of injury. The right response is to check it against what was planned, and to ask the athlete how they feel — which is what the sceptics recommend, and what the judo data supports, since recovery and sleep scores predicted perceived session load in that cohort.

Do not use a ratio to authorise or forbid a session. The band is a historical artefact from three team sports with binned endpoints, mathematically coupled, with arbitrary windows and confidence intervals in the current pooled data that overlap what they are meant to distinguish. Combat sports appear nowhere in the meta-analyses. Coaching judgement, symptoms and the plan are what should decide the session.

Expect the log to disagree with your memory. In the only MMA cohort measured, eight weeks passed with no detectable variation in load, which is not what any of those athletes would have reported from recall.

Keep the log through the injury, not just around it. What load was being carried before, during and after a problem is the part nobody records and everybody later wants. That interacts directly with the descent, which is a separate problem.

Fighter Cut logs sessions this way — type, duration and RPE, with the weekly total shown as a total and not as a risk score — because a description is what the evidence supports and a forecast is not.

What we could not verify

No named professional combat-sports programme. There is no peer-reviewed, attributed description available here of how a named professional combat-sports performance staff monitors load. The nearest things are a qualitative interview study with four anonymised full-time UK MMA coaches, and the UFC Performance Institute's own cross-sectional reports (Vol. 1, 2018; Vol. 2, 2021) — which are published by the promotion that employs the athletes they describe, are not peer reviewed, and can only ever be read as what one organisation says about itself, never as evidence. Rather than infer a programme from either, we are saying plainly that none is documented.

What the interview study does show is worth reporting on its own terms. All four coaches knew what RPE and wellness monitoring were. None of them used either. The only objective measure any of them used was heart rate, "to gain a general overview of the training day." One coach described building conditioning sparring into sessions "two to three times a week." Those are qualitative accounts from four people and cannot be used to quantify anything.

Women. One study here is female — the 20 elite junior boxers — and it did not track menstrual cycle status. The 2025 meta-analysis contained one mixed-sex study out of 22. The MMA load study does not report a sex breakdown at all. There is no ACWR threshold derived from, or validated in, female athletes.

Adolescents. The judo cohort, aged 14 to 17, is the only youth data here, and the 2020 systematic review explicitly excluded youth. The IOC consensus flags developing athletes as higher risk but offers no youth-specific threshold.

No combat sport in any ACWR meta-analysis. The 2026 analysis includes none; the 2025 analysis is 77% soccer.

No Muay Thai, kickboxing or wrestling load figures exist in these sources at all. Not in arbitrary units, not in hours. The taekwondo and karate numbers quoted earlier are validity correlations against heart-rate indices, not load volumes, and cannot be converted into one.

Sparring volume. No source here quantifies sparring rounds per week in a camp with an actual cohort. Session types were rated for intensity; they were not counted. Head-impact exposure is out of scope for every source used.

Blanch and Gabbett 2016, the paper the sweet-spot figure was redrawn from, and the original cricket fast-bowler study, were not read directly for this article. Everything attributed to them here is attributed as described by the 2016 review that cites them, and no wording is put in their mouths.

Status check. The claim that the 2016 IOC consensus stands unsuperseded rests on searches conducted on 2026-09-07 finding no newer IOC load consensus. Absence of a newer statement in a search is weaker evidence than a registry check, and it is stated here as what it is.

Sources

Sourced to

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4. The training—injury prevention paradox: should athletes be training smarter and harder? — Gabbett TJ, British Journal of Sports Medicine, 2016;50(5):273–280. DOI 10.1136/bjsports-2015-095788
5. How much is too much? (Part 1) International Olympic Committee consensus statement on load in sport and risk of injury — Soligard T, Schwellnus M, Alonso J-M et al., British Journal of Sports Medicine, 2016;50(17):1030–1041. DOI 10.1136/bjsports-2016-096581
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7. Training Load and Its Role in Injury Prevention, Part 2: Conceptual and Methodologic Pitfalls — Impellizzeri FM, McCall A, Ward P, Bornn L, Coutts AJ, Journal of Athletic Training, 2020;55(9):893–901. DOI 10.4085/1062-6050-501-19
8. The acute:chronic workload ratio: challenges and prospects for improvement — Wang C, Trejo Vargas J, Stokes T, Steele R, Shrier I, arXiv:1907.05326, 2019
9. Mathematical coupling causes spurious correlation within the conventional acute-to-chronic workload ratio calculations — Lolli L, Batterham AM, Hawkins R et al., British Journal of Sports Medicine, 2019;53(15):921–922. PMID 29101104
10. What Role Do Chronic Workloads Play in the Acute to Chronic Workload Ratio? Time to Dismiss ACWR and Its Underlying Theory — Impellizzeri FM, Woodcock S, Coutts AJ, Fanchini M, McCall A, Vigotsky AD, Sports Medicine, 2021;51(3):581–592. DOI 10.1007/s40279-020-01378-6
11. Training Schedule Confounds the Relationship between Acute:Chronic Workload Ratio and Injury — Bornn L, Ward P, Norman D, MIT Sloan Sports Analytics Conference, Boston, 2019
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13. The Relationship Between Acute:Chronic Workload Ratios and Injury Risk in Sports: A Systematic Review — Maupin D, Schram B, Canetti E, Orr R, Open Access Journal of Sports Medicine, 2020;11:51–75. DOI 10.2147/OAJSM.S231405
14. Acute to chronic workload ratio (ACWR) for predicting sports injury risk: a systematic review and meta-analysis — Qin W, Li R, Chen L, BMC Sports Science, Medicine and Rehabilitation, 2025;17:285. DOI 10.1186/s13102-025-01332-x
15. Acute:chronic workload ratio and load management for team sports: a multilevel meta-analysis — Ding L, Weldon A, Xu J, Malone S, Sampaio J, Zhang Z et al., Frontiers in Public Health, 2026;14:1896651. DOI 10.3389/fpubh.2026.1896651
16. Quantification of training load distribution in mixed martial arts athletes: A lack of periodisation and load management — Kirk C, Langan-Evans C, Clark DR, Morton JP, PLoS ONE, 2021;16(5):e0251266. DOI 10.1371/journal.pone.0251266
17. Weekly Fluctuations in Internal Load and Neuromuscular Performance Across a 10-Week Training Period in Elite Female Boxers — Aydın AS, Altuğ T, Yılmaz C, Badau A, Söyler M, Life (Basel), 2026;16(3):386. DOI 10.3390/life16030386
18. Relationship between Perceived Training Load, Well-Being Indices, Recovery State and Physical Enjoyment during Judo-Specific Training — Ouergui I, Franchini E, Selmi O et al., International Journal of Environmental Research and Public Health, 2020;17(20):7400. DOI 10.3390/ijerph17207400
19. An evaluation of coaching and training practices in mixed martial arts — Kirk C, PhD thesis, Liverpool John Moores University, 2022

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