--- title: "Sub-3 Marathon: The 4% Club, and How to Join It" description: "Explore sub-3 marathon training: race equivalents, weekly mileage, key workouts, tapering and fueling. Learn why readiness involves more than a calculator." canonical_url: "https://runima.fit/blog/sub-3-marathon" --- > Index of every page on this site: [llms.txt](https://runima.fit/llms.txt) Cross the finish line at any big-city marathon and count the runners around you. On average, only about **1 in 22** of them broke three hours — roughly **4.5% of finishers** across mass-participation marathons in 2025, and the split by sex is stark: about **4% of men** and about **1% of women**. (These come from race-results aggregators rather than peer-reviewed epidemiology, and they swing hard by race — a flat, deep, qualifier-heavy field can run 12%+, a big-city charity field far less.) Sub-3 isn't a nice round number someone picked because it sounds impressive. It's a real, measurable, sub-elite physiological standard — and it requires three physiological factors to be strong enough at once. Long-time readers will recognize the three factors. [VO 2 max](https://runima.fit/blog/the-vo2max-trap) sets aerobic capacity. [Lactate threshold](https://runima.fit/blog/lactate-threshold) sets the highest pace you can sustain for a long time. [Running economy](https://runima.fit/blog/running-economy) determines the oxygen cost of a given pace. Sub-3 is where all three stop being separate blog posts and become one specific question: what does it take to hold **6:52/mile (4:16/km)** for 26.2 miles? This is a research-backed answer — with every claim rated for how solid the evidence actually is. ## The three dials that have to line up The core framework is longstanding and still well supported: marathon speed ≈ VO 2 max × the fraction of it you can sustain at lactate threshold × running economy. [Joyner's 1991 model](https://pubmed.ncbi.nlm.nih.gov/2022559/) remains the field's central framework, and a [2026 revalidation](https://link.springer.com/article/10.1007/s40279-026-02439-y) across 888 athletes — **495 runners and 393 cyclists**, recreational to world-class — confirmed that threshold-speed proxies still predict performance, while flagging one honest gap: lab tests don't capture "durability" (fatigue resistance late in the race) or race-day execution. That revalidation changes the relative weight of these factors. In runners, **running economy contributed 22% and 20%** to speed at lactate threshold and lactate turnpoint respectively, while **fractional utilization contributed only 4-6%**. The percentage of VO 2 max you can hold — the factor that gets the most attention in training talk — turned out to be the smallest of the three in this dataset. Note this measured *lab proxies* for performance, not marathon finish times directly. Recreational marathoners in the 3:00-3:30 band average **~55.7 ± 4.8 ml/kg/min** ([Gordon et al., 2017](https://pubmed.ncbi.nlm.nih.gov/29200895/), n=97); "medium-level" (~3:14-3:45) runners sit at **55.6 ± 3.6** ([Myrkos et al., 2020](https://pmc.ncbi.nlm.nih.gov/articles/PMC7552741/), n=15). Literature norms run recreational 51-58, low-level ~65, up to 70-85 in the elite "top-class" cohort (sub-2:11 men) studied by [Billat et al., 2001](https://pubmed.ncbi.nlm.nih.gov/11740304/) (n=20). Published estimates of the **practical floor for sub-3 range from ~57 to ~63 ml/kg/min** for a typical-mass man — the low end from extrapolating cohort data, the high end from a VDOT/Mader-model estimate. Women need higher relative values (~60-70), because sub-3 is a more elite standard for them. Nobody has measured a cohort of runners *at* 2:55 and reported the number, so all of these are extrapolations, and the spread between them is wider than any single estimate suggests. Notice what the cohort data implies, though: if 3:00-3:30 runners already average 55.7, then the VO 2 max step from 3:15 to 2:55 is small. **VO2max is rarely the main limiting factor at this level** — threshold velocity, economy, and the volume tolerance to build them are. Medium-level marathoners race at **79.7 ± 7.7% of VO2max** — roughly 105% of their lactate-threshold velocity ([Myrkos et al., 2020](https://pmc.ncbi.nlm.nih.gov/articles/PMC7552741/)) — meaning true 2:40-3:00 fractional utilization is likely **80-85%**, inside the general 70-90% range for marathon effort ([Sjödin & Svedenhag, 1985](https://pubmed.ncbi.nlm.nih.gov/3890068/)). Working backward from 6:52/mile marathon pace implies a threshold pace near **6:25-6:35/mile** (about 10K-to-half-marathon effort). The classic demonstration: runners training >100 km/week were **19.2% faster** across all events than those training less, with **no difference in VO2max and none in fractional utilization** — the entire gap was explained by **19.9% better running economy** ([Scrimgeour et al., 1986](https://pubmed.ncbi.nlm.nih.gov/3699009/), n=30, three groups of ten). It's a small, cross-sectional 1986 study, so read it as a well-aimed illustration rather than proof of causation — high-mileage runners may simply be the ones economical enough to survive high mileage. Economy itself can vary **20-30% between runners with identical VO2max** ([Barnes & Kilding, 2015](https://pmc.ncbi.nlm.nih.gov/articles/PMC4555089/)). Coaching sources cite sub-elite economy around 180-200 ml/kg/km against >220 for recreational runners. The [Gordon (2017)](https://pubmed.ncbi.nlm.nih.gov/29200895/) data (n=97) point to a related finding: what tracked race speed most strongly was **absolute lactate-threshold velocity** — LT1 at r = 0.791, LT2 at r = 0.721 — rather than VO 2 max or the *percentage* of it used at threshold. Alongside those, the variables that separated the fastest band (2:30-3:00) from the slowest were plainly behavioral: **training frequency and distance per session** (16.1 ± 4.2 km vs 10.3 ± 2.6 km). Body composition matters too: in a cohort of 126 recreational male marathoners, faster training speed and lower body fat both predicted race time (training speed β = -0.52, body fat β = 0.27, model r² = 0.44), with body fat in the **13-17%** range associated with the fastest finishers ([Barandun et al., 2012](https://pmc.ncbi.nlm.nih.gov/articles/PMC3781899/); [Tanda & Knechtle, 2013](https://pubmed.ncbi.nlm.nih.gov/24379719/)). ## What does a recent race actually tell you?
Distance Approx. equivalent for sub-3 (2:59:xx)
Marathon pace 6:52/mile (4:16/km)
Half marathon ~1:24-1:27
10K ~38:00-39:30
5K ~18:00-19:00
[Riegel's classic exponent](https://pubmed.ncbi.nlm.nih.gov/7235349/) (1.06) and VDOT-style tables are accurate within **1-3% for adjacent distances** — but the marathon is the least reliable jump in the entire prediction chain. Riegel fitted his exponent to *record* performances, athletes optimally prepared at every distance, and the further you extrapolate from that assumption the more likely the formula is to overpredict your performance. It systematically predicts marathon times faster than runners actually run, and the size of the error depends mostly on how much marathon-specific endurance you've built — which is exactly what a 5K can't see. In a survey of 2,303 recreational runners, weekly training mileage plus recent race results actually beat the Riegel formula at predicting marathon time ([Vickers & Vertosick, 2016](https://pmc.ncbi.nlm.nih.gov/articles/PMC5000509/)) — a reminder that your own training log can out-predict a generic formula. A [2024 LSTM deep-learning model](https://pubmed.ncbi.nlm.nih.gov/39439845/) reported beating Riegel on running-log data, and a "big data" critical-speed approach built from ~25,000 Strava runners' training files ([Smyth & Muniz-Pumares, 2020](https://pmc.ncbi.nlm.nih.gov/articles/PMC7664951/)) can predict marathon pace directly from training data, without a dedicated race at all. The consistent theme across all three: **training data beats a single race time**, especially for the marathon. Feed a recent result straight into the [Race Time Predictor](https://runima.fit/tools/race-time-predictor) — and if you want a concise overview of how VDOT turns one race into every other distance, the [Race Time Prediction cheatsheet](https://runima.fit/cheatsheets/race-time-prediction) has it. ## The training that actually works ### Volume is the dominant lever Seven training variables all correlate negatively with marathon finish time — average weekly distance, number of weekly runs, biggest single week, number of runs ≥32 km, average training pace, longest run, and weekly hours — making volume the most consistent modifiable determinant in the literature ([Doherty et al., 2020](https://pubmed.ncbi.nlm.nih.gov/31704026/); meta-regression of 85 studies, 137 cohorts, 25% female, R² 0.38-0.81, p<0.001). Weekly km plus training pace together explain about **77%** of finish-time variance in a 2:47-3:36 band ([Tanda, 2011](https://doi.org/10.4100/jhse.2011.63.05)) — though with n=22, that figure is far less precise than it looks. **Interpret the Strava-based estimates cautiously.** Coaching outlets citing an analysis of ~119,452 runners' training data report sub-3 athletes peaking around **94 km/week** in the 2:50-2:59 band, rising toward 160 km/week for sub-2:20 — and claim weekly kilometers alone explain **over 90%** of performance variation. That last figure should not be repeated as-is: an R² above 0.9 for a single variable is wildly out of line with every peer-reviewed estimate (Doherty's meta-regression tops out at 0.81 across *all* training variables; Tanda's needs two variables to reach 0.77), and it is the signature of a curve fitted to **binned group averages** rather than individual runners — a procedure that inflates R² dramatically. The volume *ranges* are a useful sanity check. The variance claim is not evidence. Two limits apply to this entire section. All of it is **observational**, and the reverse-causation story is strong: runners capable of sub-3 can absorb 100+ km/week, which is not the same as 100 km/week producing sub-3. And returns diminish while injury risk climbs at the top end — most sub-3 plans plateau in the **~80-120 km/week** range for good reason. ### Mostly easy, with targeted hard sessions Elite and sub-elite distance runners train **~75-80% at low intensity**, the rest split between threshold and high-intensity work, in either a pyramidal or polarized pattern ([Casado et al., 2022](https://doi.org/10.1123/ijspp.2021-0435); [Stöggl & Sperlich, 2014](https://pmc.ncbi.nlm.nih.gov/articles/PMC3912323/)). An **81/12/8** split beat a **67/25/8** split head-to-head over 5 months — but the details matter: the sample was **12 subelite runners**, and the outcome was a simulated **10.4 km cross-country race** (-157 ± 13 s vs -121.5 ± 7.1 s, p = 0.03), not a marathon ([Esteve-Lanao et al., 2007](https://pubmed.ncbi.nlm.nih.gov/17685689/)). A [2025 trial of 120 recreational marathoners](https://www.nature.com/articles/s41598-025-25369-7) found polarized training produced **~30% greater improvement** than pyramidal (11.3 vs 8.7 minutes) — but its more useful result is the spread: polarized responders 31.5%, pyramidal responders 31.9%, dual 18.7%, and **17.9% who responded to neither**. The group average hid a near-even split. That "polarized wins" result is genuinely contested. A 2022 point/counterpoint in *Medicine & Science in Sports & Exercise* between [Foster, Seiler et al.](https://pubmed.ncbi.nlm.nih.gov/35136001/) and [Burnley, Bearden & Jones](https://pubmed.ncbi.nlm.nih.gov/35135998/) notes that many elite training logs labeled "polarized" are actually pyramidal once intensity is classified properly. The clearest point of agreement: **the bulk of running should be easy.** The long run gets the same volume treatment: the number of runs **≥32 km** in the final marathon block correlates with faster times, and the 92-plan analysis found a peak long run of **30-32 km** across every volume tier. This is coaching consensus (Daniels, Pfitzinger, Hansons) more than direct dose-response RCT evidence — nobody has randomized long-run distance directly. ## The taper, the lifts, and the fuel Two independent meta-analyses agree: a **2-week taper**, cutting volume by **41-60%** while holding intensity and frequency steady, is the most efficient strategy ([Bosquet et al., 2007](https://pubmed.ncbi.nlm.nih.gov/17762369/), 27 of 182 studies screened; effect of 2-week duration = 0.59 ± 0.33, P < 0.001; effect of 41-60% volume cut = 0.72 ± 0.36, P < 0.001). A follow-up ([Wang et al., *PLoS ONE*, 2023](https://pmc.ncbi.nlm.nih.gov/articles/PMC10171681/), 14 studies) confirmed it — SMD -0.45 overall, -0.77 for the 41-60% cut specifically, with the biggest gains at **8-14 days**. Typical payoff: **2-3%**, or 3-6 minutes for a 3-hour runner. A [systematic review with meta-analysis](https://pmc.ncbi.nlm.nih.gov/articles/PMC11052887/) puts high-load strength training at **ES ≈ -0.27** and combined strength-plus-plyometric methods at **ES ≈ -0.43** for improving running economy — small-to-moderate, but real. Plyometrics helps especially at slower speeds (≤12 km/h, ES ≈ -0.31); heavy resistance work pays off more at higher speeds and higher VO 2 max. Submaximal-load and isometric training showed no significant effect. Certainty: moderate — one of the better-supported additions to marathon training. Carb-loading at **10-12 g/kg/day for 36-48 hours** pre-race supercompensates glycogen (ACSM/Burke); a [2025 meta-analysis of 30 studies](https://pmc.ncbi.nlm.nih.gov/articles/PMC12399638/) confirmed muscle glycogen increases averaging **~156.5 mmol/kg dry weight** after a depletion-plus-loading protocol. In-race, **60-90 g/hour** of multiple transportable carbohydrates (2:1 glucose:fructose) enables ~90-108 g/h oxidation; sub-3 runners typically target **80-90 g/h**. GI tolerance has to be trained over 8+ weeks — and remember, each gram of stored glycogen retains ~2.7 g of water. ## The injury risk many runners overlook A systematic review of 23,047 runners across 36 studies puts pooled running-injury incidence at **26.2%** — 14.9% for novices, 26.1% for recreational runners, and a striking **62.6% for competitive runners**. If you're chasing sub-3, that last number is substantial. The old "10% rule" is only weakly supported: an RCT of 532 novices compared a standard 8-week program against a 13-week program built around the 10% rule and found **20.8% vs 20.3%** injury rates (p = .90) — five extra weeks of gentler progression bought nothing measurable ([Buist et al., 2008](https://pubmed.ncbi.nlm.nih.gov/17940147/)). The more informative signal concerns single-session spikes. The [Garmin-RUNSAFE study](https://pubmed.ncbi.nlm.nih.gov/40623829/) (Frandsen et al., *Br J Sports Med* 2025;59:1203-1210 — 5,205 runners, mean age 45.8, 22% female, 588,071 sessions, 1,820 injured) found a **significant jump in overuse injury when a single session runs more than 10% longer than your longest run of the previous 30 days** — while "no relationship was identified for the week-to-week ratio." Against a reference of regression or ≤10%: **>10-30%** longer → hazard ratio **1.64** (95% CI 1.31-2.05); **>30-100%** longer → **1.52** (1.16-2.00); **more than double** → **2.28** (1.50-3.48). Note those three aren't a clean dose-response — the middle band sits slightly *below* the smallest, with heavily overlapping intervals. The finding is "any meaningful spike past your recent longest carries risk," not "risk scales smoothly with spike size." Previous injury and marathon training itself are additional risk factors. That's a genuinely different lever than the one most runners track. If you're used to watching your acute:chronic workload ratio, it's worth knowing that the ACWR has fared badly under scrutiny: a Frontiers review concluded there is "no evidence supporting the use of ACWR in training-load-management systems," citing mathematical flaws in how the ratio couples its own numerator and denominator ([Impellizzeri et al., 2021](https://pmc.ncbi.nlm.nih.gov/articles/PMC8138569/)). RUNSAFE points in the same direction in this cohort — no week-to-week ratio effect at all. Watch the trend in your load and the size of your single longest sessions; don't manage your training to a ratio threshold. ## How long will this actually take you? An important limitation is that **no RCT or cohort study directly tracks time-to-sub-3 by starting point.** Everything below synthesizes adaptation-rate physiology with training-determinant data and coaching consensus — treat it as informed estimation, not established fact. VO 2 max is about **50% genetically determined** ([HERITAGE Family Study](https://pubmed.ncbi.nlm.nih.gov/10484570/); Bouchard's 20-week program in 481 adults produced a mean +0.4 L/min gain, with ~7% non-responders and ~8% high-responders). Untrained people gain **15-25%** VO 2 max in 3-6 months; trained runners gain only **3-8% per year** and increasingly rely on economy and lactate-threshold improvements, which keep accruing for years. In short: initial aerobic gains occur relatively quickly, but the economy, threshold, and injury-resistant volume tolerance that actually *enable* sub-3 take years of consistent training.
Persona Realistic time to sub-3 Basis / caveats
Complete beginner (never run) 3-5+ years; many never Needs VO2max, LT, economy, AND years of injury-resistant volume tolerance
Recreational 3:30-4:00 2-4 years Needs a large volume increase plus LT/economy gains; many never reach it
Recreational 3:00-3:15 ~1-2 years Mostly volume/specificity + marginal gains; closest starting group
Former collegiate/HS track 1-2 years High residual VO2max/economy; main task is rebuilding endurance base
Masters 40s Harder, but common Age decline stays modest with consistent training
Masters 50s+ Progressively rarer Cumulative VO2max/economy decline
Women (all ages) Sub-3 ≈ elite ~11-12% physiological sex gap; roughly a 2:40 male-standard effort
**Age alone does not rule out sub-3 as quickly as many runners assume.** [Lepers and colleagues](https://pmc.ncbi.nlm.nih.gov/articles/PMC7959843/) studied 40 runners (39 men, 1 woman) who broke three hours in **five consecutive decades**, spanning 32.9 ± 1.6 years between their first and last sub-3. Their speed declined **0.67 ± 0.29% per year** — about 64 seconds a year, or under 7% per decade to at least age 60. One caveat that's easy to miss: this cohort had a mean personal best of **2:23 ± 9 min at age 28.6**, so they were starting with roughly 37 minutes of headroom below three hours. The encouraging part is the *slope* of decline under sustained training; the cohort itself is heavily selected. The sex gap is one of the strongest-evidenced numbers in the whole review: the physiological gap in elite marathon performance is **~11-12%** (Hunter/Joyner; [Hallam & Amorim, 2022](https://pmc.ncbi.nlm.nih.gov/articles/PMC8764368/); the current world-record gap sits at 10.7%), driven largely by VO 2 max and body-composition differences that emerge at puberty. The gap you actually *see* in race-day fields is bigger — around 18% — and a meaningful share of that excess is a participation-depth artifact rather than physiology: fewer women finish overall, which thins the fast tail of the distribution. Estimates of how much of the field gap that accounts for vary by race and method, so treat the ~11-12% physiological figure as the well-evidenced number and the field-gap decomposition as approximate. Age-related decline is also steeper for women: female age-group winners slow **~2:33/year** versus male **~2:06/year** after age 35 ([Zavorsky et al., 2017](https://pubmed.ncbi.nlm.nih.gov/28187185/)). ## How do you know you're actually on track? **Field tests.** Critical speed via the 3-minute all-out test (3MT) or a 2-3 time-trial protocol is reliable (ICC ~0.95, CV ~3%) and valid for critical speed, though it underestimates anaerobic work capacity (D′) by around 16% ([Pettitt et al., 2012](https://pubmed.ncbi.nlm.nih.gov/22422309/); [systematic review, 2025](https://pmc.ncbi.nlm.nih.gov/articles/PMC11933073/), 19 studies, 285 participants). Time trials of 3-20 minutes with recovery gaps of 7+ minutes give valid results, and critical speed separates "heavy" from "severe" effort more reliably than heart rate alone. Single-study findings within that review — such as a 10-minute submaximal treadmill test predicting critical speed at r = 0.93 — are promising but not independently replicated; the reliability figures above are the part that's been established across studies. **Wearables and training-load models.** CTL, ATL, and TSB are widely used but self-referential — CTL doesn't change for a fixed training volume even as your actual speed improves, so it tracks *dose*, not fitness. A race-day TSB around **+10 to +25** is a widely used target, but it's platform convention rather than a validated number — no trial has established an optimal TSB. Wearable VO 2 max estimates commonly err by **5-10%** against a lab test and can be worse for individuals at the extremes — good for spotting trends, useless as an absolute figure to compare against the thresholds above. Running-power meters (Stryd) predict critical speed reasonably well, though the specifics (one study attributing 63-69% of critical-speed variance to stance time and impact loading) come from small single studies rather than replicated work. **HRV and resting heart rate.** Meta-analyses ([Granero-Gallegos et al., 2020](https://pmc.ncbi.nlm.nih.gov/articles/PMC7663087/); [Düking et al., 2021](https://pubmed.ncbi.nlm.nih.gov/34489178/)) find HRV-guided training modestly outperforms fixed plans for submaximal fatigue markers (medium effect, fewer non-responders) — but only a small, often non-significant edge for VO 2 max or actual performance. Best practice is a standardized morning RMSSD reading, trended over roughly 7 days, rather than reacting to any single number. Watch pace at a controlled heart rate over weeks, not any single test — it's exactly the trend the [Runima app](https://runima.fit/) is built to surface as fitness changes over time. **Benchmark workouts** (coaching consensus, not directly RCT-validated): a tune-up half-marathon around 1:25; a long run with marathon-pace segments (say, 26-32 km with 16-24 km at 6:52/mile) held comfortably; threshold reps (3-6 × 1 mile at 6:25-6:35/mile) staying controlled; and a 20-24 km marathon-pace run that feels sustainable about 3 weeks out. ## The build, stage by stage **Stage 1 — Assess honestly, now.** If your recent half is slower than 1:30 or your 10K slower than 41:00, sub-3 isn't a current-cycle goal — build your base first. A half ≤1:27 or 10K ≤39:00 signals physiological readiness. Get an approximate VO 2 max (lab or watch trend): **~57-63 ml/kg/min** is the working floor for men, higher relative to bodyweight for women — but weight the race times far more heavily than the VO 2 max figure, which is both extrapolated and, from a watch, 5-10% off. **Stage 2 — Build volume safely (months 1-6+).** Progress toward **80-120 km/week** with roughly 80% easy running. Grow the long run gradually — the single strongest injury-risk lever in the RUNSAFE data is avoiding a session more than 10% longer than anything you've run in the past 30 days. Add heavy strength plus plyometric work twice a week for the economy payoff. **Stage 3 — Specific phase (final 12 weeks).** 1-2 quality sessions a week: threshold work at 6:25-6:35/mile, and marathon-pace long runs. Peak long run 30-32 km, including at least three runs ≥32 km. Practice fueling at 80-90 g/h on those long runs to train your gut before race day. **Stage 4 — Taper and race.** A 2-week exponential taper cutting volume 41-60% while keeping intensity and frequency (per Bosquet). Carb-load 10-12 g/kg/day for 36-48 hours. Aim for a TSB around +10 to +25 on race morning. Race with even, slightly conservative first-half pacing. Positive splits and going out too fast are a common cause of late-race slowing — model your split plan with the [Race Strategy Calculator](https://runima.fit/tools/race-strategy-calculator) before race day, not during it. **Thresholds that should change the plan:** if a tune-up half predicts worse than 3:02 four to six weeks out, adjust the goal or extend the build. If resting heart rate stays chronically elevated or RMSSD stays suppressed, cut the load. If you can hold a 24 km marathon-pace run comfortably about 3 weeks out, you're on track. ## How solid is each claim, really?
Topic Evidence quality Notes
Joyner model / big-three physiology Strong (peer-reviewed, replicated 2026) Doesn't capture durability or race execution
VO2max/LT/economy values for sub-3 Weak-moderate No cohort measured at 2:55; every threshold here is extrapolated, and estimates span 57-63
Race-time prediction (Riegel/VDOT) Weak for the marathon Riegel fitted to record performances; over-predicts, worst from short races
Volume → performance Strong (meta-regression), but observational Reverse causation likely; ignore the "90% of variance" Strava claim
Taper Strong (two meta-analyses) 2 weeks, -41-60% volume, keep intensity
Strength/plyometrics → economy Moderate (meta-analysis) Small-moderate effect, method-dependent
Carbohydrate strategy Strong Well-established nutrition science
Injury epidemiology Moderate-strong 10% weekly rule is weak; single-session spikes matter more
Intensity distribution optimum Moderate, contested "Mostly easy" is solid; the head-to-head trials are n=12 and n=120, with ~18% non-responders
Injury spike rule (RUNSAFE) Moderate Large cohort but observational, and not a clean dose-response
ACWR for injury management Discredited Mathematically flawed; no week-to-week ratio effect in RUNSAFE either
Timeline to sub-3 by persona Weak / indirect No direct studies; synthesized from adaptation science + consensus
HRV/CTL/wearable readiness Moderate for trends, weak for prediction Good for monitoring, not for forecasting marathon day
Sex & age differences Strong (large datasets) ~11-12% physiological gap; participation confounds the field gap
## The takeaway Sub-3 depends on several interacting demands: the same physiology as every other distance, tuned to a pace of 6:52/mile and a duration of 26.2 miles that leave little margin for deficiency in any one area. Aerobic capacity, sustainable threshold pace, and running economy all need to be strong enough on the same day, on top of ~80-120 km/week of mostly-easy volume, a real taper, and fueling you've actually rehearsed. What the evidence *can't* tell you yet is exactly how long your specific path will take — that part is still coaching judgment and consistency, not a peer-reviewed table. This builds on our [VO 2 max](https://runima.fit/blog/the-vo2max-trap), [lactate threshold](https://runima.fit/blog/lactate-threshold), and [running economy](https://runima.fit/blog/running-economy) trilogy — and on [training LT1 by heart rate, LT2 by pace](https://runima.fit/blog/training-lactate-thresholds) for the week-to-week execution. Know the standard. Build toward it deliberately. Then [track the trend](https://runima.fit/) that tells you which side of that 1-in-22 line you're actually on. --- ## References 1. [Joyner MJ (1991)](https://pubmed.ncbi.nlm.nih.gov/2022559/). *Modeling: optimal marathon performance on the basis of physiological factors.* J Appl Physiol. 70(2):683-687. 2. [Mougin L et al. (2026)](https://link.springer.com/article/10.1007/s40279-026-02439-y). *35 years of Joyner's endurance performance model: assessing the contribution of physiological determinants of performance proxies in 888 individuals from recreational to world class.* Sports Med. 3. [Gordon D et al. (2017)](https://pubmed.ncbi.nlm.nih.gov/29200895/). *Physiological and training characteristics of recreational marathon runners.* Open Access J Sports Med. 8:231-241. 4. [Myrkos A et al. (2020)](https://pmc.ncbi.nlm.nih.gov/articles/PMC7552741/). *Physiological and race pace characteristics of medium and low-level Athens marathon runners.* Sports (Basel). 8(9):116. 5. 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