LiftGauge FAQ — DOTS, 1RM formulas, powerlifting percentiles, and the data behind them.

Q1

What is a good DOTS score?

DOTS (Konertz / BVDK 2019) replaced Wilks as the IPF raw-open coefficient from 2020 because Wilks was shown to skew systematically toward lighter lifters (Vanderburgh & Batterham 1999, PMID 10613442). The formula takes competition total in kilograms, bodyweight in kilograms, and a sex-specific polynomial; the resulting scalar lets two lifters of different bodyweight be ranked against each other.

The table below shows the actual DOTS distribution for male and female raw lifters at age 30 in the OpenPowerlifting public dataset (snapshot 2026-04). For male 83 kg raw lifters around 395 reads as intermediate club lifter; above 500 is national-class. For female 63 kg raw lifters around 350 reads as intermediate; above 450 is national-class.

The interactive DOTS calculator is at scores, where DOTS is reported alongside Wilks 2020 and IPF GoodLift for the same total.

Q2

How accurate is the Epley formula versus Brzycki?

Both are polynomial approximations to a theoretical 1RM from submaximal load and reps. Epley (1985) is linear in reps and produces a higher estimate at moderate-to-high reps; Brzycki (1993) is hyperbolic and converges to a hard ceiling near 12 reps. Neither is "correct" in isolation — the underlying load-rep relationship is a curve that drifts with the lifter's training age, exercise selection, and effort calibration.

LiftGauge computes all seven common formulas in parallel — Epley, Brzycki, Mayhew (PMID 1614532), Lombardi, O'Conner, Wathan, and Lander — and surfaces the highest-vs-lowest spread as a confidence band rather than picking one. Divergence between formulas widens above 10 reps; at 1–3 reps they cluster within roughly 2 percent of each other.

The comparative validation literature (LeSuer et al. 1997, Southern Illinois University) tested several of these against direct 1RM measurement and found no single formula dominant across all rep ranges or all three powerlifting exercises. The seven estimates are visible in the calculator view.

Q3

What dataset powers the percentile ranking?

Competition cohort. The OpenPowerlifting public dataset (CC0 1.0, openpowerlifting.org, snapshot 2026-04, ~141,000 raw-lifter records). Segmented by sex, per-year age 15–80, IPF bodyweight class, and equipment (raw, single-ply, multi-ply). Exact cohort cells are used when n ≥ 30; below that threshold the system widens hierarchically and shows the widening explicitly in the readout.

Recreational cohort. StrengthLevel self-reported gym-log aggregates, 153M+ entries. The bodyweight-axis table and the age-axis table are combined multiplicatively. No calibration factor is applied — these are presented as self-reported recreational data, not validated meet records.

General-population cohort. A constructed proxy: the ACFT 3-rep hex-bar deadlift converted to a conventional barbell estimate via a 0.92 factor (PMID 28151780, the ACFT validation literature), with squat and bench-press values derived from published lift ratios (NSCA Essentials of Strength Training and Conditioning; ACSM resistance-training guidelines). The age curve is drawn from NHANES 2011–2014 grip strength as a multi-joint strength proxy.

Full cohort-cell widening rules live in rankings and the method reference in method.

Q4

How is a hex bar deadlift converted to a barbell deadlift?

The hex bar's centered load path puts the resistance vector through the lifter's centre of mass rather than in front of it, which shortens the lever arm at the hip and reduces the spinal-erector contribution. At comparable loads the hex bar reads as approximately 8 percent easier than a straight bar; the conversion factor used in this tool is therefore 0.92.

To convert, multiply the hex-bar load by 0.92 to obtain a barbell-equivalent estimate. Source: Swinton et al., ACFT validation literature (PMID 28151780). The factor is applied only in the general-population comparison mode, where the ACFT hex-bar deadlift is the available proxy for barbell strength.

Q5

What is the difference between Wilks, DOTS, and IPF GL?

Each of the three coefficients normalises a competition total for bodyweight and sex; IPF GL additionally accounts for equipment category. Wilks (Wilks 2017/2020 revision) was the IPF standard through 2019 and is still cited in older records. DOTS (Konertz / BVDK 2019) replaced Wilks for IPF raw open from 2020. IPF GL — the Goodlift formula, derived from the Schwartz / Malone tradition and re-fit on IPF meet data — is the IPF-sanctioned coefficient for all equipment categories from 2020.

LiftGauge computes all three from a typed competition total, bodyweight, and sex and displays them in parallel because the same total ranks differently in each. See the scores view for the comparative readout.

Q6

What does percentile mean in this context?

Percentile N means N percent of lifters in the comparison cohort recorded a lower value. The cohort is filtered first — by sex, equipment category, and bodyweight class in the powerlifting and recreational modes; by sex and age in the general-population mode — and the percentile is computed against the filtered subset, not against the dataset as a whole.

When the exact cohort cell is sparse (n < 30 is the threshold used here), the system widens hierarchically: exact bodyweight class at the given age → all ages in that bodyweight class → ±1 bodyweight class → ±2 bodyweight classes → any bodyweight. The widening is shown in the readout so that the visitor sees which comparison pool produced the percentile. Sparse extreme-weight cohorts are the reason this hierarchy exists.

Q7

What is Prilepin's table?

A. S. Prilepin was a Soviet sports scientist who, in the 1970s, tabulated recommended total rep counts per training session at a given percentage of 1RM, derived from observation of elite Soviet weightlifters. The table encodes an optimal lift count, a range, and an intensity zone for each percentage band — a prescription for how much volume is productive at a given load.

The English translation in standard circulation is Andrew Charniga's edition of A. S. Medvedyev, A System of Multi-Year Training in Weightlifting (Sportivny Press, 1986). The calculator view highlights the active Prilepin zone given the current load as a percentage of 1RM and reports the recommended rep range; the full table is in method.

Q8

How is TDEE calculated in the nutrition view?

Total daily energy expenditure is computed with Mifflin-St Jeor (1990, PMID 2305711), which takes sex, age, height, and weight and returns resting metabolic rate; that rate is then multiplied by a category-based activity factor (sedentary, light, moderate, active, very active).

Beyond a standard TDEE calculator, the nutrition view's STRENGTH tab models how much strength is retained over 52 weeks at a given caloric deficit, anchored on Helms et al. 2014 (PMID 24864135) and Murphy & Koehler 2022. This couples the calorie target to the lift numbers entered elsewhere in the tool. See nutrition for the interactive view.

Q9

What is an SNS score?

SNS is a composite readiness metric — Sleep, Nutrition, Stress — sometimes labelled SRQ (Subjective Readiness Questionnaire) in the sports-science literature on athlete monitoring. The PLAN tab takes a readiness input from the visitor and uses it to modify the training-day calorie recommendation.

The implementation in this tool is a simplified composite, not a validated clinical instrument; it sits in the general category of session-RPE and subjective monitoring rather than in any specific validated questionnaire. Treat it as a directional input, not a diagnostic.

Q10

Is the app available offline?

Yes. LiftGauge is a progressive web application (PWA) built with Workbox. After the first load, the app shell and all calculation logic — the seven 1RM formulas, the DOTS / Wilks / IPF GL coefficients, the plate-loading, the warmup ramp, the Prilepin readout, and the Mifflin-St Jeor TDEE — run without a network connection.

The OpenPowerlifting cohort file (~2.8 MB) is precached on first load, so the percentile-ranking view also functions offline once the initial fetch has completed. Only the very first visit requires connectivity; subsequent visits and calculations run from the local cache.