The chart library
Every chart we use across blogs, calculators, and the book — authored once as code, theme-aware, free to download. Each entry has a permalink, a high-resolution PNG, and a list of where it appears.
Load–velocity profile
The load-vs-speed function for a given lift and athlete. Plot a few sub-maximal sets and you can read 1RM from the line, compare lifts side-by-side, and see why a single percentage of 1RM lands different athletes in different velocity zones.
Bar velocity drops across a set
Per-rep velocity loss for a single working set. The cutoff line marks where the set should end.
Velocity-time graph
Bar velocity across a whole set of five reps. Each rep is a concentric spike above zero and an eccentric dip below it — the raw signal every velocity metric is calculated from.
Acceleration-time graph
The acceleration of the bar across the same five-rep set. Because acceleration is a rate of change, it spikes hard at every turnaround — the reason peak-based metrics are so sensitive to noise.
Force–velocity curve
The hyperbolic relationship between contractile force and shortening velocity. Theoretical, derived from isolated-muscle physiology — distinct from the load–velocity profile.
Anatomy of a rep
The velocity-time trace of a single rep, with the three ways to measure it drawn on: peak velocity (the fastest instant), mean velocity (average of the whole concentric), and propulsive velocity (concentric up to the point of deceleration).
RPE × reps table
Percentage of 1RM at every RPE × rep combination. Coaches use it forward (load → effort) and backward (effort → load), in both directions every session.
Deceleration ratio table
The share of the concentric spent actively decelerating the bar, by load. It falls from 28 % at 20 % 1RM to zero at ~80 % 1RM — the point where propulsive and mean velocity become identical.
Bryan Mann's 5 velocity zones
The canonical 5-zone velocity model. Mean concentric bar speed maps to a dominant training quality across the 0.00–2.00 m/s range.
Load–power profile
Mechanical power output across the working load range, plotted in watts. The parabolic shape peaks at an intermediate load — typically 30–50 % 1RM for the squat.
Maximum-power profile
A load–power profile with the apex called out — a horizontal dashed line at peak power in watts and a vertical dashed line at the load that produces it, meeting at the maximum-power point.
Minimum velocity threshold by lift
Minimum velocity threshold values for back squat, front squat, bench, all three deadlifts, barbell row, and overhead press — by training level (novice / elite) and by effort tier (max out / tough / moderate).
20% velocity loss maximises strength
Pareja-Blanco 2017 — squat 1RM gains scale with the velocity-loss cap inside each set. Strength response peaks around 20 % v-loss, then drops as fatigue overruns adaptation.
VBTcoach 3-zone model
A simplified velocity-zone model defined on the % 1RM axis. Three load bands — Speed, Power, Strength — instead of Mann's five velocity-axis zones.
VBT has better results than %s
Vasiljevic 2024 — velocity-based training out-performed percentage-based on every test, including 1RM squat, 1RM bench, squat jump, and countermovement jump.
Bar-speed feedback boosts performance
Randell 2011 — pro rugby players who saw real-time velocity feedback during jump-squat training out-gained the no-feedback group on every transfer test.
Henneman size principle
Motor units are recruited smallest-first, largest-last. Three logistic curves show how force production and motor-unit size climb as demand rises — and why only maximal intent recruits the high-threshold units.
Cluster sets boost power gains
Morales-Artacho 2018 — cluster sets out-gained traditional 6×6 sets at every load tested (25 / 50 / 75 % 1RM), with the biggest gap at the peak-power region around 25 % 1RM.
Cluster sets boost strength gains
Akhil Samson 2018 — cluster sets out-performed traditional sets on every compound lift tested over 8 weeks — bench, shoulder, row, sumo squat, back squat, calf raise.
Cluster sets sustain bar speed
Tufano 2016 — cluster set training (3×5×2 with intra-set rest) maintains mean concentric velocity across all 36 reps; traditional 3×12 sets decline within sets and cumulatively across sets.
Training to failure slows jump recovery
Gonzalez-Badillo 2016 — jump performance crashed 44 % immediately after a higher-effort squat workout (3×8) and stayed depressed for 48 hours; the lower-effort 3×4 group bounced back inside 6 hours.
Lower velocity loss, better gains
Pareja-Blanco 2016 — training to 20 % velocity loss out-gained 40 % on 1RM, bar velocity, jump, and type-II muscle fibres, while doing significantly less total volume.
Back squat 1RM fluctuates daily
Zourdos 2016 — three trained powerlifters tested daily for 36 days. Day-to-day variation runs ± 3-5 % from the previous day's reading, even with no programmed change in load.
Feedback beats internal & external cues
Keller 2014 measured two outcomes from the same three-condition study — acute jump output and within-set fatigue. Augmented feedback won both — ~4× more acute improvement than the best verbal cue, plus an inverted within-set fatigue curve.
Velocity feedback boosts transfer
Weakley 2019 — 4 weeks of augmented velocity feedback in rugby union players. Feedback group beat the no-feedback group on every test, including a peak-power loss the no-feedback group couldn't avoid.
Prilepin's chart
The canonical reps × intensity × session-volume table from Soviet weightlifting research. For each load band, the prescribed reps per set, optimal session total, and acceptable total range.
VBT-adjusted loads beat fixed loads
Muñoz de la Cruz 2023 — six weeks of resistance training with daily VBT-adjusted loads out-gained a fixed-load prescription on every outcome, including strength, jumps, and 30 m sprint metrics.
Individualised VBT beats group loads
Dorrell 2020 — six weeks of VBT, with one group prescribed loads from a shared group-mean profile and the other from each athlete's own load-velocity profile. The individualised group out-gained on every measure.
Reliability vs validity
The classic 2×2 target illustration. Validity is hitting the bullseye; reliability is grouping tightly. For day-to-day velocity-based training, a tight group in the wrong spot beats a loose scatter around the right one.
Load–velocity vs force–velocity curve
The load–velocity profile is the practical, lift-specific line you measure in the gym. The force–velocity curve is the theoretical Hill hyperbola from in-vitro muscle physiology. Plotted on the same axes, they don't match — and that mismatch is the point.
Load–velocity and power curves
Linear LV profile (descending) and parabolic power curve (peaking mid-load) overlaid on the same load axis, dual y-axes. Shows why peak power lives between heavy strength loads and light speed loads.
How cluster sets break up a set
Four cluster-set protocols (4×5, 7×3, 10×2, 20×1) drawn to scale on a 10-minute session timeline. All four equate to ~20 reps at the same %1RM but distribute them very differently.
RPE conversion chart
All four common effort languages on one chart — RPE 5.5–10, RIR 5–0, velocity loss 5–45 %, last-rep velocity 0.52–0.25 m/s. Drop a finger on any row to read across.