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.
Machines match free weights for gains
Hernández-Belmonte 2023 found no statistical difference between machine-only and barbell-only training on most athletic measures — barbells edged ahead on change-of-direction and balance.
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×(6×2) with intra-set rest) maintains mean concentric velocity across all 36 reps; traditional 3×12 sets decline within sets and cumulatively across sets.
Submaximal training wins long-term
Izquierdo-Gabarren 2010 — across 8 weeks, stopping sets short of failure (20% velocity loss) produced steady gains while training to failure (40% velocity loss) lost performance early and never caught up.
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 2017 — 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.
Deadlift mean velocity by load
Benavides-Ubric 2020 — mean concentric velocity at every 5 % of 1RM in the deadlift, from 50 resistance-trained men. The group profile with a ±1 SD band, and 0.33 ± 0.04 m/s on a true 1RM.
Feedback beats internal & external cues
Keller 2015 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.
Feedback dose scales adaptation
Keller 2014 — 6 weeks of drop-jump training, three groups by augmented-feedback frequency. Jump-height gain scaled with the dose: 100% feedback +14%, 50% +10%, 0% +6%.
Failure loses at every set count
Peterson 2005 (meta-analysis) — strength effect-size for not-to-failure conditions exceeded failure conditions at every set count, and the gap widened with more sets.
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.
Intended vs actual velocity
Behm & Sale 1993 — a limb strapped down so it couldn't move, training with the intent to move fast, gained just as much high-speed strength as the limb that actually moved fast.
Intent cueing vs traditional cueing
Rheese 2021 — 3 weeks of knee-extension training at a fixed 30°/s. Cueing high intended velocity produced no neural or performance advantage, and less than half the power gain of a steady-and-controlled cue.
Velocity loss thresholds by goal
The five velocity-loss bands, how hard each one makes a set, and what each is best for — 0–10 % for power and peaking, 10–20 % for quality volume, 20–30 % for strength and hypertrophy, 30–40 % for hypertrophy blocks, 40 %+ only when failure is the point.
Squat bar path
The squat bar should track a vertical through mid-foot. Side by side with the classic fault — hips shooting back out of the bottom, carrying the bar forward before the lifter drags it back to lockout.
Snatch bar path
What a good snatch trace looks like — the bar drifting back past the knees, the hips sending it up rather than out, and a tight turnover loop finishing behind the line it started on.
Power vs 1RM as a predictor
Loturco 2018 — bar power correlated more strongly with jump and sprint performance than 1RM squat on every test, and on the 5 m sprint the 1RM relationship was negative.
Power output by exercise
Baker 1995 — estimated power output at 100 % 1RM across five exercises for one 75 kg athlete. The hang pull and power clean produce 3–9 times the watts of the squat, deadlift and bench press.
Fatigue: max force vs peak RFD
D'Emanuele et al. 2021 — across four individual studies and 43 pooled strength studies, fatigue cuts peak rate of force development further than it cuts maximal force. Max force −8 % to −23 %, peak RFD −11 % to −30 %.
Bands vs chains for strength and power
Yan 2025 — a squat-only meta-analysis of 20 studies. Elastic bands drove maximal strength (g = 0.67) while chains did nothing; chains drove explosive power (g = 0.37) while bands did nothing.
Variable vs traditional resistance
Yan 2025 — a squat-only meta-analysis of 20 studies. Variable resistance beat constant resistance on strength, acute output and jumping, and did nothing for sprinting.
Velocity target → today's load
How a velocity target turns into a weight on the bar. Warmup sets build the day's load–velocity profile, a target band is laid across it, and the crossing drops to the load axis as the working weight.
Velocity target blocks (18 weeks)
An 18-week strength cycle written entirely in bar speeds. The target velocity steps down once per block; the working load climbs — and wobbles — because it is read off the athlete's profile every session rather than prescribed.
Power vs strength + power
Cormie 2007 — 12 weeks of jump squats, matched for total work. Adding 3×3 back squats at 90 % 1RM produced the same jump and power gains as power-only training, plus everything the power-only group failed to gain.
Power cycle anchor loads
A load–power curve marked with the three loads a 16-week power cycle works across — 92.5 % for overspeed, 100 % for max power, 107.5 % for overload — read as percentages of the load that produces peak watts.
Squat depth and power transfer
Rhea 2016 — 28 highly trained athletes, 16 weeks, identical programs bar squat depth. Quarter squats put 15 % on the vertical jump and 2 % on the 40; full squats put on 1 % and nothing.
Volume and hypertrophy
Pelland 2026 — 67 studies, 2,058 participants. Muscle growth rises with weekly sets across the whole range the literature covers, with diminishing returns and a credible interval that never touches zero.
Frequency and strength
Pelland 2026 — 67 studies, 2,058 participants. With weekly set volume controlled, strength gains rise with frequency and the credible interval never touches zero. Diminishing returns past two sessions.
Ballistic power vs heavy strength
Cormie 2010 — 24 relatively weak men, 10 weeks, jump squats at 0–30 % 1RM versus back squats at 75–90 % 1RM. Jump and sprint improved the same in both groups. Squat 1RM went up 31 % versus 4.5 %.
Strength vs power with age
Skelton 1994 — 50 healthy men aged 65–89. Across the age bands isometric strength falls to 71 % of the 65–69 value while leg extensor power falls to 38 %. Power declines at roughly double the rate.
RFD recovery after ACL
Angelozzi 2012 — 44 athletes tested before injury and at 6 and 12 months after ACL reconstruction. At 6 months maximal strength was back to 97 % of pre-injury while rate of force development sat at 80 % and 63 %.