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RaceProof Methodology

PTA Power Threshold Array

A multi-threshold measurement framework for evaluating fitness development across the full power-duration spectrum. Developed by Holden Comeau / Half Wheel.

From one threshold to seven

The history of power-based fitness measurement in endurance sport is a history of increasing resolution. Each generation improved on the last by capturing more of the power-duration relationship. PTA is the next step in that progression.

1965 → present
Critical Power
Monod and Scherrer established the hyperbolic power-duration relationship. Refined by Poole, Jones, and others. Two parameters describe the entire curve: CP (the power asymptote) and W' (the work capacity above CP).
Parameters: 2 (CP, W')
2008 → present
Power Duration Model
Coggan, Cusick, and TrainingPeaks expanded to three modeled parameters (Pmax, FRC, mFTP) and nine individualized training levels (iLevels) derived from the fitted curve shape. A continuous model with derived metrics.
Parameters: 3 + 9 iLevels
2026 →
Power Threshold Array
RaceProof identifies seven discrete bioenergetic transition points directly from performance data. Not modeled. Not derived. Measured at the individual's actual physiological inflection points, tracked over time.
Parameters: 7 (PT-N through PT-T)

Each step represents an expansion of resolution. CP reduces the power-duration relationship to two numbers. The Power Duration Model fits a continuous curve and extracts three parameters plus derived zones. PTA measures seven individually located transition points, each tracking a specific energy system, each shifting as the rider develops. The resolution increase from 2 to 3 to 7 is not arbitrary. It reflects the number of distinct bioenergetic transitions that are physiologically meaningful and detectable from performance data.


Why PTA requires racing data

The Critical Power model was developed from laboratory time-to-exhaustion protocols: a rider sustains a fixed power output until failure, repeated at 3-5 intensities on separate days. This produces clean data but under artificial conditions, with 3-5 data points per athlete.

The Power Duration Model improved on this by using field data from training files. But training data carries an inherent limitation: it reflects the prescription, not the physiology. If a coach prescribes 4×8 minutes at 95% of FTP, the data will show the rider's response to that specific stimulus, not what they could have produced across the full duration spectrum under unconstrained effort.

The racing data advantage
Racing produces fundamentally different data. In a competitive event, riders produce effort across the full power-duration spectrum under genuine stress, without prescribed constraints on duration or intensity. The competitive context demands near-maximal output at whatever duration the race dynamics require: a 6-second sprint to close a gap, a 90-second surge to bridge, a 5-minute threshold effort to stay in the group, a 20-minute sustained push to the finish.

The mean-max curve derived from racing data is a truer reflection of physiological capacity at every duration because the effort is self-selected under competitive pressure rather than prescribed by a training plan. This is what makes PTA detection possible. The transitions between energy systems produce detectable inflection points in the curve only when the data at each duration represents genuine maximal or near-maximal effort. Training data, constrained by prescription, often lacks the resolution at the specific durations where transitions occur. Racing data has it by nature.

The emergence of large-scale competitive cycling platforms created, for the first time in the history of exercise science, a dataset where hundreds of thousands of riders produce unconstrained maximal efforts across the full power-duration spectrum, multiple times per week, over years. PTA is a direct consequence of this data existing. It could not have been developed from laboratory protocols or training files alone.


The seven thresholds

PTA identifies seven bioenergetic transition thresholds: the specific durations where the power-duration curve inflects as one energy system depletes and the next takes over. These transition points are physiologically real, individually variable, and they shift as the rider develops.

Threshold
Energy system
Population range
What it measures
PT-N
PCr peak
3.9 – 5.2s
Neuromuscular sprint capacity
PT-S
PCr depletion onset
11.4 – 14.7s
Sprint sustainability
PT-G
Glycolytic activation
28.3 – 36.1s
Anaerobic capacity onset
PT-X
Glycolytic ceiling
54.2 – 71.8s
Maximal anaerobic output
PT-V
Aerobic transition
108 – 142s
VO2max onset
PT-A
Aerobic capacity
265 – 318s
Sustained aerobic output
PT-T
Threshold sustained
1080 – 1260s
Functional threshold, lactate equilibrium
The population ranges represent the observed distribution of transition offsets. A given rider's PT-G might fall at 31.2 seconds while another's falls at 34.8 seconds. Both are within the glycolytic activation zone, but the precise location reflects that rider's individual anaerobic physiology. PTA captures this variation. Conventional fixed time windows (5s, 30s, 1min, 5min, 20min) approximate these transitions but don't align precisely with any individual's actual transition points.

Two dimensions of progress

The precise location of each threshold is not just a fingerprint. It is a second axis of development. Traditional power measurement asks one question: did the watts go up at this duration? PTA asks two: did the watts go up, and did the threshold itself shift?

Consider a rider whose PT-A (aerobic capacity) sits at 278 seconds producing 310 watts. Three months later, that same rider's PT-A has shifted to 294 seconds, still producing 310 watts. By a fixed-window measurement at 300 seconds, nothing changed. By PTA, that rider's aerobic system is now sustaining dominance 16 seconds longer before the next energy system becomes the limiter. That is a real physiological adaptation, invisible to conventional measurement.

The same power, sustained for longer, is progress. More power at the same offset is also progress. The most complete development shows both: the threshold shifts outward and the power at the threshold increases. Two independent signals from each of the seven PTA points gives fourteen dimensions of development from a single profile, versus one number from FTP.

Why this matters for coaching
A rider who shows increasing power at PT-A but no outward shift is building capacity within their current aerobic window. A rider who shows outward shift at PT-A but stable power is extending the duration their aerobic system can dominate, a different adaptation with different training implications. A rider whose PT-G shifts inward (earlier glycolytic activation) while PT-A shifts outward is developing a gap between their anaerobic ceiling and aerobic floor, a pattern that often indicates the rider is training endurance at the expense of high-intensity repeatability. These are coaching-actionable distinctions that no single-number metric can detect.

How PTA detects transitions

PTA operates on the mean-max power curve: the best average power at every duration from a collection of rides. The curve is computed from 1Hz power data at variable-density resolution, with dense sampling in the bioenergetic transition zones where curve shape is most information-rich, and coarser sampling in the post-transition regions where the curve is smooth.

Transition points are identified as inflection points where the rate of power decline changes significantly, indicating one energy system's depletion and the next system's dominance. The detection algorithm locates each individual's specific offsets within the seven bioenergetic zones, producing a unique physiological fingerprint.

Illustrative PTA curve with threshold positions (single rider, 1Hz data, 90-day envelope)
The vertical dashed lines mark this rider's seven PT positions. Notice that PT-G falls at ~32s, not at the conventional 30s mark. That 2-second offset represents a measurable difference in when the glycolytic system activates for this specific individual. Tracking whether PT-G shifts outward (later activation, meaning the PCr system is sustaining longer) or inward (earlier activation) over time reveals one dimension of development. Tracking whether the power at PT-G increases or decreases reveals the second. Both are progress. Both are invisible to fixed-window measurement.

PTA value: the composite metric

PTA value is the unweighted average watt change across all seven thresholds from Q1 best to Q4 best within a measurement period. It answers a question that no single metric can: across the full power-duration spectrum, did this rider get stronger or weaker?

A rider with a PTA value of +3.2W improved by an average of 3.2 watts at each of the seven transition points. A rider at -18.0W declined by an average of 18 watts at each. The composite captures whole-curve movement that FTP, CP, or any single-duration metric misses entirely.

PTA value measures the power dimension of development. Combined with the positional dimension (how each threshold's time offset is shifting), the full PTA profile produces up to fourteen independent development signals from a single assessment, compared to one from FTP.

Why the composite matters
A rider who improves their PT-N (sprint) by 30 watts and their PT-A (aerobic capacity) by 15 watts while their PT-T (threshold) stays flat will show no FTP change. By that metric, nothing happened. By PTA value, they improved by +6.4W on average, with a development pattern indicating neuromuscular and aerobic expansion. That is a materially better bike racer who would be invisible to threshold-only tracking.

What PTA enables

Individual fingerprinting

Each rider's PT-N through PT-T positions and powers form a unique signature. Two riders at the same FTP may have completely different PTA profiles, reflecting different strengths, different energy system development, and different responses to training stimuli.

Directional development tracking

PTA detects whether a rider is expanding (improving at long-duration thresholds), sharpening (improving at short-duration thresholds), or developing uniformly across the curve. This informs training prescription with specificity that zone-based systems cannot provide.

Cross-source validation

When multiple power sources record the same effort, PTA curve shape comparison can detect measurement inconsistencies and establish trust scoring for data quality. The shape of the PTA curve is a more reliable indicator of consistency than absolute power values.

Population-scale analysis

The analysis in The Racing Effect applies the PTA framework to population-level event participation data. The seven thresholds are approximated from the best available discrete power peaks per event. This simplified application is sufficient to detect macro patterns (cohort-level development rates, seasonal erosion, dose-response relationships) while the full-resolution PTA methodology from 1Hz data enables individual-level precision.


PTA nomenclature

For clarity and consistency across RaceProof publications:

PTA refers to the methodology: Power Threshold Array.

PTA model refers to the analytical framework and detection algorithm.

PTA curve refers to the visualization of the seven threshold points and the power at each.

PTA value refers to the composite development metric (average watt change across all seven thresholds).

PT-N through PT-T refer to individual Power Thresholds. When referencing a single threshold, use PT (not PTA). The array is the collection; the individual point is a Power Threshold.

Power Threshold Array (PTA) is a proprietary methodology developed by Holden Comeau and Half Wheel, commercialized through RaceProof. © 2026 Half Wheel.