What GPS vests miss about your players

Walk into any professional football, rugby, or AFL training session and you will see the same thing: players wearing GPS tracking vests. Catapult, STATSports, Polar Team Pro — the brand varies, but the premise is identical. A small unit between the shoulder blades records position, speed, and acceleration dozens of times per second. After the session, analysts download the data and produce dashboards full of total distance, high-speed running meters, sprint counts, acceleration loads, and various composite metrics.

This is now standard practice from the English Premier League down to semi-professional clubs. The hardware works. The data is accurate. The problem is what gets done with it — or more precisely, what doesn't get done with it.

The external load paradigm

GPS vests are fundamentally instruments of external load. They tell you what a player did: how far they ran, how fast they went, how many times they changed direction. This is valuable information. It lets you compare sessions, monitor weekly training loads, and flag large spikes in volume that might increase injury risk.

But external load answers only half the question. The other half — the one that actually determines whether a player adapts, fatigues, or breaks down — is internal load. Internal load is the physiological cost of the work. It is what happened inside the player's body while they were covering those meters and hitting those speeds.

Here is the core issue: the same external load imposes a completely different internal cost on different players.

The 11-kilometer problem

Consider two midfielders on the same team. Both cover 11 km during a match. Both log similar high-speed running totals. On the post-match report, they look equivalent. But one of them has high aerobic endurance — a large proportion of slow oxidative muscle fibers, strong fat-oxidation capacity, efficient oxygen delivery. For this player, 11 km at match intensity is well within their aerobic range. They finish the game tired but intact. Recovery takes 48 hours.

The other midfielder has lower aerobic endurance. Same maximum speed, similar body composition, but a different muscle fiber profile. For this player, the same 11 km at the same speeds pushes them well above their endurance threshold for extended periods. They accumulate more glycolytic fatigue, deplete glycogen stores faster, and generate more metabolic stress. They finish the game exhausted. Recovery takes 72 hours or more.

The GPS vest saw two identical performances. The players experienced two fundamentally different games.

This is not a hypothetical scenario. It plays out on every pitch, in every match, across every team sport. And currently, almost nobody is measuring it.

What the heart rate data already contains

Here is the irony: the data needed to assess internal physiological load is already being collected. Most GPS vests include a heart rate sensor, either built in or via a paired chest strap. Heart rate is recorded alongside speed and position at every timestamp. The streams are synchronized and available in the same export file.

Yet in most professional environments, heart rate data from vests is either ignored entirely or reduced to crude summaries — average heart rate, time in generic heart rate "zones," or a session RPE correlation. The rich, time-series relationship between speed and heart rate goes unanalysed.

This relationship is where the physiology lives.

Three parameters that define a player

TrueZone extracts three physiological parameters from standard heart rate and speed data, using the same recordings that GPS vests already produce:

• E (Endurance) — a value between 0 and 1 reflecting the player's aerobic endurance capacity. This is determined by the proportion and oxidative capacity of slow-twitch muscle fibers. It governs where the player's exercise thresholds sit on the intensity scale. A player with E = 0.80 has tightly spaced thresholds high on the intensity range — they can sustain high speeds aerobically. A player with E = 0.40 has widely spaced thresholds and transitions to anaerobic metabolism at much lower relative intensities.

• Vmax (Maximum speed) — the player's functional maximum speed, extracted from the heart rate response rather than from raw GPS peak speed (which is noisy and context-dependent). This represents the upper bound of the player's speed-intensity scale.

• P (Power) — a composite parameter reflecting the player's overall power output capacity, influenced by muscle mass, body size, and anaerobic contribution.

From these three parameters, five exercise thresholds are calculated for each player. These thresholds are not generic percentage zones — they are individual physiological transition points derived from that player's data.

Player archetypes

Once you have E, Vmax, and P for every player on a squad, patterns emerge. Players cluster into recognizable archetypes:

Endurance-dominant players (high E, moderate Vmax) sustain high work rates across 90 minutes with minimal drift. They are your box-to-box midfielders and pressing forwards who can maintain intensity deep into the second half. Their heart rate stays relatively stable at submaximal speeds because they are operating within their aerobic capacity.

Speed-dominant players (high Vmax, lower E) produce explosive outputs but at a higher physiological cost. They need more recovery between sprints and are more susceptible to fatigue-related performance drops. Their heart rate climbs steeply and drifts upward during sustained efforts.

Power players (high P, variable E and Vmax) generate large forces relative to their body size. They are effective in short, high-intensity actions — tackles, aerial duels, accelerations from standing — but may not sustain repeated efforts efficiently.

All-rounders show balanced profiles across all three parameters. They are versatile but may not excel at the extremes of either endurance or speed.

These archetypes are not labels imposed from outside. They emerge directly from the data. And they have immediate tactical implications: which players can handle a high-press system for 90 minutes, which need to be substituted by the 70th minute, which combinations create mismatches in specific phases of play.

Cardiac drift as a fatigue signal

One of the most practically useful outputs of physiological monitoring is cardiac drift — the gradual upward creep of heart rate at a constant workload. In a match context, this shows up as a player maintaining the same running speed but with a progressively higher heart rate over time.

Cardiac drift is a well-established physiological phenomenon driven by dehydration, core temperature rise, glycogen depletion, and central fatigue. What makes it powerful in a team sport context is that TrueZone can detect drift patterns relative to the player's individual model. A 5 bpm drift in a high-endurance player might be normal. The same drift in a low-endurance player might indicate they are approaching a critical fatigue threshold.

When drift patterns change between sessions — say, a player who normally shows minimal drift starts showing early and pronounced drift — that is an early warning signal. It may indicate under-recovery, illness, accumulated fatigue, or overtraining. This kind of signal appears in the heart rate data days before it shows up in subjective wellness questionnaires or performance test scores.

Practical applications

The applications span the full range of performance management in professional sport:

Pre-match selection. If two players are competing for the same position, their physiological profiles can inform which one is better suited to the tactical demands of a specific match. A high-press game against a possession-heavy opponent favors the endurance-dominant player.

Training periodisation. Knowing each player's physiological profile allows coaches to individualize training loads within team sessions. The player with E = 0.45 needs different recovery protocols than the player with E = 0.80, even when they perform identical drills.

Return-to-play. After injury, tracking the recovery of E, Vmax, and P gives medical staff objective markers of physiological readiness. A player may pass physical performance tests (speed, agility, strength) while still showing depressed endurance values — indicating they are not yet ready for full match intensity.

Talent identification. In academy and recruitment settings, physiological profiling adds a layer of assessment beyond technical skill and physical attributes. Two young players with identical sprint times but different endurance profiles have different long-term development trajectories.

Validation

This is not a theoretical framework. TrueZone's team sport model was validated on 13 elite women's football players over a full 6-month competitive season, comprising 684 training and match sessions. The model parameters were extracted from standard GPS vest and heart rate data without any additional hardware, lab testing, or invasive measurement.

The model converges within 5 to 10 sessions per player. After convergence, individual threshold positions and physiological profiles remain stable over weeks to months, shifting only in response to genuine changes in fitness — which is exactly what you want from a monitoring tool.

No new hardware

The most important practical point is this: TrueZone does not require teams to buy new equipment. It works with the data that GPS vests already collect. The heart rate and speed streams that are currently being underutilised contain enough information to build individual physiological models for every player on the squad.

The vest data is already there. The heart rate recordings are already there. The physiological insight has been hiding in plain sight — waiting to be extracted.