Sony × Driftline
Sony tracks the ball, the broadcast, and the fan. TrueZone adds the athlete.
Hawk-Eye tracks where the ball goes. STATSports tracks where the players go. But neither tracks what it costs them physiologically. TrueZone extracts each athlete's aerobic capacity, endurance profile, and fatigue state from the heart rate data STATSports already captures—adding the physiological layer that connects Sony's entire sports technology stack.
Runway as a live substitution timer. Freshness as a fan-facing match graphic. PrimeScore as a season-long fitness number. Player archetypes as a content category. None of these products exist anywhere on the market today.
The Sony Sports Stack
Four platforms. One missing layer.
Sony has built the most complete sports technology ecosystem in the world—tracking, officiating, visualization, and digital platforms. TrueZone adds the physiological intelligence that connects them all. Player fitness data flows from STATSports vests into every downstream product.
Illustrative — Sony properties sit on top of the TrueZone physiology layer, fed by heart rate from STATSports vests.
GPS vests capturing 70+ real-time metrics from 800+ elite teams. Heart rate, speed, acceleration, distance. The raw physiological signal is already there.
Officiating and performance analysis across 23 of the top 25 global sports leagues. Tracks what happens on the field. TrueZone tracks what it costs the athletes.
Data-driven visualizations and fan engagement. Player physiology adds a new dimension — endurance profiles, fatigue state, and real-time metabolic cost visible to broadcasters and fans.
Powers the Premier League app, Cricket World Cup, and FC Barcelona OTT. Player fitness data and physiological insights could drive new content and engagement features.
STATSports + TrueZone
GPS vests measure load. TrueZone measures the athlete.
STATSports tells you a player covered 11.2 km, hit 34.8 km/h, and spent 23 minutes above 85% HRmax. What it cannot tell you: was that session easy or hard for that specific player? Is their aerobic system improving or declining? Are they speed-dominant or endurance-dominant?
TrueZone extracts three parameters per player from the same heart rate data the Apex pod already captures. No additional hardware. No lab tests. No disruption to the training week.
STATSports today vs. STATSports + TrueZone
| Metric | STATSports alone | + TrueZone |
|---|---|---|
| Player fitness | Inferred from load tolerance — not directly measured | Endurance (E) and Vmax profiled continuously from HR data |
| Training load | Total distance, PlayerLoad, high-speed running | Oxidative vs glycolytic load partitioned per player |
| Player archetypes | Position-based grouping | Speed-dominant vs endurance-dominant from E and Vmax |
| Fatigue detection | Acute:chronic workload ratio | Threshold drift — real-time physiological cost increase |
| Training zones | Fixed speed/HR bands for entire squad | Individualized HR zones per player based on actual thresholds |
| Return-to-play | Load progression benchmarks | PrimeScore trajectory session by session, with E and Vmax sub-tracks |
Player Archetypes
Same position. Different physiology. Different management.
Two wingers from the same squad. Both top out at ~29 km/h. But #4 has E = 0.61 (all-rounder) and #15 has E = 0.50 (sprinter). The first sustains threshold intensity for 75+ minutes. The second peaks in short bursts and fatigues faster. TrueZone identifies the taxonomy from routine vest data — no fitness tests, no questionnaires.
Elite Women's Football Season Analysis — 13 players, 309 sessions. Each dot is a real player; dashed lines mark the squad-relative medians that define the archetype quadrants.
Sprinter
High Vmax, lower E
Peaks in explosive efforts, fatigues at sustained pace. Higher glycolytic contribution per match minute. Needs longer recovery between high-intensity days.
Diesel
High E, moderate Vmax
Sustains threshold intensity 75+ minutes. Lower glycolytic load per match minute. Recovers faster between high-intensity bouts. Can handle higher weekly volume.
All-rounder
High E, high Vmax — rare
The complete physiological profile: sustains threshold intensity AND produces explosive efforts. The squad's most versatile players, capable of filling multiple tactical roles across a 90-minute match.
Load-sensitive
Lowest E relative to squad
Accumulates fatigue fastest during sustained efforts. Needs the most careful workload management — substitution timing, recovery protocols, and session load limits.
Beyond the dressing room
The same archetype labels become a fantasy-football axis (“how do this team's diesels fare in the final 15 minutes?”), a scouting taxonomy (“endurance-dominant winger between £8–12M”), and a season-long broadcast narrative — not just a coaching tool.
Beyond the Coaching Staff
Player physiology as content.
Sony's sports stack doesn't just serve coaches — it serves broadcasters, leagues, and fans. Player physiology opens new possibilities across every platform.
Four metrics, built for the screen
Runway
Minutes a player can hold their last 5-minute pace before freshness crosses threshold. Already smoothed, reads in clock units — the first physiologically grounded substitution timer.
Freshness
Starts at 100% and ticks down through the match. Diesels hold above 80% deep into the second half; load-sensitive players dip below 50% by the hour. A real-time gauge fans read instantly.
PrimeScore
A single age- and sex-normalized fitness number, 0–10. The format ESPN, Sky, and league apps already use for expected goals or shot quality — but for the player, not the play.
Player archetype
Each player classified Sprinter / Diesel / All-rounder / Load-sensitive from their fitted E and Vmax. A fantasy axis, a scouting taxonomy, and a season-long content series in one label.
On the screen
Illustrative — physiological freshness rendered as a Hawk-Eye-style broadcast overlay. 64th minute, KR × Arsenal. #7 just dropped below 40% freshness with 4 minutes of runway left — the director's substitution cue is already armed.
Where it lives
Broadcast
Live freshness and runway overlays during matches. “Player X is down to 52% freshness with 8 minutes of runway at current pace — expect a substitution before the hour.” A new storytelling layer for commentators, powered by Hawk-Eye + TrueZone.
Fan engagement
Beyond Sports visualizations enriched with player endurance profiles and PrimeScore. Fans see not just what players did, but how fit they are and how much they had left. New content for Pulselive apps and league platforms.
Fantasy and betting
Endurance trends and fatigue data as inputs for performance prediction. A player whose E has dropped 8% over the congested fixture period is a different proposition than their season average suggests.
On player data rights
Physiological data is special-category data under GDPR, and the commercial uses above sit within emerging consent and revenue-share frameworks being shaped by FIFPRO and league players' unions. Driftline is positioning to co-design these structures with player representatives rather than retrofit around them — the applications above are deployable inside properly-consented data architectures, not bolted on top.
Working with KR football club
A complete physiological profile from a single training session.
One routine training session. No lab tests, no questionnaires, no protocol changes. The model lands a player's full E / Vmax / P profile in minutes — and that profile reproduces the HR curve of the session it was fit on, drift and all, across the entire 57-minute training.
The fit below comes from one of KR's first-team starters (anonymised). It is the kind of profile coaches today need a lab or weeks of monitoring to build — and we get it from a single normal session.
Player A · KR first team · 57 min full training
Actual HR vs. model prediction over one session
Aerobic dominance. Mid-to-high for a starter — sustains threshold pace well, leans diesel.
Extracted from the model's response to submaximal efforts — the player does not need to actually sprint to Vmax for us to estimate it. Observed sprints only act as a lower bound.
Inferred from the HR-drift dynamics across the session — the player never needs to hit max HR. Observed peaks only act as a lower bound on the estimate.
Median session error for football — close to the physiological ceiling given match-style HR variability.
What this means at scale
Every player on a STATSports-equipped roster carries the sensors needed for this fit. Driftline reads the HR stream they already capture. A first-team profile that today takes weeks of monitoring or a lab visit lands after a single session — and refines further as more sessions arrive.
Match day — without an HR strap
Real-time physiology, even when the player doesn't wear an HR monitor in the match.
Many players don't want a chest strap during competitive matches — it's uncomfortable, sometimes against league rules, and easy to forget. The trade-off used to be: skip the strap, lose the physiology read. We close that gap.
Because Player A's E / Vmax / P profile is already fitted from his training data, all the live physiology — predicted HR, Freshness, Runway — derives from his GPS speed alone during this real top-league match. No strap. No estimate of HR from RR. Just the model running on the data the vests already capture.
Player A · KR top-league match · 2026 season
Match-day Freshness — GPS only, no HR strap
Freshness % over the full match — fresh (mint), fading (amber), gassed (coral). Grey band marks the half-time break; you can see the visible bounce as the player refuels.
Held just above the 50% director-cue threshold all the way through — recovered visibly at half-time, then steady decline through the second half.
Projected match-pace minutes remaining when the final whistle blew — the model says he had another sub's worth of effort in the tank.
What the broadcast / coach sees in real time
Every minute of this match produced a live HR estimate, Freshness %, and Runway — all derived from the GPS speed stream alone, against a profile fit from a single prior training session. The director sees the player's physiological budget in real time without the strap tightening across his chest at kickoff.
Why archetype matters
Every player has their own speed-duration curve.
A Sprinter and a Diesel can have the same average pace over 90 minutes and look identical on a stat sheet — but they live in completely different physiological worlds. One has a huge top end and a short fuse; the other holds elevated pace for an hour. The model captures this with two numbers per player — Vmax and Endurance — that together define the curve below.
We then read every speed sample in context of that curve. 23 km/h is comfortable cruise for a Diesel and a near-max effort for a Sprinter — same number, totally different physiological cost. That's how Runway, Freshness, and the broadcast chips become meaningful instead of one-size- fits-all.
Two football archetypes · same league, same position
How long can each player sustain a given speed?
Each curve is the maximum speed the player can sustain for a given duration. The dots mark V2 — the lab-style 2-hour anchor. The dashed horizontals are the FIFA / UEFA speed zones. The Sprinter dominates the HSR and Sprint bands; the Diesel dominates everything from comfortable running down.
Vmax 35 km/h, Endurance 0.45. Reaches 30 km/h, but can only hold it for seconds. Drops out of HSR within minutes of continuous pressure. Manage him in short, explosive bursts — Runway burns out fast, Freshness recovers slower.
Vmax 31 km/h, Endurance 0.82. Lower ceiling, but he'll still be running at 22 km/h after an hour when the Sprinter is walking. Bigger Runway, slower fatigue accrual, the player you keep on for the full ninety.
Validation
Validated in the environments that matter.
The heart rate model has been validated across 28,516 sessions spanning running, cycling, and team sports. Team sports validation uses the SoccerMon dataset — 684 sessions from elite women's football with GPS and heart rate data matching the format STATSports Apex produces.
Team sport sessions (SoccerMon)
Elite women's football. GPS + heart rate data matching STATSports vest format. Player profiles emerge from match and training data.
Total sessions validated
Five independent datasets across lab studies, running, cycling, and team sports. 815 individual athletes.
VO²max vs gold-standard CPET
Validated against cardiopulmonary exercise testing at the University of Iceland. No significant difference (p > 0.05).
Sessions to convergence
Parameters stabilize quickly from normal training. Production-quality player profiles from routine sessions.
Integration
Vest data in. Physiology across the stack.
TrueZone integrates via C++ SDK or REST API. Input is heart rate time series from STATSports Apex pods. Output feeds coaching dashboards, broadcast overlays, fan platforms, and performance analytics. Patent-protected to 2045.
STATSports vest data flows in
Heart rate time series from Apex GPS pods. The same data STATSports already captures — no additional sensors, no protocol changes, no player burden.
TrueZone profiles each player
Bayesian fitting extracts E, Vmax, and P per player. Parameters converge within 3–10 sessions from normal training. Updated continuously.
Physiology across the Sony stack
Player profiles feed into STATSports dashboards, Hawk-Eye performance overlays, Beyond Sports visualizations, and Pulselive fan-facing content.
Sony owns the complete sports technology stack. TrueZone adds the layer that makes it physiological.
800+ elite teams already wear STATSports vests. The data is there. The physiology is waiting to be read.