Guides

Team Sports

Sprint detection, player archetypes, PrimeScore, and squad analytics.

Set "sport": "team_sports" in settings. Optionally provide squadMeanE and squadMeanVmax for archetype classification.

Team sports outputs

"team_sports": {
  "sprint_count": 12,
  "sprint_threshold_mps": 5.6,
  "max_speed_mps": 7.0,
  "distance_m": 8500,
  "primescore": 6.2,
  "archetype": "Speed-dominant"
}

Sprint detection

Sprints are detected when speed exceeds 80% of the session's max speed. A sprint ends when speed drops below 56% of max (70% of the sprint threshold). This captures genuine high-speed efforts while filtering out brief accelerations.

Player archetypes

When squadMeanE and squadMeanVmax are provided, the SDK classifies the player relative to the squad:

ArchetypeCriteriaProfile
Speed-dominantVmax well above squad meanHighest top speeds, sprint specialists
Endurance-dominantE well above squad meanSustain effort, box-to-box players
Load-sensitiveE well below squad meanFatigue fastest, need careful load management
All-rounderNear squad averagesBalanced, versatile across roles

Settings for archetype classification

{
  "sport": "team_sports",
  "squadMeanE": 0.62,
  "squadMeanVmax": 8.5
}

squadMeanVmax is in m/s. Compute it as the average of trueVmax across all players in the squad.

Training load partitioning

The training_load section (included for all sports) partitions load into oxidative and glycolytic components:

  • HRD oxidative: Aerobic stress (zones 1–2)
  • HRD glycolytic: Anaerobic stress (zones 3–5)
  • Glycolytic ratio: What fraction of the session was glycolytic
  • Load_TZ: Weighted total (glycolytic stress × 2)

This tells coaches not just how hard a session was, but what metabolic systems it stressed — critical for periodization and recovery planning.

Cardiac drift in team sports

Drift detection works on intermittent activities despite variable speed. The debt accumulation model tracks fatigue through the full session, capturing the cumulative glycolytic stress even across rest periods.

Use case: squad monitoring

To monitor a full squad, analyze each player's sessions individually (passing their personal memory for multi-session learning), then aggregate:

  1. Per-player E, Vmax, PrimeScore trends over the season
  2. Squad-level archetype distribution
  3. Per-session load comparison across players
  4. Drift and recovery metrics for substitution timing
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