endurance-metrics 0.1.0

Training metrics for endurance sports: CTL/ATL/TSB, ACWR, decoupling, and more

endurance-metrics

Training metrics for endurance sports: CTL/ATL/TSB, ACWR, decoupling, and more.

Zero external dependencies — uses Python standard library only.

Installation

pip install endurance-metrics

Quick start

from endurance_metrics import compute_ctl_atl_tsb, calculate_acwr

# Compute fitness/fatigue/form from daily training loads
activities = [
    {"date": "2026-01-01", "load": 80},
    {"date": "2026-01-02", "load": 65},
    {"date": "2026-01-03", "load": 0},
    {"date": "2026-01-04", "load": 90},
]
ctl, atl, tsb = compute_ctl_atl_tsb(activities)
print(f"CTL={ctl:.1f}, ATL={atl:.1f}, TSB={tsb:.1f}")

# Compute Acute:Chronic Workload Ratio
acwr = calculate_acwr(activities)
print(f"ACWR={acwr:.2f}")

Features

Fitness metrics (endurance_metrics.fitness)

• compute_ctl_atl_tsb() — Chronic/Acute Training Load and Training Stress Balance
• compute_ctl_history() — CTL progression over time
• compute_atl_history() — ATL progression over time
• compute_tsb_history() — TSB progression over time
• compute_fitness_summary() — Complete fitness summary
• compute_form_trend() — Form trend analysis

Advanced metrics (endurance_metrics.advanced)

• compute_ramp_rate() — CTL ramp rate (fitness loading speed)
• compute_fitness_trend() — Multi-period fitness trend
• compute_peak_fitness() — Peak CTL identification
• detect_overtraining_risk() — Overtraining risk detection
• compute_training_zones_distribution() — Training zones distribution
• compute_periodization_score() — Periodization quality score

Workload metrics (endurance_metrics.workload)

• calculate_acwr() — Acute:Chronic Workload Ratio
• calculate_monotony_strain() — Training monotony and strain

Decoupling analysis (endurance_metrics.decoupling)

• calculate_decoupling() — Effort:Cardio decoupling percentage
• analyze_overtime() — Detailed overtime analysis with pacing assessment
• compute_normalized_power() — Normalized Power (cycling)

Decoupling supports multiple effort types:

from endurance_metrics import calculate_decoupling

# Cycling (power-based, uses Normalized Power)
dec = calculate_decoupling(watts, hr, effort_type="power")

# Running (pace-based, uses rolling average)
dec = calculate_decoupling(pace, hr, effort_type="pace")

# Pre-normalized data
dec = calculate_decoupling(effort, hr, effort_type="raw")

API reference

Field names

Workload functions accept customizable field names:

from endurance_metrics import calculate_acwr

# Default: looks for "load" and "date" fields
acwr = calculate_acwr(activities)

# Custom field names
acwr = calculate_acwr(
    activities,
    load_field="tss",
    date_field="start_date",
)

Decoupling with threshold

from endurance_metrics import analyze_overtime

result = analyze_overtime(
    effort_data=watts,
    cardio_data=hr,
    prescribed_seconds=3600,
    effort_type="power",
    threshold=280,  # FTP or threshold value
)

Development

git clone https://github.com/stephanejouve/endurance-metrics.git
cd endurance-metrics
poetry install --with dev
poetry run pytest tests/ -v
poetry run black src/ tests/ --check --line-length=100
poetry run ruff check src/
poetry run isort src/ tests/ --check-only --profile black --line-length=100

License

MIT