---
id: wiki-2026-0508-elite-sport-science-protocols
title: Elite Sport Science Protocols
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [sport science, elite athlete, periodization, recovery, monitoring, GPS, HRV]
duplicate_of: none
source_trust_level: A
confidence_score: 0.94
verification_status: applied
tags: [sport-science, elite-athlete, periodization, recovery, monitoring, training-load]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: Sport Science
  applicable_to: [Performance, Recovery, Monitoring]
---

# Elite Sport Science Protocols

## 매 한 줄
> **"매 elite performance 의 systematic prepare + monitor + recover"**. 매 periodization (Bondarchuk, block), 매 monitoring (HRV, GPS, RPE), 매 recovery (sleep, nutrition, modality), 매 individualize (genotype, response). 매 modern: 매 wearable + ML.

## 매 핵심

### 매 pillar
1. **Strength & conditioning** (S&C).
2. **Nutrition** (peri-workout, micronutrient).
3. **Recovery** (sleep > active recovery > modality).
4. **Skill / tactical**.
5. **Psychology** (motivation, focus).
6. **Monitoring** (objective + subjective).
7. **Periodization** (macro, meso, micro).

### 매 monitoring metric
- **External load**: 매 GPS (TD, HSR, sprint).
- **Internal load**: 매 HR, RPE, HRV.
- **Wellness**: 매 sleep, soreness, mood.
- **Performance test**: 매 jump, sprint, repeat sprint.
- **Biomarker**: 매 CK, cortisol.

### 매 periodization
- **Linear** (Matveyev): 매 prep → comp → transition.
- **Block** (Issurin): 매 accumulation / transmutation / realization.
- **Conjugate** (Verkhoshansky): 매 multiple qualities.
- **Tapering**: 매 2-3 wk pre-comp.

### 매 recovery hierarchy (modern)
1. **Sleep** (8-10 h elite).
2. **Nutrition + hydration**.
3. **Active recovery**.
4. **Modality** (cold, compression — 매 evidence weak).

### 매 응용
1. **Endurance**: 매 lactate threshold.
2. **Power**: 매 PAP, complex training.
3. **Team sport**: 매 small-sided games.
4. **Combat sport**: 매 weight cut + recovery.
5. **eSports / aim**: 매 cognitive + visual.

## 💻 패턴

### Acute:Chronic Workload Ratio (Gabbett)
```python
def acwr(daily_loads, acute_days=7, chronic_days=28):
    if len(daily_loads) < chronic_days: return None
    acute = sum(daily_loads[-acute_days:]) / acute_days
    chronic = sum(daily_loads[-chronic_days:]) / chronic_days
    ratio = acute / chronic if chronic > 0 else 0
    risk = 'sweet_spot' if 0.8 <= ratio <= 1.3 else ('high_risk' if ratio > 1.5 else 'detrain')
    return {'ratio': ratio, 'risk': risk}
```

### HRV-guided training
```python
def hrv_decision(today_hrv, baseline_mean, baseline_sd):
    z = (today_hrv - baseline_mean) / baseline_sd
    if z < -1: return 'reduce_intensity_or_rest'
    elif z < 0: return 'maintain'
    elif z < 1: return 'normal'
    else: return 'opportunity_for_high_intensity'
```

### Session RPE (Foster)
```python
def session_rpe_load(rpe_0_10, duration_min):
    return rpe_0_10 * duration_min  # 매 AU (arbitrary units)

def weekly_monotony(daily_loads):
    return np.mean(daily_loads) / max(np.std(daily_loads), 1e-6)

def strain(weekly_load, monotony):
    return weekly_load * monotony  # 매 > 6000 = high illness risk
```

### Bondarchuk periodization (block)
```python
PERIODIZATION = {
    'accumulation': {'duration_wk': 4, 'volume': 'high', 'intensity': 'low-mod', 'focus': 'aerobic_capacity'},
    'transmutation': {'duration_wk': 3, 'volume': 'mod', 'intensity': 'high', 'focus': 'sport_specific'},
    'realization': {'duration_wk': 2, 'volume': 'low', 'intensity': 'peak', 'focus': 'competition'},
}
```

### Sleep tracking
```python
def sleep_quality(records):
    return {
        'duration_h': np.mean([r.duration for r in records]),
        'efficiency': np.mean([r.efficiency for r in records]),  # 매 time_asleep / time_in_bed
        'deep_sleep_pct': np.mean([r.deep / r.total for r in records]),
        'rem_pct': np.mean([r.rem / r.total for r in records]),
    }
```

### GPS load (team sport)
```python
def gps_metrics(trace):
    return {
        'total_distance_m': trace.distance,
        'high_speed_running_m': trace.distance_above(5.5),  # 매 m/s
        'sprints': sum(1 for s in trace.efforts if s.peak_speed > 7),
        'accelerations_high': sum(1 for a in trace.accels if a > 3),  # 매 m/s²
        'player_load_au': sqrt_sum_jerks(trace),
    }
```

### Lactate threshold
```python
def lactate_threshold(test_data):
    """매 LT2 = 매 4 mmol/L OR 매 inflection point."""
    speeds, lactates = zip(*test_data)
    for i, l in enumerate(lactates):
        if l >= 4.0: return speeds[i]
    return None
```

### Tapering protocol
```python
def taper_volume(days_to_comp, peak_volume):
    """매 2-week exponential taper."""
    if days_to_comp > 14: return peak_volume
    return peak_volume * 0.5 ** ((14 - days_to_comp) / 4)
```

### Wellness questionnaire (5-pt)
```python
def daily_wellness(sleep, soreness, fatigue, stress, mood):
    """매 1=worst, 5=best."""
    score = sleep + soreness + fatigue + stress + mood
    return {'score': score, 'flag_red': score < 12, 'flag_yellow': score < 17}
```

### CK (creatine kinase) interpretation
```python
def ck_load_classification(ck_iu_l, baseline=200):
    if ck_iu_l < baseline * 1.5: return 'normal'
    if ck_iu_l < baseline * 3: return 'elevated_typical'
    if ck_iu_l < baseline * 5: return 'high_recovery_priority'
    return 'very_high_consider_rest'
```

### Heat acclimation
```python
def heat_acclimation_protocol():
    """매 10-14 day protocol."""
    return {
        'days': 14,
        'duration_per_day_min': 60,
        'temperature_c': 35,
        'intensity': '50-65% VO2max',
        'expected_adaptations': ['plasma_volume_+12%', 'sweat_rate_+30%', 'core_temp_threshold_-0.4C'],
    }
```

### Genotype-informed (e.g., ACTN3)
```python
def actn3_recommendation(genotype):
    if genotype == 'RR': return 'power-leaning'
    if genotype == 'RX': return 'mixed'
    if genotype == 'XX': return 'endurance-leaning'
```

## 매 결정 기준
| 상황 | Protocol |
|---|---|
| Endurance | Polarized 80/20 + LT |
| Power | Block + complex |
| Team sport | Tactical periodization + GPS |
| Combat | Weight cut + recovery |
| Pre-comp | Taper |
| Overreach risk | ACWR + HRV |

**기본값**: 매 ACWR + HRV-guided + sleep priority + RPE log + periodization (block) + individual response.

## 🔗 Graph
- 부모: [[Sport-Science]] · [[Performance]]
- 변형: [[Periodization]]
- 응용: [[Strength-Conditioning]] · [[Elite-Sport-Science-Protocols|Endurance-Athletics-Cognition]]
- Adjacent: [[Recovery]] · [[HRV]]

## 🤖 LLM 활용
**언제**: 매 elite athlete prep. 매 team sport S&C.
**언제 X**: 매 recreational unguided.

## ❌ 안티패턴
- **No load monitor**: 매 overtraining.
- **Modality before basics**: 매 ice bath > sleep is wrong.
- **Same plan for all**: 매 individual response.
- **No taper**: 매 peak miss.
- **CK only as fatigue marker**: 매 multi-marker 의 prefer.

## 🧪 검증 / 중복
- Verified (NSCA, ACSM, Bondarchuk, Issurin, Gabbett 2016).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-04-20 | Auto-reinforced |
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — periodization + ACWR / HRV / GPS / sleep / taper code |
