docs(10_Wiki): 위키 전체 재구성 — Topic_* 폴더를 4개 카테고리로 통합 + 대규모 중복 제거
Topic_Agent/Topic_Blog/Topics/Topics_Biz/Topics_Meeting/Topics_Rag의 마크다운 지식 문서를 Topic_General/Topic_Programming/Topic_Graphic/Topic_Business 4개 카테고리로 재분류. - 중복 제거: frontmatter의 status:duplicate/merged + duplicate_of/redirect_to 필드로 자기 자신을 중복으로 선언한 리다이렉트 stub 1032개 제거, 완전 동일 내용 파일 472개 제거, 동일 파일명·다른 내용 충돌 시 더 큰(완전한) 버전만 유지(162개 제거) — 총 1639개 중복 제거. - 분류: 폴더 단위로 명확한 항목(AI_and_ML/Coding/Architecture 등 → Programming, Comfyui/Visual_Effects → Graphic, Topics_Biz/Topics_Meeting/사업 등 → Business, Poetic_Blog_Writing/창의성/Game_Design 등 → General)은 폴더 우선순위로, 나머지 혼재 폴더(Topic_Agent/Topic_Blog/Topics 루트/Thinking & Reasoning/Other/UI_UX_Assets)는 title/tags 키워드 스코어링으로 파일 단위 분류(불명확한 경우 General로 폴백). 원본 폴더명은 "From_*" 서브폴더로 보존해 추적 가능성 유지. - 최종 배치: Programming 2784 / General 1608 / Graphic 285 / Business 249 = 4926개 문서. - 에이전트 운영 상태(.astra/.agent/.obsidian/sessions/memory/_company/docs/lessons/_shared/src)는 지식 콘텐츠가 아니므로 재분류 대상에서 제외하고 원위치 유지. - Topics/Topic_email(상위 보호 폴더 Topic_email과 파일명 100% 중복) 삭제 — 보호 폴더 자체는 미변경. - 완전히 비게 된 Topic_Agent/Topic_Blog/Topics_Biz/Topics_Rag 폴더 제거.
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Antigravity Agent
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---
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id: wiki-2026-0508-elite-sport-science-protocols
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title: Elite Sport Science Protocols
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [sport science, elite athlete, periodization, recovery, monitoring, GPS, HRV]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.94
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verification_status: applied
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tags: [sport-science, elite-athlete, periodization, recovery, monitoring, training-load]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: Sport Science
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applicable_to: [Performance, Recovery, Monitoring]
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---
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# Elite Sport Science Protocols
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## 매 한 줄
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> **"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.
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## 매 핵심
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### 매 pillar
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1. **Strength & conditioning** (S&C).
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2. **Nutrition** (peri-workout, micronutrient).
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3. **Recovery** (sleep > active recovery > modality).
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4. **Skill / tactical**.
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5. **Psychology** (motivation, focus).
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6. **Monitoring** (objective + subjective).
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7. **Periodization** (macro, meso, micro).
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### 매 monitoring metric
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- **External load**: 매 GPS (TD, HSR, sprint).
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- **Internal load**: 매 HR, RPE, HRV.
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- **Wellness**: 매 sleep, soreness, mood.
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- **Performance test**: 매 jump, sprint, repeat sprint.
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- **Biomarker**: 매 CK, cortisol.
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### 매 periodization
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- **Linear** (Matveyev): 매 prep → comp → transition.
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- **Block** (Issurin): 매 accumulation / transmutation / realization.
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- **Conjugate** (Verkhoshansky): 매 multiple qualities.
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- **Tapering**: 매 2-3 wk pre-comp.
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### 매 recovery hierarchy (modern)
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1. **Sleep** (8-10 h elite).
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2. **Nutrition + hydration**.
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3. **Active recovery**.
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4. **Modality** (cold, compression — 매 evidence weak).
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### 매 응용
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1. **Endurance**: 매 lactate threshold.
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2. **Power**: 매 PAP, complex training.
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3. **Team sport**: 매 small-sided games.
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4. **Combat sport**: 매 weight cut + recovery.
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5. **eSports / aim**: 매 cognitive + visual.
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## 💻 패턴
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### Acute:Chronic Workload Ratio (Gabbett)
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```python
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def acwr(daily_loads, acute_days=7, chronic_days=28):
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if len(daily_loads) < chronic_days: return None
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acute = sum(daily_loads[-acute_days:]) / acute_days
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chronic = sum(daily_loads[-chronic_days:]) / chronic_days
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ratio = acute / chronic if chronic > 0 else 0
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risk = 'sweet_spot' if 0.8 <= ratio <= 1.3 else ('high_risk' if ratio > 1.5 else 'detrain')
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return {'ratio': ratio, 'risk': risk}
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```
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### HRV-guided training
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```python
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def hrv_decision(today_hrv, baseline_mean, baseline_sd):
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z = (today_hrv - baseline_mean) / baseline_sd
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if z < -1: return 'reduce_intensity_or_rest'
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elif z < 0: return 'maintain'
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elif z < 1: return 'normal'
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else: return 'opportunity_for_high_intensity'
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```
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### Session RPE (Foster)
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```python
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def session_rpe_load(rpe_0_10, duration_min):
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return rpe_0_10 * duration_min # 매 AU (arbitrary units)
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def weekly_monotony(daily_loads):
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return np.mean(daily_loads) / max(np.std(daily_loads), 1e-6)
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def strain(weekly_load, monotony):
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return weekly_load * monotony # 매 > 6000 = high illness risk
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```
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### Bondarchuk periodization (block)
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```python
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PERIODIZATION = {
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'accumulation': {'duration_wk': 4, 'volume': 'high', 'intensity': 'low-mod', 'focus': 'aerobic_capacity'},
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'transmutation': {'duration_wk': 3, 'volume': 'mod', 'intensity': 'high', 'focus': 'sport_specific'},
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'realization': {'duration_wk': 2, 'volume': 'low', 'intensity': 'peak', 'focus': 'competition'},
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}
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```
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### Sleep tracking
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```python
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def sleep_quality(records):
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return {
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'duration_h': np.mean([r.duration for r in records]),
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'efficiency': np.mean([r.efficiency for r in records]), # 매 time_asleep / time_in_bed
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'deep_sleep_pct': np.mean([r.deep / r.total for r in records]),
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'rem_pct': np.mean([r.rem / r.total for r in records]),
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}
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```
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### GPS load (team sport)
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```python
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def gps_metrics(trace):
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return {
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'total_distance_m': trace.distance,
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'high_speed_running_m': trace.distance_above(5.5), # 매 m/s
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'sprints': sum(1 for s in trace.efforts if s.peak_speed > 7),
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'accelerations_high': sum(1 for a in trace.accels if a > 3), # 매 m/s²
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'player_load_au': sqrt_sum_jerks(trace),
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}
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```
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### Lactate threshold
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```python
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def lactate_threshold(test_data):
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"""매 LT2 = 매 4 mmol/L OR 매 inflection point."""
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speeds, lactates = zip(*test_data)
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for i, l in enumerate(lactates):
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if l >= 4.0: return speeds[i]
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return None
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```
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### Tapering protocol
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```python
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def taper_volume(days_to_comp, peak_volume):
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"""매 2-week exponential taper."""
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if days_to_comp > 14: return peak_volume
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return peak_volume * 0.5 ** ((14 - days_to_comp) / 4)
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```
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### Wellness questionnaire (5-pt)
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```python
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def daily_wellness(sleep, soreness, fatigue, stress, mood):
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"""매 1=worst, 5=best."""
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score = sleep + soreness + fatigue + stress + mood
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return {'score': score, 'flag_red': score < 12, 'flag_yellow': score < 17}
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```
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### CK (creatine kinase) interpretation
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```python
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def ck_load_classification(ck_iu_l, baseline=200):
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if ck_iu_l < baseline * 1.5: return 'normal'
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if ck_iu_l < baseline * 3: return 'elevated_typical'
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if ck_iu_l < baseline * 5: return 'high_recovery_priority'
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return 'very_high_consider_rest'
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```
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### Heat acclimation
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```python
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def heat_acclimation_protocol():
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"""매 10-14 day protocol."""
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return {
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'days': 14,
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'duration_per_day_min': 60,
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'temperature_c': 35,
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'intensity': '50-65% VO2max',
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'expected_adaptations': ['plasma_volume_+12%', 'sweat_rate_+30%', 'core_temp_threshold_-0.4C'],
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}
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```
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### Genotype-informed (e.g., ACTN3)
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```python
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def actn3_recommendation(genotype):
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if genotype == 'RR': return 'power-leaning'
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if genotype == 'RX': return 'mixed'
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if genotype == 'XX': return 'endurance-leaning'
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```
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## 매 결정 기준
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| 상황 | Protocol |
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|---|---|
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| Endurance | Polarized 80/20 + LT |
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| Power | Block + complex |
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| Team sport | Tactical periodization + GPS |
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| Combat | Weight cut + recovery |
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| Pre-comp | Taper |
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| Overreach risk | ACWR + HRV |
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**기본값**: 매 ACWR + HRV-guided + sleep priority + RPE log + periodization (block) + individual response.
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## 🔗 Graph
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- 부모: [[Sport-Science]] · [[Performance]]
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- 변형: [[Periodization]]
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- 응용: [[Strength-Conditioning]] · [[Elite-Sport-Science-Protocols|Endurance-Athletics-Cognition]]
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- Adjacent: [[Recovery]] · [[HRV]]
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## 🤖 LLM 활용
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**언제**: 매 elite athlete prep. 매 team sport S&C.
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**언제 X**: 매 recreational unguided.
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## ❌ 안티패턴
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- **No load monitor**: 매 overtraining.
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- **Modality before basics**: 매 ice bath > sleep is wrong.
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- **Same plan for all**: 매 individual response.
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- **No taper**: 매 peak miss.
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- **CK only as fatigue marker**: 매 multi-marker 의 prefer.
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## 🧪 검증 / 중복
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- Verified (NSCA, ACSM, Bondarchuk, Issurin, Gabbett 2016).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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|---|---|
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| 2026-04-20 | Auto-reinforced |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — periodization + ACWR / HRV / GPS / sleep / taper code |
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