9148c358d0
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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7.2 KiB
id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack
| id | title | category | status | canonical_id | aliases | duplicate_of | source_trust_level | confidence_score | verification_status | tags | raw_sources | last_reinforced | github_commit | tech_stack | ||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| wiki-2026-0508-elite-sport-science-protocols | Elite Sport Science Protocols | 10_Wiki/Topics | verified | self |
|
none | A | 0.94 | applied |
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2026-05-10 | pending |
|
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
- Strength & conditioning (S&C).
- Nutrition (peri-workout, micronutrient).
- Recovery (sleep > active recovery > modality).
- Skill / tactical.
- Psychology (motivation, focus).
- Monitoring (objective + subjective).
- 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)
- Sleep (8-10 h elite).
- Nutrition + hydration.
- Active recovery.
- Modality (cold, compression — 매 evidence weak).
매 응용
- Endurance: 매 lactate threshold.
- Power: 매 PAP, complex training.
- Team sport: 매 small-sided games.
- Combat sport: 매 weight cut + recovery.
- eSports / aim: 매 cognitive + visual.
💻 패턴
Acute:Chronic Workload Ratio (Gabbett)
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
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)
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)
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
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)
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
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
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)
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
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
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)
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
- 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 |