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+---
+id: wiki-2026-0508-게임-밸런싱
+title: 게임 밸런싱
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [Game Balancing, 밸런스 디자인]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.9
+verification_status: applied
+tags: [game-design, balancing, simulation]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: Python/TypeScript
+  framework: Unity/Unreal/web
+---
+
+# 게임 밸런싱
+
+## 매 한 줄
+> **"매 fairness × engagement × progression의 quantitative tuning loop"**. 게임 밸런싱은 단순 number tweak이 아닌, player skill curve와 reward schedule을 데이터 기반으로 calibrate하는 systems engineering이다. 2026 현업은 telemetry-driven A/B + LLM-assisted simulation으로 cycle을 일/주 단위로 단축한다.
+
+## 매 핵심
+
+### 매 밸런싱 차원
+- **Power balance**: weapon/character/spell 의 win-rate convergence to ~50%.
+- **Resource economy**: gold/XP/material 의 inflation/sink ratio.
+- **Difficulty curve**: time-to-mastery 의 monotonic with checkpoints.
+- **Matchmaking**: MMR/Elo/Glicko-2 — skill variance bounded.
+
+### 매 측정 지표
+- Win rate, pick rate, ban rate (MOBA/FGC).
+- Time-to-kill (TTK), DPS, EHP (effective HP).
+- Retention D1/D7/D30, session length, churn at level X.
+- Gini coefficient of resource distribution.
+
+### 매 응용
+1. League of Legends 의 patch cycle (2-week) — champion winrate band [47%, 53%].
+2. Hearthstone 의 nerf/buff 의 telemetry-driven (Vicious Syndicate stats).
+3. Slay the Spire 의 Monte Carlo simulation 의 deck balance.
+4. Destiny 2 의 sandbox team 의 sandbox tuning (Bungie quarterly).
+
+## 💻 패턴
+
+### 1. Win-rate convergence target
+```python
+# Bayesian winrate estimate with prior
+import numpy as np
+from scipy.stats import beta
+
+def champion_winrate(wins, losses, prior_a=50, prior_b=50):
+    a = prior_a + wins
+    b = prior_b + losses
+    mean = a / (a + b)
+    ci_low, ci_high = beta.ppf([0.025, 0.975], a, b)
+    return mean, (ci_low, ci_high)
+
+# Patch decision: nerf if CI bound > 0.53
+mean, (lo, hi) = champion_winrate(wins=12500, losses=10800)
+needs_nerf = lo > 0.53
+```
+
+### 2. DPS / TTK calculation
+```python
+def time_to_kill(damage_per_shot, fire_rate_hz, target_hp, armor=0):
+    effective_dps = damage_per_shot * fire_rate_hz * (1 - armor)
+    return target_hp / effective_dps
+
+# Weapon balance check: TTK in [0.4s, 1.2s] band
+for w in weapons:
+    ttk = time_to_kill(w.dmg, w.rpm/60, target_hp=200, armor=0.1)
+    assert 0.4 <= ttk <= 1.2, f"{w.name} TTK={ttk:.2f}s out of band"
+```
+
+### 3. Resource economy faucet/sink
+```typescript
+type Economy = { faucets: number; sinks: number; supply: number };
+
+function inflationRate(econ: Economy, dt: number): number {
+  const net = econ.faucets - econ.sinks;
+  return net / econ.supply / dt; // per-day inflation
+}
+
+// Target: |inflation| < 0.02 per week (2%)
+```
+
+### 4. Monte Carlo deck simulation
+```python
+def simulate_deck(deck, opponents, n_runs=10000):
+    wins = 0
+    for _ in range(n_runs):
+        opp = random.choice(opponents)
+        result = play_match(deck, opp)
+        wins += result == "win"
+    return wins / n_runs
+
+# Auto-balance: card value = marginal winrate Δ when included
+```
+
+### 5. Elo / Glicko-2 matchmaking
+```python
+def glicko2_update(rating, rd, opponents):
+    # rd = rating deviation; volatility tracked separately
+    g = lambda phi: 1 / math.sqrt(1 + 3 * phi**2 / math.pi**2)
+    # ... (full Glicko-2 spec)
+    return new_rating, new_rd
+```
+
+### 6. Difficulty curve fitting
+```python
+import numpy as np
+# Player completion rate per level
+levels = np.arange(1, 51)
+completion = np.array([...])  # telemetry
+# Target: monotonic decrease, no cliff > 15% drop
+diff = np.diff(completion)
+cliffs = np.where(diff < -0.15)[0]
+# cliffs → level redesign candidates
+```
+
+### 7. A/B test with CUPED variance reduction
+```python
+def cuped_estimate(treatment_y, control_y, treatment_x, control_x):
+    # x = pre-experiment covariate (e.g., D7 retention)
+    theta = np.cov(np.concatenate([treatment_y, control_y]),
+                   np.concatenate([treatment_x, control_x]))[0,1] / \
+            np.var(np.concatenate([treatment_x, control_x]))
+    adj_t = treatment_y - theta * treatment_x
+    adj_c = control_y - theta * control_x
+    return adj_t.mean() - adj_c.mean()
+```
+
+### 8. LLM-assisted balance review (2026)
+```python
+# Claude Opus 4.7 의 patch note review
+prompt = f"""
+Patch diff:
+{diff}
+
+Telemetry (last 7d): {telemetry_json}
+
+Identify:
+1. Champions exceeding [47%, 53%] winrate band.
+2. Hidden interactions (item × champion synergy outliers).
+3. Risk score (1-10) per change.
+"""
+review = claude.messages.create(model="claude-opus-4-7", ...)
+```
+
+## 매 결정 기준
+| 상황 | Approach |
+|---|---|
+| Competitive PvP | Winrate convergence + telemetry-driven nerf cycle |
+| Single-player | Difficulty curve + retention cliff analysis |
+| Gacha/F2P | Whale vs F2P parity + pity timer math |
+| Esports patch | 2-week cycle + pro-play meta watch |
+| New release | Closed beta MC sim + open beta A/B |
+
+**기본값**: telemetry → Bayesian winrate → 2-week patch cycle 의 [47%, 53%] band.
+
+## 🔗 Graph
+- 부모: [[Game_Design]]
+
+## 🤖 LLM 활용
+**언제**: patch note generation, telemetry anomaly detection, balance hypothesis brainstorming, sim scenario writing.
+**언제 X**: 최종 numerical tuning (designer judgment + playtesting 의 X-able), competitive integrity decision.
+
+## ❌ 안티패턴
+- **Whale-only balancing**: F2P player 의 churn 의 무시.
+- **Knee-jerk nerf**: 1-day data 의 over-react — sample size 의 부족.
+- **Symmetric balance obsession**: asymmetric design 의 intentional 의 flatten의 X.
+- **Nerf-only meta**: power creep 의 reverse — fun 의 erosion.
+- **Ignoring rank-stratified data**: bronze 와 challenger 의 winrate 의 different — aggregate 의 misleading.
+
+## 🧪 검증 / 중복
+- Verified (Riot Games engineering blog, GDC talks 2023-2025, Vicious Syndicate).
+- 신뢰도 A.
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — game balancing full content with patterns |