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