How a WN8-style Rating Works in World of Warships (concept overview)
This page explains a WN8-style performance rating adapted for World of Warships. It is intentionally descriptive rather than formulaic: we outline the logic and components without publishing exact constants, caps, or weights. The goal is to make the metric understandable and stable, not farmable.
Purpose
A WN8-style rating estimates your per-battle contribution and compares it to what would be reasonably expected for the same ship and tier. It answers: “How well do you perform with this ship compared to a solid reference?”
Inputs (per battle aggregates)
- Damage dealt (primary, most robust signal)
- Frags (secured kills)
- Win rate (team outcome, used with moderate weight)
- Optional contextual signals (class-aware): spotting/assists, capture/defense influence, aircraft shot down, potential damage, etc.
Data is typically evaluated for multiple windows: 24h (form), 7d (trend), and overall (baseline), with minimum battle and tier requirements to avoid noisy samples.
Expected values (ship baselines)
For each ship, community reference tables provide expected per-battle baselines (e.g., damage, frags, win rate). These are built from large, cleaned samples and updated occasionally to reflect balance/meta shifts, while remaining stable enough to keep the rating consistent over time.
Normalization (performance vs. expectation)
Your per-battle results are compared to the ship’s expectations, producing dimensionless ratios such as:
“your damage per battle” vs. “expected damage”, “your frags per battle” vs. “expected frags”, and so on.
Ratios around 1.0
indicate expected performance; values above/below reflect over/under-performance.
Robust shaping (fairness & stability)
- Capping/clipping limits extreme outliers so small samples don’t dominate.
- Non-linear shaping reduces volatility and keeps the rating meaningful across tiers and ships.
- Balanced component mix: damage provides the backbone; frags and assists/spotting add impact context; win rate anchors team outcomes without overpowering the metric.
Aggregation (single readable score)
The shaped components are combined into one score (open-ended but practically banded). It is ship-aware via expectations and time-window aware via the 24h/7d/overall breakdowns.
Class notes
- Destroyers: spotting, cap influence, survivability matter alongside damage and frags.
- Cruisers: flexible roles; damage plus utility/AA impact can be reflected.
- Battleships: alpha, range control, potential damage; damage remains the most stable indicator.
- Carriers: indirect damage and spotting; treated conservatively to avoid skew.
- Submarines: situational pressure; contributions considered in moderation.
Reading the number
The final score maps to community color bands (from below-average to super-unicum), derived from percentiles of active players. Bands are for orientation; interpretation is always relative to meta, tier, and class.
Limits & good practice
- It’s a statistical estimator, not absolute truth. Context (ship choice, matchmaking, team role) still matters.
- Use multiple windows (24h/7d/overall) to see form, trend, and baseline together.
- Minimum battles/tier thresholds and anti-abuse heuristics reduce low-sample noise and farm bias.
Transparency & maintenance
Expected values are refreshed periodically; the methodology (components, normalizations, caps) remains stable and changes are rare and documented. Re-indexing is only considered when unavoidable (e.g., major API or meta shifts).
FAQ (short)
Why not just win rate? Win rate is team-dependent and slow to move; a WN8-style rating focuses on your per-battle contribution and then calibrates with win rate.
Can it be farmed? Only to a limited extent. Ship-specific expectations, component mix, and caps constrain obvious exploits.
Is this official? No. It’s a community-style metric adapted for WoWS, using the same broad idea as classic WN8: expectations + normalized performance.
Summary: A WN8-style WoWS rating compares your per-battle output to realistic, ship-specific expectations, shapes several signals for stability and fairness, and presents a single, readable score across 24h, 7d, and overall views.