Weapon Battle Simulator
Run practical cross-franchise weapon simulations with WBS scoring, matchup adjustments, Hax overrides, and Monte Carlo win rates.
Environment Tags
Input GuideHow to choose inputs and interpret win rates.Expand
Input Guide
How to choose inputs and interpret win rates.
Expand
Methodology Overview
This simulator estimates win probability using a Weapon Battle Score baseline plus matchup adjustments and a Monte Carlo simulation loop. Change the scenario and constraints to model context, then compare how the win rate shifts.
For scoring definitions and how WBS is constructed, see the methodology link below.
Inputs below select the two weapons, the battle context, and constraints such as win condition and environment tags. They do not change the dataset values; they change how the simulator evaluates the matchup.
Weapon Inputs
- Weapon A / Weapon B: type a weapon name or slug. Exact slug/name matches first, then partial matches.
- Use a more specific query if you get the wrong match (e.g., include a unique word from the name).
- Two identical weapons are not allowed.
Scenario & Constraints
- Scenario: 1v1 / War / Cosmic shifts how scale and battlefield assumptions matter.
- Win Condition: “all” includes every win route; a specific value filters to that win condition model.
- User Skill: models how well the user can leverage complex kits and high-skill weapons.
Simulation Count
- Range: 100–5000 (higher is slower but reduces randomness).
- For quick comparisons, 500–1500 is usually enough; use higher counts when the matchup is close.
Environment Tags
- Select tags to bias the context (e.g., space, urban, magic, technology, close-combat, long-range).
- Clear tags to return to the default “no restriction” context.
- Sharing: after simulating, the URL includes query parameters for reproducible results.
Frequently Asked QuestionsCommon questions about the weapon battle simulator.Expand
Frequently Asked Questions
Common questions about the weapon battle simulator.
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How is the weapon win rate calculated?
The win rate is generated using a Monte Carlo simulation. We run the selected matchup thousands of times (default 1000) using the Enjoyip Weapon Battle Score (WBS) framework. We apply matchup adjustments, rule overrides (like extreme hax), and environmental modifiers to determine who wins each iteration.
What does the "User Skill" setting do?
Some weapons require intense training to use effectively, while others are essentially "point and shoot." The User Skill setting adjusts the combat effectiveness of weapons that have a high skill requirement. If you set it to "low", complex weapons will perform worse than simple ones.
How do Environment Tags affect the battle?
Tags add specific context to the simulation. For instance, selecting "space" or "water" might drastically lower the performance of a fire-based weapon, or "close-combat" might heavily favor a melee weapon over a sniper rifle.
Can I link someone directly to a simulated battle?
Yes. Once you click "Simulate Battle", the URL in your browser will automatically update to include all your selected settings. You can copy that link to share the exact simulation with others.
Rankings
Weapon Ranking Simulator
Explore All-vs-All rankings, win rates, and the strongest weapons across scenarios.
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Methodology
Enjoyip Weapon Battle Score
See the scoring dimensions and formulas behind weapon battles and rankings.
Open