LOOT DROP & GACHA PROBABILITY CALCULATOR
Model exact drop chances, banner pulls, and pity thresholds for mobile and RPG games. Calculate the mathematical probability of obtaining rare items over $N$ attempts with cumulative probability distribution curves.
PROBABILITY PARAMETERS BINOMIAL MATH
PROBABILITY BREAKDOWN EXACT ODDS
WEIGHTED RANDOM LOOT TABLE MANAGER (C# UNITY)
using System.Collections.Generic;
using UnityEngine;
[System.Serializable]
public class LootItem
{
public string itemName;
[Range(0.01f, 100f)] public float dropWeight = 1.0f;
public GameObject itemPrefab;
}
public class LootTableManager : MonoBehaviour
{
public List lootPool = new List();
public LootItem RollLoot()
{
float totalWeight = 0f;
foreach (var item in lootPool) totalWeight += item.dropWeight;
float roll = Random.Range(0f, totalWeight);
float cumulative = 0f;
foreach (var item in lootPool)
{
cumulative += item.dropWeight;
if (roll <= cumulative) return item;
}
return lootPool[lootPool.Count - 1];
}
}
UNDERSTANDING GACHA & LOOT DROP MATHEMATICS
The Gambler's Fallacy & Binomial Probability
A common misconception is that a 1% drop rate guarantees an item within 100 pulls. In reality, each attempt is an independent Bernoulli trial. The probability of failing 100 consecutive times at 1% is:
Therefore, after 100 pulls, you only have a 63.4% chance of getting at least one drop.
Why Games Implement Pity Systems
To protect players from statistical extreme outliers (the unluckiest 1% who might roll 400+ times without an item), modern games implement Hard Pity and Soft Pity mechanics.
Hard pity guarantees a 100% success rate on roll $N$, while soft pity progressively scales drop rates (e.g. +6% per pull after pull 74 in Genshin Impact) to smooth out player retention and satisfaction.