eloranker v0.2.0

Ranker

A robust and flexible TypeScript library for ranking items based on pairwise comparisons using an advanced Elo-like rating system.

Table of Contents

• Ranker
  • Table of Contents
  • Features
  • Installation
  • Quick Start
  • Configuration
    • RankerConfig
    • Effects of Parameters
    • Progress Tracking
      • ProgressParams
  • API Reference
    • Ranker Class
      • Constructor
      • Methods
    • Types
  • Advanced Usage
    • Batch Processing
    • Rating Delta Analysis
  • Mathematical Foundation
  • Contributing

Features

• 🚀 Easy-to-use API for managing rankable items and their comparisons
• ⚙️ Highly configurable ranking parameters
• 🧠 Intelligent calculation of optimal next comparison
• 📊 Comprehensive progress tracking for ranking stability
• 📈 Detailed item statistics and historical data
• 🔧 Full TypeScript support with type definitions
• 🧮 Transparent mathematical model with customizable parameters

Installation

Install Ranker using npm:

npm install ranker

Or using yarn:

yarn add ranker

Quick Start

Get up and running with Ranker in just a few lines of code:

import { Ranker, RankableItem, ComparisonResult } from "eloranker";

// Initialize items
const initialItems: RankableItem[] = [
  { id: "item1", initialRating: 1500 },
  { id: "item2", initialRating: 1500 },
  { id: "item3", initialRating: 1500 },
];

// Create a new Ranker instance
const ranker = new Ranker(initialItems, { kFactor: 32 });

// Add a comparison result
const result: ComparisonResult = {
  itemId1: "item1",
  itemId2: "item2",
  result: "win",
  timestamp: Date.now(),
};

const ratingDelta = ranker.addComparisonResult(result);
console.log(`Rating change: ${ratingDelta}`);

// Get current rankings
const rankings = ranker.getRankings();
console.log("Current rankings:", rankings);

Configuration

Customize Ranker's behavior with the following configuration options:

RankerConfig

Example configuration:

const ranker = new Ranker(initialItems, {
  kFactor: 24,
  minimumComparisons: 30,
  defaultInitialRating: 1600,
  minRating: 100,
});

Effects of Parameters

• kFactor: Higher values make the system more responsive but potentially volatile. Lower values provide more stability but slower adaptation.
• minimumComparisons: Increasing this value requires more data for stability, potentially increasing accuracy but increasing number of comparisons.
• defaultInitialRating: Adjust based on prior knowledge about the general strength of new items.
• minRating: Prevents extremely low ratings, which may be desirable in some applications.

Progress Tracking

Monitor ranking stability with the getProgress method:

const progress = ranker.getProgress({
  ratingChangeThreshold: 5,
  stableComparisonsThreshold: 10,
});
console.log(`Ranking progress: ${progress * 100}%`);

ProgressParams

• Lower ratingChangeThreshold increases accuracy but slows stabilization.
• Higher stableComparisonsThreshold increases confidence but takes longer to achieve.

API Reference

Ranker Class

Constructor

constructor(initialItems: RankableItem[], config: Partial<RankerConfig>)

Methods

Types

type RankableItem = {
  id: string;
  initialRating: number;
  currentRating: number;
  comparisons: number;
  wins: number;
  losses: number;
  ties: number;
  lastComparisonTime: number | null;
  ratingHistory: Array<{ rating: number; timestamp: number }>;
};

type ComparisonResult = {
  itemId1: string;
  itemId2: string;
  result: "win" | "loss" | "tie";
  timestamp: number;
  metadata?: any;
};

type RankerConfig = {
  kFactor: number;
  minimumComparisons: number;
  defaultInitialRating: number;
  minRating: number;
};

type ProgressParams = {
  ratingChangeThreshold: number;
  stableComparisonsThreshold: number;
};

Advanced Usage

Batch Processing

For systems with many items, implement batched processing:

const allItems = ranker.getAllItems();
const batchSize = 100;

for (let i = 0; i < allItems.length; i += batchSize) {
  const batch = allItems.slice(i, i + batchSize);
  // Process batch...
}

Rating Delta Analysis

Use the rating delta to trigger events or updates:

const SIGNIFICANT_CHANGE_THRESHOLD = 50;

const ratingDelta = ranker.addComparisonResult(result);
if (ratingDelta > SIGNIFICANT_CHANGE_THRESHOLD) {
  console.log("Significant ranking change detected!");
  // Trigger updates, notifications, etc.
}

Mathematical Foundation

Ranker uses an advanced Elo-like rating system. Here are the key equations:

1. Expected Score Calculation:

   The expected score for player A when competing against player B is:

   $E(A) = \frac{1}{1 + 10^{(R_B - R_A) / 400}}$

   Where $R_A$ and $R_B$ are the current ratings of players A and B.

2. Rating Update:

   After a comparison, ratings are updated using:

   $R{new} = R{old} + K \cdot (S - E)$

   Where:

   • $R_{new}$ is the new rating
   • $R_{old}$ is the old rating
   • $K$ is the k-factor (configurable)
   • $S$ is the actual score (1 for win, 0.5 for tie, 0 for loss)
   • $E$ is the expected score from step 1

3. Rating Delta:

   The rating delta returned by addComparisonResult is:

   $\Delta = |R{new_A} - R{oldA}| + |R{newB} - R{old_B}|$

   This represents the total change in ratings for both items.

These equations ensure:

• Winning against a higher-rated opponent yields a larger rating increase.
• Losing against a lower-rated opponent results in a larger rating decrease.
• The k-factor controls the maximum possible change from a single comparison.

Contributing

We welcome contributions to Ranker! Here's how you can help:

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request