feat(ratings): Add Python implementation for Elo Expected Score formula

[Navodhya-Fernando]

🎯 Feature: Implement the Elo Expected Score Formula

📋 Description

This PR introduces the calculate_elo_expected_score function to the math section. This function is a core component of the widely used Elo Rating System, which determines the expected outcome of a match between two players based on their current ratings.

⚽ Why This Matters (Sports Industry Relevance)

This algorithm is directly applicable to statistical analysis in many competitive fields, including:

• Sports Analytics: Ranking teams/players in chess, football, basketball, and esports.
• Predictive Modeling: Calculating the probability of a win, which informs betting odds and team strategy.

🔧 What's Added

• New File: maths/elo_expected_score.py
• New Function: calculate_elo_expected_score(rating_a, rating_b)
• Includes: Necessary import math and a basic if __name__ == '__main__' usage example.

✅ Checklist

• Code adheres to repository style guides.
• Includes a comprehensive docstring explaining the formula and parameters.
• Includes a self-testing block (if __name__ == '__main__':).
• Added to the correct category (maths).

🎃 Hacktoberfest 2025
This is submitted as a high-quality contribution, adding a practical and mathematically significant algorithm to the collection.

[algorithms-keeper]

Click here to look at the relevant links ⬇️

🔗 Relevant Links

Repository:

• Contributing guidelines
• Project Euler solution guidelines

Python:

• Formatted string literals (f-strings)
• Type hints
• doctest
• unittest
• pytest

Automated review generated by algorithms-keeper. If there's any problem regarding this review, please open an issue about it.

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[algorithms-keeper]

Please provide return type hint for the function: test_calculate_elo_expected_score. If the function does not return a value, please provide the type hint as: def function() -> None:

[cclauss]

Closing require_type_hints PRs to prepare for Hacktoberfest