Elo Rating Algorithm
Elo Rating Algorithm is widely used rating algorithm that is used to rank players in many competitive games.
Players with higher ELO rating have a higher probability of winning a game than a player with lower ELO rating. After each game, ELO rating of players is updated. If a player with higher ELO rating wins, only a few points are transferred from the lower rated player. However if lower rated player wins, then transferred points from a higher rated player are far greater.
Approach:
P1: Probability of winning of player with rating2
P2: Probability of winning of player with rating1.P1 = (1.0 / (1.0 + pow(10, ((rating1 – rating2) / 400))));
P2 = (1.0 / (1.0 + pow(10, ((rating2 – rating1) / 400))));
Obviously, P1 + P2 = 1.The rating of player is updated using the formula given below :-
rating1 = rating1 + K*(Actual Score – Expected score);
In most of the games, “Actual Score” is either 0 or 1 means player either wins or loose. K is a constant. If K is of a lower value, then the rating is changed by a small fraction but if K is of a higher value, then the changes in the rating are significant. Different organizations set a different value of K.
Example:
Suppose there is a live match on chess.com between two players
rating1 = 1200, rating2 = 1000;
P1 = (1.0 / (1.0 + pow(10, ((1000-1200) / 400)))) = 0.76
P2 = (1.0 / (1.0 + pow(10, ((1200-1000) / 400)))) = 0.24And Assume constant K=30;
CASE-1 : Suppose Player 1 wins:
rating1 = rating1 + k*(actual – expected) = 1200+30(1 – 0.76) = 1207.2;
rating2 = rating2 + k*(actual – expected) = 1000+30(0 – 0.24) = 992.8;Case-2 : Suppose Player 2 wins:
rating1 = rating1 + k*(actual – expected) = 1200+30(0 – 0.76) = 1177.2;
rating2 = rating2 + k*(actual – expected) = 1000+30(1 – 0.24) = 1022.8;
CPP
// CPP program for Elo Rating #include <bits/stdc++.h> using namespace std; // Function to calculate the Probability float Probability(int rating1, int rating2) { return 1.0 * 1.0 / (1 + 1.0 * pow(10, 1.0 * (rating1 - rating2) / 400)); } // Function to calculate Elo rating // K is a constant. // d determines whether Player A wins or Player B. void EloRating(float Ra, float Rb, int K, bool d) { // To calculate the Winning // Probability of Player B float Pb = Probability(Ra, Rb); // To calculate the Winning // Probability of Player A float Pa = Probability(Rb, Ra); // Case -1 When Player A wins // Updating the Elo Ratings if (d == 1) { Ra = Ra + K * (1 - Pa); Rb = Rb + K * (0 - Pb); } // Case -2 When Player B wins // Updating the Elo Ratings else { Ra = Ra + K * (0 - Pa); Rb = Rb + K * (1 - Pb); } cout << "Updated Ratings:-\n"; cout << "Ra = " << Ra << " Rb = " << Rb; } int main() { // Ra and Rb are current ELO ratings float Ra = 1200, Rb = 1000; int K = 30; bool d = 1; EloRating(Ra, Rb, K, d); return 0; } |
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Java
// Java program to count rotationally // equivalent rectangles with n unit // squares class GFG { // Function to calculate the Probability static float Probability(float rating1, float rating2) { return 1.0f * 1.0f / (1 + 1.0f * (float)(Math.pow(10, 1.0f * (rating1 - rating2) / 400))); } // Function to calculate Elo rating // K is a constant. // d determines whether Player A wins // or Player B. static void EloRating(float Ra, float Rb, int K, boolean d) { // To calculate the Winning // Probability of Player B float Pb = Probability(Ra, Rb); // To calculate the Winning // Probability of Player A float Pa = Probability(Rb, Ra); // Case -1 When Player A wins // Updating the Elo Ratings if (d == true) { Ra = Ra + K * (1 - Pa); Rb = Rb + K * (0 - Pb); } // Case -2 When Player B wins // Updating the Elo Ratings else { Ra = Ra + K * (0 - Pa); Rb = Rb + K * (1 - Pb); } System.out.print("Updated Ratings:-\n"); System.out.print("Ra = " + (Math.round( Ra * 1000000.0) / 1000000.0) + " Rb = " + Math.round(Rb * 1000000.0) / 1000000.0); } //driver code public static void main (String[] args) { // Ra and Rb are current ELO ratings float Ra = 1200, Rb = 1000; int K = 30; boolean d = true; EloRating(Ra, Rb, K, d); } } // This code is contributed by Anant Agarwal. |
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Python3
# Python 3 program for Elo Rating import math # Function to calculate the Probability def Probability(rating1, rating2): return 1.0 * 1.0 / (1 + 1.0 * math.pow(10, 1.0 * (rating1 - rating2) / 400)) # Function to calculate Elo rating # K is a constant. # d determines whether # Player A wins or Player B. def EloRating(Ra, Rb, K, d): # To calculate the Winning # Probability of Player B Pb = Probability(Ra, Rb) # To calculate the Winning # Probability of Player A Pa = Probability(Rb, Ra) # Case -1 When Player A wins # Updating the Elo Ratings if (d == 1) : Ra = Ra + K * (1 - Pa) Rb = Rb + K * (0 - Pb) # Case -2 When Player B wins # Updating the Elo Ratings else : Ra = Ra + K * (0 - Pa) Rb = Rb + K * (1 - Pb) print("Updated Ratings:-") print("Ra =", round(Ra, 6)," Rb =", round(Rb, 6)) # Driver code # Ra and Rb are current ELO ratings Ra = 1200Rb = 1000K = 30d = 1EloRating(Ra, Rb, K, d) # This code is contributed by # Smitha Dinesh Semwal |
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C#
// C# program to count rotationally equivalent // rectangles with n unit squares using System; class GFG { // Function to calculate the Probability static float Probability(float rating1, float rating2) { return 1.0f * 1.0f / (1 + 1.0f * (float)(Math.Pow(10, 1.0f * (rating1 - rating2) / 400))); } // Function to calculate Elo rating // K is a constant. // d determines whether Player A wins or // Player B. static void EloRating(float Ra, float Rb, int K, bool d) { // To calculate the Winning // Probability of Player B float Pb = Probability(Ra, Rb); // To calculate the Winning // Probability of Player A float Pa = Probability(Rb, Ra); // Case -1 When Player A wins // Updating the Elo Ratings if (d == true) { Ra = Ra + K * (1 - Pa); Rb = Rb + K * (0 - Pb); } // Case -2 When Player B wins // Updating the Elo Ratings else { Ra = Ra + K * (0 - Pa); Rb = Rb + K * (1 - Pb); } Console.Write("Updated Ratings:-\n"); Console.Write("Ra = " + (Math.Round(Ra * 1000000.0) / 1000000.0) + " Rb = " + Math.Round(Rb * 1000000.0) / 1000000.0); } //driver code public static void Main() { // Ra and Rb are current ELO ratings float Ra = 1200, Rb = 1000; int K = 30; bool d = true; EloRating(Ra, Rb, K, d); } } // This code is contributed by Anant Agarwal. |
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Output:
Updated Ratings:- Ra = 1207.207642 Rb = 992.792419
Time Complexity
Time complexity of algorithm depends mostly on the complexity of pow function whose
complexity is dependent on Computer Architecture.
On x86, this is constant time operation:-O(1)
References
http://www.gautamnarula.com/rating/
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