add p_win method and misc cleanup and refactoring

* change g2rating,etc. to attr_reader (instead of attr_accessor)
* move @dvolatility into DVOL constant
* add p_win
* make comparison operator use p_win(self,other)
* use << when adding to results array instead of .push()
* improved comments; mostly from Glicko2 document
* minor variable renaming to improve readability
* clear results array after ratings are finally updated (no functional change, just cleaner)

rb/glicko2.rb

@@ -1,4 +1,4 @@
-#!/usr/bin/env ruby
+#!/usr/bin/env ruby -w
 
 #
 #
@@ -41,7 +41,7 @@ class Glicko2
   protected
 
   # Glicko2 reprensentation (Glicko2 uses different scales from Glicko)
-		attr_accessor :g2rating, :g2deviation, :g2volatility
+  attr_reader :g2rating, :g2deviation, :g2volatility
 
   # a nested Class to hold a single result
   Result = Struct.new(:opponent, :result)
@@ -52,11 +52,13 @@ class Glicko2
   LOSS = 0.0
   DRAW = 0.5
 
+  # system constant, determines delta volatility over time; should be [0.3,1.2]
+  DVOL = 0.3
+
   # Constructor with rating, rating deviation, and volatility optionally
   # specified.  If nothing specified, nitializes to a rating of 1500, a 
   # rating deviation of 350, and a volatility of 0.06.
   def initialize(rating = 1500.0, deviation = 350.0, volatility = 0.06)
-			@dvolatility    = 0.3
     self.rating     = rating
     self.deviation  = deviation
     self.volatility = volatility
@@ -67,7 +69,12 @@ class Glicko2
 
   # Comparison operator
   def <=>(other)
-			self.rating <=> other.rating
+    p_win(other) <=> 0.5
+  end
+
+  # get the probability of beating opponent
+  def p_win(opponent)
+    1.0 / (1.0 + 10.0**(((opponent.rating - self.rating) / (400.0 * Math.sqrt(1.0 + 0.0000100723986 * (self.deviation**2.0 + opponent.deviation**2.0))))))
   end
 
   # Get the current rating.  This rating is a Glicko rating, not a Glicko2 rating.
@@ -108,14 +115,14 @@ class Glicko2
   # and/or add_draw().  This method is called automatically whenever update()
   # is called.
   def clear_results
-			@opponents = []
+    @opponents = Array.new
-			@results = []
+    @results   = Array.new
   end
 
   # Add a result to this rating.  Note that no calculation is performed until
   # update() is called.
   def add_result(opponent,result)
-			@results.push( Result.new(opponent.clone, result) )
+    @results << Result.new(opponent.clone, result)
   end
 
   # Add a win result to this rating.  Note that no calculation is performed until
@@ -136,81 +143,82 @@ class Glicko2
     add_result(opponent,Glicko2::DRAW)
   end
 
-		# Update rating based on current results list, and clear results.
+  # util func 'g'
-		def update
-		
-			# util func
   def Glicko2.g(deviation)
     1.0 / (Math.sqrt(1.0 + 3.0 * deviation ** 2.0 / (Math::PI ** 2.0)))
   end
 
-			# util func
+  # util func 'E'
   def Glicko2.E(rating, rating_opponent, deviation_opponent)
     1.0 / (1.0 + Math.exp(-Glicko2.g(deviation_opponent)*(rating - rating_opponent)));
   end
 
-			# bail if no opponents set
+  # Update rating based on current results list, and clear results.
+  def update
+    # Note that if a player does not compete during the rating period, then
+    # only Step 6 applies. 
     if @results.empty?
-				return nil
+      # In this case, the player's rating and volatility parameters remain the
+      # same, but the RD increases according to:
+      @g2deviation = Math.sqrt(@g2deviation**2.0 + @g2volatility**2.0)
+      return
     end
 
-			# compute variance
+    # Step 1. Determine a rating and RD for each player at the onset of the
+    # rating period. The system constant which constrains the change in
+    # volatility over time, needs to be set prior to application of the system.
+    # Reasonable choices are between 0.3 and 1.2, though the system should be
+    # tested to decide which value results in greatest predictive accuracy
+    # ... (ratings already stored in instance)
 
-			#variance = 0.0
+    # Step 2. For each player, convert the ratings and RD's onto the Glicko-2
-			#@results.inject(0.0) do |variance,r|
+    # scale.
-			#	g_i = Glicko2.g(r.opponent.g2deviation)
+    # ... (ratings already stored in G2 format)
-			#	e_i = Glicko2.E(@g2rating,r.opponent.g2rating,r.opponent.g2deviation)
-			#	g_i ** 2.0 * e_i * (1.0 - e_i)
-			#end
 
-			variance = 0.0
+    # Step 3.  Compute the quantity v. This is the estimated variance of the
-			@results.each do |r|
+    # team's/player's rating based only on game outcomes.
+    variance = 1.0 / @results.inject(0.0) do |sum,r|
       g_i = Glicko2.g(r.opponent.g2deviation)
       e_i = Glicko2.E(@g2rating, r.opponent.g2rating, r.opponent.g2deviation)
-				variance += g_i ** 2.0 * e_i * (1.0 - e_i)
+      sum + g_i**2.0 * e_i * (1.0 - e_i)
     end
-			variance = 1.0 / variance
 
-			# compute delta
+    # Step 4. Compute the quantity delta, the estimated improvement in rating
-			delta = 0.0
+    # by comparing the pre-period rating to the performance rating based only
-			@results.each do |r|
+    # on game outcomes.
-				delta += Glicko2.g(r.opponent.g2deviation) * (r.result - Glicko2.E(@g2rating, r.opponent.g2rating, r.opponent.g2deviation))
+    delta = variance * @results.inject(0.0) do |sum,r|
+      sum + Glicko2.g(r.opponent.g2deviation) * (r.result - Glicko2.E(@g2rating, r.opponent.g2rating, r.opponent.g2deviation))
     end
-			delta *= variance
 
-			# determine new volatility
+    # Step 5. Determine the new value of the volatility.
-			new_volatility = 0.0
     a     = Math.log(@g2volatility**2.0)
-			x              = 0.0
+    x0, x = 0.0, a
-			x_new          = a
+    while ((x0 - x).abs > 0.0000001)
-			while ( (x - x_new).abs > 0.0000001 )
+      x0 = x
-				x     = x_new
+      d  = @g2deviation**2.0 + variance + Math.exp(x0)
-				d     = @g2deviation**2.0 + variance + Math.exp(x)
+      h1 = -(x0 - a)/(DVOL**2.0) - 0.5*Math.exp(x0)/d + 0.5*Math.exp(x0)*(delta/d)*(delta/d)
-				h1    = -(x - a)/(@dvolatility**2.0) - 0.5*Math.exp(x)/d + 0.5*Math.exp(x)*(delta/d)*(delta/d)
+      h2 = -1.0/(DVOL**2.0) - 0.5*Math.exp(x0)*(@g2deviation**2.0+variance)/(d**2.0) + 0.5*(delta**2.0)*Math.exp(x0)*((@g2deviation**2.0) + variance - Math.exp(x0))/(d**3.0)
-				h2    = -1.0/(@dvolatility**2.0) - 0.5*Math.exp(x)*(@g2deviation**2.0+variance)/(d**2.0) + 0.5*(delta**2.0)*Math.exp(x)*((@g2deviation**2.0) + variance - Math.exp(x))/(d**3.0)
+      x  = x0 - h1/h2
-				x_new = x - h1/h2
     end
-			new_volatility = Math.exp(x_new / 2.0)
+    new_volatility = Math.exp(x / 2.0)
 
-			# update the rating deviation to the new pre-rating period value
+    # Step 6. Update the rating deviation to the new pre-rating period value.
     pre_deviation = Math.sqrt(@g2deviation**2.0 + new_volatility**2.0)
 
-			# update the rating and deviation
+    # Step 7. Update the rating and RD to the new values.
     new_deviation = 1.0 / (Math.sqrt(1.0/(pre_deviation**2.0) + 1.0 / variance))
-			new_rating    = 0.0
+    new_rating    = @g2rating + new_deviation**2.0 * @results.inject(0.0) do |sum,r|
-			@results.each do |r|
+      sum + Glicko2.g(r.opponent.g2deviation) * (r.result - Glicko2.E(@g2rating, r.opponent.g2rating, r.opponent.g2deviation))
-			    new_rating += Glicko2.g(r.opponent.g2deviation) * (r.result - Glicko2.E(@g2rating, r.opponent.g2rating, r.opponent.g2deviation))
     end
-			new_rating  = new_rating * new_deviation**2.0
-			new_rating += @g2rating
 
-			# wipe our result lists
+    # Step 8. Convert ratings and RD's back to original scale.
-			clear_results
-
-			# copy new values
     @g2deviation  = new_deviation
     @g2volatility = new_volatility
     @g2rating     = new_rating
+
+    # wipe our result lists
+    clear_results
   end
+
 end
 

rb/test_glicko2.rb

@@ -1,6 +1,6 @@
-#!/usr/bin/env ruby
+#!/usr/bin/env ruby -w
 
-require 'glicko2'
+require_relative 'glicko2'
 require 'test/unit'
 
 class TestGlicko2 < Test::Unit::TestCase
@@ -26,7 +26,7 @@ class TestGlicko2 < Test::Unit::TestCase
     a.update()
 
     assert_in_delta(1464.05,a.rating,0.01)
-		assert_in_delta(151.516,a.deviation,0.01)
+    assert_in_delta(151.875,a.deviation,0.01)
 
   end