Add elo catch up and many

Author: Meeeds

Elo-Catch-Up.py

@@ -0,0 +1,63 @@
+import random
+#https://github.com/ddm7018/Elo
+from elosports.elo import Elo
+import sys
+import matplotlib.pyplot as plt
+import statistics
+from matplotlib.pyplot import cm
+import numpy as np
+
+
+base_elo = 1000
+real_elo  = 2000
+nb_match = 500
+player0 = "0"
+player1 = "1"
+color = iter(cm.rainbow(np.linspace(0, 1, len(range (0,7)))))
+
+
+x_data=[]
+y_data=[]
+#plot target
+for i in range(1,nb_match):
+    x_data.append(i)
+    y_data.append(real_elo)
+    
+plt.plot(x_data, y_data, c=next(color), label='target=%d elo' % real_elo)
+
+
+for k_factor in (2**(i/1.0) for i in range (0,6)):
+    eloLeague = Elo(k = k_factor , homefield = 0)
+    eloLeague.addPlayer(player0, rating = base_elo)
+    eloLeague.addPlayer(player1, rating = base_elo)
+    x_data=[]
+    y_data=[]
+    
+
+    for i in range(1,nb_match):
+        current_guy_elo = eloLeague.ratingDict[player0]
+        current_proba = eloLeague.expectResult(real_elo,current_guy_elo)
+        print("Start match %d, current_guy_elo=%.2f, current_proba=%.2f%%" % (i , current_guy_elo, 100*current_proba ))
+        if random.uniform(0, 1) < current_proba :
+            eloLeague.gameOver(player0, player1,  winnerHome = True)
+        else:
+            eloLeague.gameOver(player1, player0,  winnerHome = True)
+            
+        #update oponent to current elo
+        #print("before", eloLeague.ratingDict[player0],eloLeague.ratingDict[player1])
+        eloLeague.ratingDict[player1]=eloLeague.ratingDict[player0]
+        #print("after", eloLeague.ratingDict[player0],eloLeague.ratingDict[player1])
+        x_data.append(i)
+        y_data.append(eloLeague.ratingDict[player1])
+    
+    plt.plot(x_data, y_data, c=next(color), label='k_factor=%.2f' % k_factor)
+        
+        
+plt.title("Catch up to real_elo=%d" % real_elo)
+plt.xlabel("matches")
+plt.ylabel("elo")
+plt.legend(loc='best')
+plt.show()
+    
+
+

elo-et-variation.py

@@ -0,0 +1,51 @@
+import random
+#https://github.com/ddm7018/Elo
+from elosports.elo import Elo
+import sys
+import matplotlib.pyplot as plt
+import statistics
+from matplotlib.pyplot import cm
+import numpy as np
+
+
+base_elo = 1000
+nb_round_robin = 3
+k_factor = 32
+#Number of bins in the histogram
+
+
+
+iter_nb_players = range(200,2400,1000)
+color = iter(cm.rainbow(np.linspace(0, 1, len(iter_nb_players))))
+
+for nb_players in iter_nb_players:
+    print("Computing ecart_t for nb_players=%d" %(nb_players))
+    eloLeague = Elo(k = k_factor , homefield = 0)
+    x_data=[]
+    y_data=[]
+    for i in range(nb_players):
+        eloLeague.addPlayer(str(i), rating = base_elo)
+        
+    for r in range(nb_round_robin):
+        print("Running round %d/%d k=%d" %(r+1,nb_round_robin,k_factor))
+        for i in range(nb_players):
+            for j in range(nb_players):
+                if i != j :
+                    if random.randrange(2)==0:
+                        eloLeague.gameOver(winner  = str(i), loser = str(j),  winnerHome = True)
+                    else:
+                        eloLeague.gameOver(loser  = str(i), winner = str(j),  winnerHome = True)
+        current_ecart_t = statistics.stdev((eloLeague.ratingDict.values()))
+        print("\tAfter round %d/%d elo ecart_t=%f" % (r+1,nb_round_robin,current_ecart_t))
+        x_data.append(r)
+        y_data.append(current_ecart_t)
+        
+    plt.plot(x_data, y_data, '--.', c=next(color), label='nb_players=%d' % nb_players)
+
+
+# Plotting the Graph
+plt.title("Evolution of ET per round for different nb_players")
+plt.xlabel("round")
+plt.ylabel("ecart-type")
+plt.legend(loc='best')
+plt.show()

elo-et.py

@@ -0,0 +1,47 @@
+import random
+#https://github.com/ddm7018/Elo
+from elosports.elo import Elo
+import sys
+import matplotlib.pyplot as plt
+import statistics
+
+
+nb_players = 1000
+base_elo = 1000
+nb_round_robin = 6
+
+x_data=[]
+y_data=[]
+
+
+for k_factor in (2**(i/2.0) for i in range (0,15)):
+    print("Computing ecart_t for k=%.2f" %(k_factor))
+    eloLeague = Elo(k = k_factor , homefield = 0)
+    average_ecart_t = 0
+    for i in range(nb_players):
+        eloLeague.addPlayer(str(i), rating = base_elo)
+        
+    for r in range(nb_round_robin):
+        print("Running round %d/%d k=%.2f" %(r+1,nb_round_robin,k_factor))
+        for i in range(nb_players):
+            for j in range(nb_players):
+                if i != j :
+                    if random.randrange(2)==0:
+                        eloLeague.gameOver(winner  = str(i), loser = str(j),  winnerHome = True)
+                    else:
+                        eloLeague.gameOver(loser  = str(i), winner = str(j),  winnerHome = True)
+        current_ecart_t = statistics.stdev((eloLeague.ratingDict.values()))
+        average_ecart_t+=current_ecart_t
+        print("\tAfter round %d/%d elo ecart_t=%.2f" % (r+1,nb_round_robin,current_ecart_t))
+        
+    average_ecart_t = average_ecart_t/nb_round_robin
+    x_data.append(k_factor)
+    y_data.append(average_ecart_t)
+
+
+# Plotting the Graph
+plt.plot(x_data, y_data, '--bo')
+plt.title("Evolution of ET versus K")
+plt.xlabel("K")
+plt.ylabel("ecart_t")
+plt.show()

elo.py

@@ -31,10 +31,6 @@
     print("\tAfter round %d/%d elo ecart_t=%f" % (r+1,nb_round_robin,statistics.stdev((eloLeague.ratingDict.values()))))
 
 
-
-#for team in eloLeague.ratingDict.keys():
-#    print(team, eloLeague.ratingDict[team])
-    
 elo_list=eloLeague.ratingDict.values()
 hist_bins = []
 current = min(elo_list)

random-match.py

@@ -0,0 +1,54 @@
+import random
+#https://github.com/ddm7018/Elo
+from elosports.elo import Elo
+import sys
+import matplotlib.pyplot as plt
+import statistics
+from matplotlib.pyplot import cm
+import numpy as np
+
+nb_players = 2000
+matches_ratio = 0.01
+nb_matches = nb_players * nb_players * matches_ratio
+base_elo = 1000
+plot_step = 10
+print_step = plot_step*100
+k_factor = 32
+number_of_curve = 4
+
+#Number of bins in the histogram
+color = iter(cm.rainbow(np.linspace(0, 1, number_of_curve)))
+
+for iteration in range(number_of_curve):
+    print("Iteration %d "% iteration)
+    eloLeague = Elo(k = k_factor , homefield = 0)
+    x_data=[]
+    y_data=[]
+    for i in range(nb_players):
+        eloLeague.addPlayer(str(i), rating = base_elo)
+        
+    for m in range(int(nb_matches)):
+        i,j = random.sample(range(nb_players), 2)
+        if random.randrange(2)==0:
+            eloLeague.gameOver(winner  = str(i), loser = str(j),  winnerHome = True)
+        else:
+            eloLeague.gameOver(loser  = str(i), winner = str(j),  winnerHome = True)
+            
+            
+        if m % plot_step == 0 or m < nb_matches * 0.4 :
+            current_ecart_t = statistics.stdev((eloLeague.ratingDict.values()))
+            x_data.append(m)
+            y_data.append(current_ecart_t)
+            if m % print_step == 0:
+                print("\tAfter nb_matches %d/%d=%.2f%% elo ecart_t=%.2f" % (m+1,nb_matches, 100.0*m/nb_matches, current_ecart_t))
+            
+        
+    plt.plot(x_data, y_data, c=next(color), label='iteration=%d' % iteration)
+
+
+# Plotting the Graph
+plt.title("Evolution of ET per matches nb_players=%d, plot_step=%d" % (nb_players, plot_step))
+plt.xlabel("matches")
+plt.ylabel("ecart-type")
+plt.legend(loc='best')
+plt.show()