diff --git a/AlphaGo/.gitignore b/AlphaGo/.gitignore
index 9c2fe16..e578e5a 100644
--- a/AlphaGo/.gitignore
+++ b/AlphaGo/.gitignore
@@ -1,3 +1,4 @@
 data
 checkpoints
 checkpoints_origin
+*.log
diff --git a/AlphaGo/data_statistic.py b/AlphaGo/data_statistic.py
new file mode 100644
index 0000000..6fedf1c
--- /dev/null
+++ b/AlphaGo/data_statistic.py
@@ -0,0 +1,29 @@
+import os
+import cPickle
+
+class Data(object):
+    def __init__(self):
+        self.boards = []
+        self.probs = []
+        self.winner = 0
+
+def file_to_training_data(file_name):
+    with open(file_name, 'rb') as file:
+        try:
+            file.seek(0)
+            data = cPickle.load(file)
+            return data.winner
+        except Exception as e:
+            print(e)
+            return 0
+
+if __name__ == "__main__":
+    win_count = [0, 0, 0]
+    file_list = os.listdir("./data")
+    #print file_list
+    for file in file_list:
+        win_count[file_to_training_data("./data/" + file)] += 1
+    print "Total play : " + str(len(file_list))
+    print "Black wins : " + str(win_count[1])
+    print "White wins : " + str(win_count[-1])
+
diff --git a/AlphaGo/game.py b/AlphaGo/game.py
index 90d0bf0..9fc8fa2 100644
--- a/AlphaGo/game.py
+++ b/AlphaGo/game.py
@@ -62,7 +62,7 @@ class Game:
 
     def think(self, latest_boards, color):
         mcts = MCTS(self.game_engine, self.evaluator, [latest_boards, color], self.size ** 2 + 1, inverse=True)
-        mcts.search(max_step=20)
+        mcts.search(max_step=100)
         temp = 1
         prob = mcts.root.N ** temp / np.sum(mcts.root.N ** temp)
         choice = np.random.choice(self.size ** 2 + 1, 1, p=prob).tolist()[0]