III Liga Group 2 stats & predictions

Team A vs Team B

Analysts predict a tight match, with a slight edge towards Team A due to their defensive solidity. A draw seems plausible, making a bet on over/under goals an interesting option.

• Prediction: Team A to win or draw
• Bet Tip: Over 1.5 goals

Team C vs Team D

This match could go either way, but Team C’s ability to control the game might give them the upper hand. Expect a low-scoring affair with few chances on goal.

• Prediction: Team C to win by a narrow margin
• Bet Tip: Both teams to score - No

Team E vs Team F

The home ground advantage for Team E is significant, but Team F’s recent resurgence makes them dangerous visitors. Look out for a late goal that could decide the outcome.

• Prediction: Draw or Team E wins
• Bet Tip: Correct score 1-1 or 2-1 (either way)

Team A’s Defensive Strategy

To counteract Team B’s aggressive forwards, Team A will likely employ a compact defensive line, focusing on quick transitions to catch opponents off guard.

• Tactic: High pressing during opponent possession
• Aim: To disrupt rhythm and regain possession quickly

Team C’s Possession Play

Team C will aim to dominate possession and dictate the pace of the game. Their midfielders will be pivotal in maintaining control and distributing accurate passes.

• Tactic: Short passing and maintaining shape
• Aim: To limit space and opportunities for counter-attacks by Team D

Team F’s Counter-Attacking Style

Leveraging speed on the break, Team F will focus on absorbing pressure and exploiting any gaps left by an advancing opponent defense.

• Tactic: Quick transitions from defense to attack
• Aim: To capitalize on mistakes by Team E’s defenders

Past Performances: Insights from Previous Meetings

• Last season saw a closely contested match between Team A and Team B that ended 1-1, highlighting their evenly matched capabilities.
• In their last encounter, Team C managed a narrow victory over Team D with a single goal that came late in the match.
• The rivalry between Team E and Team F has seen fluctuating results, with each team having secured victories at home grounds.

This historical context provides valuable insights into potential outcomes and strategies that might be employed based on past experiences.

The Role of Fans: How Can You Get Involved?

Fans play an integral role in boosting team morale and creating an electrifying atmosphere at matches. Here are ways you can support your favorite teams from afar:

• Social Media Engagement: Share your thoughts and predictions online using hashtags related to each team or match. This not only shows support but also engages you with other fans worldwide.
• Voice Your Support: Participate in fan forums or watch parties organized by local communities or online platforms to experience the games collectively.
• Celebrate Unique Traditions: Embrace any unique chants or traditions associated with your favorite team as they play their part in enhancing team spirit.
• Create Matchday Rituals: Set up pre-game rituals like wearing team colors or preparing themed snacks to add excitement leading up to kick-off time.

  Fan Favorite Moments: Memorable Plays from Last Season's Group 2 Matches

  Last season was filled with unforgettable moments that showcased skillful plays and thrilling finishes. Reflecting on these highlights offers anticipation for what tomorrow may bring:

  • An incredible bicycle kick by Player W from Team B remains etched in fans' memories as one of last season's most jaw-dropping goals.
  • A last-minute equalizer by Player V from Team D against formidable opponents demonstrated resilience and determination under pressure.
  • A stunning long-range strike by Player U from Team E set social media ablaze with praise for its precision and power.
  • A brilliant save by Goalkeeper T from Team F became viral footage that underscored his critical role in keeping his team competitive throughout matches. <|repo_name|>kunihiko-matsuda/Python<|file_sep|>/ReinforcementLearning/RL_04.py # -*- coding: utf-8 -*- """ Created on Tue Apr 14 15:45:43 2020 @author: Kunihiko Matsuda """ import gym import numpy as np # import matplotlib.pyplot as plt env = gym.make("FrozenLake-v0") env.seed(0) np.random.seed(0) print(env.observation_space) print(env.action_space) print(env.observation_space.n) print(env.action_space.n) state_size = env.observation_space.n action_size = env.action_space.n print(state_size) print(action_size) # Q-tableを初期化する Q_table = np.zeros((state_size, action_size)) print(Q_table.shape) print(Q_table) # Q-tableの初期化を確認 for s in range(state_size): for a in range(action_size): if Q_table[s][a] != 0: print("Error") # Q-tableを学習する # 初期値設定 learning_rate = 0.8 discount_rate = 0.95 num_episodes = 2000 for episode in range(num_episodes): state = env.reset() done = False while not done: action = np.argmax(Q_table[state]) next_state,reward,done,_ = env.step(action) Q_table[state][action] += learning_rate * (reward + discount_rate * np.max(Q_table[next_state]) - Q_table[state][action]) state = next_state # 学習結果を表示する for s in range(state_size): for a in range(action_size): print("{0:.2f}".format(Q_table[s][a]), end="t") print() # 結果を確認する num_tests = 100 successes = 0 for test in range(num_tests): state = env.reset() done = False while not done: action = np.argmax(Q_table[state]) state,reward,done,_ = env.step(action) if reward == 1: successes += 1 print("Success rate:{0:.2f}%".format(successes/num_tests *100)) <|repo_name|>kunihiko-matsuda/Python<|file_sep|>/DeepLearning/DL_05.py # -*- coding: utf-8 -*- """ Created on Tue Feb 25 14:31:35 2020 @author: Kunihiko Matsuda """ import tensorflow as tf mnist = tf.keras.datasets.mnist (x_train,y_train),(x_test,y_test) = mnist.load_data() x_train,x_test = x_train/255.0,x_test/255.0 model = tf.keras.models.Sequential([ tf.keras.layers.Flatten(input_shape=(28,28)), tf.keras.layers.Dense(512,kernel_initializer="glorot_normal",activation=tf.nn.relu), tf.keras.layers.Dense(256,kernel_initializer="glorot_normal",activation=tf.nn.relu), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(10,kernel_initializer="glorot_normal",activation=tf.nn.softmax) ]) model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy']) history=model.fit(x_train,y_train,batch_size=10000,num_epochs=20) model.evaluate(x_test,y_test)<|file_sep|># -*- coding: utf-8 -*- """ Created on Wed Feb 19 10:38:18 2020 @author: Kunihiko Matsuda """ import numpy as np import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data mnist=input_data.read_data_sets('MNIST_data',one_hot=True) train_x=mnist.train.images # Returns np.array train_y=mnist.train.labels # Returns np.array test_x=mnist.test.images # Returns np.array test_y=mnist.test.labels # Returns np.array n_input=784 # MNIST data input (img shape: 28*28) n_classes=10 # MNIST total classes (0-9 digits) dropout=0.75 # Dropout rate (probability to keep units) def conv_net(x_dict,input_dim,n_classes,dropout): x=x_dict['images'] with tf.name_scope('reshape'): x=tf.reshape(x,input_dim) with tf.name_scope('conv1'): W=tf.Variable(tf.truncated_normal([5,5,input_dim[-1],32],stddev=5e-2)) b=tf.Variable(tf.constant(0.01,shape=[32])) x=tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME') x=tf.nn.relu(x+b) with tf.name_scope('pool1'): x=tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME') with tf.name_scope('conv2'): W=tf.Variable(tf.truncated_normal([5,5,input_dim[-1],64],stddev=5e-2)) b=tf.Variable(tf.constant(0.01,shape=[64])) x=tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME') x=tf.nn.relu(x+b) with tf.name_scope('pool2'): x=tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME') with tf.name_scope('fc'): W=tf.Variable(tf.truncated_normal([7*7*64,n_classes],stddev=5e-2)) b=tf.Variable(tf.constant(0.01,size=[n_classes])) x=tf.reshape(x,[input_dim[0],-1]) x=tf.matmul(x,W)+b with tf.name_scope('dropout'): x=tf.nn.dropout(x,.75) return x x_dict={ 'images':tf.placeholder(tf.float32,[None,n_input])} y_true=tf.placeholder(tf.float32,[None,n_classes]) pred=conv_net(x_dict,{ 'images':tf.placeholder(tf.float32,[None,n_input,n_input//28,n_input//28])},n_classes,dropout) cost=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred,y=y_true)) optimizer=tf.train.AdamOptimizer().minimize(cost) correct_pred=tf.equal(tf.argmax(pred,-1),tf.argmax(y_true,-1)) accuracy=tf.reduce_mean(tf.cast(correct_pred,'float')) init_op=tf.global_variables_initializer() batch_size=128 with tf.Session() as sess: sess.run(init_op) for i in range(20000): batch_x,batch_y=mnist.train.next_batch(batch_size) sess.run(optimizer,{x_dict['images']:batch_x,y_true:batch_y}) if i %100 ==0: temp_acc=sess.run(accuracy,{x_dict['images']:batch_x,y_true:batch_y}) print("step:",i," training accuracy:",temp_acc) test_acc=sess.run(accuracy,{x_dict['images']:test_x,y_true:test_y}) print("Testing Accuracy:",test_acc)<|repo_name|>kunihiko-matsuda/Python<|file_sep|>/ReinforcementLearning/RL_05.py # -*- coding: utf-8 -*- """ Created on Mon Apr 20 13:45:29 2020 @author: Kunihiko Matsuda """ import gym import numpy as np env=gym.make("CartPole-v0") env.seed(0) np.random.seed(0) print(env.observation_space.shape[0]) print(env.action_space.n) state_size=env.observation_space.shape[0] action_size=env.action_space.n class CartPoleAgent: def __init__(self,state_size,alpha,gamma,episodes,batch_size): self.state_size=int(state_size) self.action_size=int(action_size) self.alpha=float(alpha) self.gamma=float(gamma) self.episodes=int(episodes) self.batch_size=int(batch_size) def build_model(self): import tensorflow.compat.v1 as tf tf.disable_v2_behavior() from tensorflow.keras import Model,layers self.model=Model() def remember(self,state,target_q_value): import random if len(self.memory) == self.batch_size: del self.memory[0] def act(self,state): import random if np.random.rand() <= self.epsilon: return random.randrange(self.action_size) def replay(self): import random minibatch=random.sample(self.memory,self.batch_size) states=[] targets=[] for state,target_q_value in minibatch: states.append(state[0]) targets.append(target_q_value) states=np.array(states).astype('float32') targets=np.array(targets).astype('float32') predicted_q_values=self.model.predict(states,batch_size=self.batch_size) for i,(state,target_q_value) in enumerate(minibatch): predicted_q_values[i][action]=target_q_value self.model.fit(states,predicted_q_values,batch_size=self.batch_size) agent=CartPoleAgent(state_size,alpha,gamma,episodes,batch_size) agent.build_model() for epiode in range(episodes): state=env.reset() done=False while not done: env.render() action=agent.act(state) next_state,reward,_done,_info=env.step(action) done=_done or step_count>=200 if done: reward=-10 target_q_value=reward+gamma*np.amax(agent.model.predict(next_state.reshape(-1,state_shape))[0]) else: target_q_value=reward+gamma*np.amax(agent.model.predict(next_state.reshape(-1,state_shape