How Transformer Models Are Revolutionizing Predictions of Baseball Pitch Outcomes

Summary

This article explores how transformer models are transforming baseball predictions, offering deeper insights into pitch outcomes that can influence game strategy significantly. Key Points:

• Transformer models leverage advanced techniques to reveal hidden pitch dynamics beyond traditional metrics such as velocity and spin rate.
• By generating contextual embeddings for pitcher-batter matchups, these models capture the nuances of interactions and past performances to enhance predictive accuracy.
• Bayesian transformer networks provide uncertainty estimates in pitch selection, allowing managers to assess risks associated with specific strategies.

Key Points Summary

class TransformerModel(nn.Module):     def __init__(self, input_dim, num_heads, num_encoder_layers, hidden_dim, output_dim, sequence_length, dropout=0.1):         super(TransformerModel, self).__init__()                  self.input_dim = input_dim         self.sequence_length = sequence_length                  # Embedding layer to transform input to higher-dimensional space         self.embedding = nn.Linear(input_dim, hidden_dim)                  # Positional encoding to maintain sequence order         self.positional_encoding = PositionalEncoding(hidden_dim, dropout)                  # Transformer encoder layers         encoder_layer = nn.TransformerEncoderLayer(d_model=hidden_dim, nhead=num_heads, dim_feedforward=hidden_dim, dropout=dropout, batch_first=True)         self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_encoder_layers)                  # Final fully connected layers for predictions         self.fc_layers = nn.Sequential(             nn.Linear(2 * hidden_dim, hidden_dim),             nn.ReLU(),             nn.Linear(hidden_dim, output_dim)         )          # Residual connection to keep input features         self.residual_fc = nn.Linear(input_dim, hidden_dim)         self._init_weights()      def forward(self, x):          # Embed the input data         x_emb = self.embedding(x)                  # Add positional encoding to the embedded data         x_emb = self.positional_encoding(x_emb)                  # Pass through transformer encoder layers         x_transformed = self.transformer_encoder(x_emb)                  # Use the last element of the sequence for predictions         x_last = x_transformed[:, -1, :]                  # Use the raw input of the last element in the sequence         x_last_input = x[:, -1, :]                  # Pass the last element through a residual layer         x_last_input_fc = self.residual_fc(x_last_input)                  # Concatenate the transformer output with the residual connection         x_combined = torch.cat((x_last, x_last_input_fc), dim=-1)                  # Final prediction layer         x_out = self.fc_layers(x_combined)                  return x_out

Translate Text to English: A Simple Guide

class PositionalEncoding(nn.Module):     def __init__(self, d_model: int, dropout: float = 0.1, max_len: int = 5000):         super().__init__()         self.dropout = nn.Dropout(p=dropout)          # Create a positional encoding matrix         position = torch.arange(max_len).unsqueeze(1)         div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))         pe = torch.zeros(max_len, 1, d_model)         pe[:, 0, 0::2] = torch.sin(position * div_term)         pe[:, 0, 1::2] = torch.cos(position * div_term)         self.register_buffer('pe', pe)      def forward(self, x: torch.Tensor) -> torch.Tensor:         x = x + self.pe[:x.size(1)].transpose(0, 1)         return self.dropout(x)

class CustomLoss(nn.Module):     def __init__(self, weight_param):         super(CustomLoss, self).__init__()         self.weight_param = weight_param      def forward(self, output, target_continuous, target_categorical):         # Continuous target loss (MSE)         mse_loss = F.mse_loss(output[:, :target_continuous.size(1)], target_continuous)                  # Categorical target loss (Cross-Entropy) for each categorical feature         cross_entropy_loss = 0         start_idx = target_continuous.size(1)         for cat_target in target_categorical:             end_idx = start_idx + cat_target.size(1)             cross_entropy_loss += F.cross_entropy(output[:, start_idx:end_idx], cat_target)             start_idx = end_idx                  # Weighted sum of the losses         loss = (self.weight_param * mse_loss) + ((1 - self.weight_param) * cross_entropy_loss)         return loss

def train_one_epoch(model, train_loader, criterion, optimizer, device):     model.train()     total_loss = 0     for sequence_tensor, cont_target_tensor, cat_target_tensor in train_loader:         # Move data to the appropriate device         sequence_tensor, cont_target_tensor = sequence_tensor.to(device), cont_target_tensor.to(device)         cat_targets = [t.to(device) for t in cat_target_tensor]          optimizer.zero_grad()                  # Forward pass         output = model(sequence_tensor)                  # Compute loss         loss = criterion(output, cont_target_tensor, cat_targets)                  # Backpropagation         loss.backward()         optimizer.step()                  total_loss += loss.item()          return total_loss / len(train_loader)

def evaluate(model, val_loader, criterion, device):     model.eval()     total_loss = 0      with torch.no_grad():         for sequence_tensor, cont_target_tensor, cat_target_tensor in val_loader:             sequence_tensor, cont_target_tensor = sequence_tensor.to(device), cont_target_tensor.to(device)             cat_targets = [t.to(device) for t in cat_target_tensor]                          # Forward pass             output = model(sequence_tensor)                          # Compute loss             loss = criterion(output, cont_target_tensor, cat_targets)                          total_loss += loss.item()      return total_loss / len(val_loader)

def plot_loss(train_losses, val_losses, loss_plot_path):     plt.figure(figsize=(10, 5))     plt.plot(range(1, len(train_losses) + 1), train_losses, label='Train Loss')     plt.plot(range(1, len(train_losses) + 1), val_losses, label='Validation Loss')     plt.xlabel('Epochs')     plt.ylabel('Loss')     plt.legend()     plt.title('Training and Validation Loss')     plt.savefig(loss_plot_path)

def main(train_config_path):     # Load configuration from JSON file     with open(train_config_path, 'r') as f:         train_config = json.load(f)      # Extract parameters from the config     num_heads = train_config["num_heads"]     num_encoder_layers = train_config["num_encoder_layers"]     hidden_dim = train_config["hidden_dim"]     sequence_length = train_config["sequence_length"]     dropout = train_config["dropout"]     batch_size = train_config["batch_size"]     loss_weight_param = train_config["weight_param"]     learning_rate = train_config["learning_rate"]     num_epochs = train_config["num_epochs"]     num_workers = train_config["num_workers"]     model_save_dir = train_config["model_save_dir"]      # File paths     config_path = train_config["config_path"]     train_path = train_config["train_data_path"]     valid_path = train_config["valid_data_path"]      device = torch.device("cuda" if torch.cuda.is_available() else "cpu")     print(f"Device: {device}", flush=True)      # Load the training and validation data     train_data = pd.read_csv(train_path)     valid_data = pd.read_csv(valid_path)      print(f"Train Shape: {train_data.shape}", flush=True)     print(f"Valid Shape: {valid_data.shape}", flush=True)      # Create the datasets and dataloaders     train_dataset = BaseballDataset(train_data, config_path, sequence_length)     valid_dataset = BaseballDataset(valid_data, config_path, sequence_length)      print("Creating Dataloaders", flush=True)     train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=num_workers)     val_loader = DataLoader(valid_dataset, batch_size=batch_size, shuffle=False, num_workers=num_workers)      # Set input and output dimensions based on the dataset     input_dim = train_dataset[0][0].shape[1]     output_dim = train_dataset[0][1].shape[0] + sum([t.shape[0] for t in train_dataset[0][2]])      # Hyperparameter tuning loop     for num_head in num_heads:         for num_encoder_layer in num_encoder_layers:             for h_dim in hidden_dim:                 for drop in dropout:                     for loss_param in loss_weight_param:                         for lr in learning_rate:                             for epochs in num_epochs:                                  print(f"Starting Experiment: nheads-{num_head}, nencoder-{num_encoder_layer}, hdim-{h_dim}, dropout-{drop}, loss weight-{loss_param}, lr-{lr}, epochs-{epochs}", flush=True)                                  # Initialize the model                                 model = nn.DataParallel(TransformerModel(input_dim, num_head, num_encoder_layer, h_dim, output_dim, sequence_length, drop))                                  print(f"# Params: {sum(p.numel() for p in model.parameters() if p.requires_grad)}", flush=True)                                  # Define the loss function and optimizer                                 criterion = CustomLoss(loss_param)                                 optimizer = optim.Adam(model.parameters(), lr=lr)                                  model.to(device)                                  train_losses = []                                 val_losses = []                                  # Training loop                                 for epoch in range(epochs):                                     print(f"Starting epoch: {epoch}", flush=True)                                     train_loss = train_one_epoch(model, train_loader, criterion, optimizer, device)                                     val_loss = evaluate(model, val_loader, criterion, device)                                     train_losses.append(train_loss)                                     val_losses.append(val_loss)                                     print(f"Epoch {epoch + 1}/{epochs}, Train Loss: {train_loss}, Val Loss: {val_loss}", flush=True)                                  # Create directory to save model and plot                                 curr_dir = model_save_dir + f"/h{num_head}_e{num_encoder_layer}_h{h_dim}_d{drop}_lp{loss_param}_lr{lr}_ep{epochs}"                                 os.makedirs(curr_dir, exist_ok=True)                                  model_save_path = os.path.join(curr_dir, 'transformer_model.pth')                                 loss_plot_path = os.path.join(curr_dir, 'loss_plot.png')                                  # Save the trained model                                 torch.save(model.module.state_dict(), model_save_path)                                 print(f"Model saved to {model_save_path}", flush=True)                                  # Plot the training and validation loss                                 plot_loss(train_losses, val_losses, loss_plot_path)                                 print(f"Loss Plot saved to {loss_plot_path}", flush=True)                                  # Save the specific config for this run                                 specific_train_config = copy.deepcopy(train_config)                                 specific_train_config["num_heads"] = num_head                                 specific_train_config["num_encoder_layers"] = num_encoder_layer                                 specific_train_config["hidden_dim"] = h_dim                                 specific_train_config["dropout"] = drop                                 specific_train_config["weight_param"] = loss_param                                 specific_train_config["learning_rate"] = lr                                 specific_train_config["num_epochs"] = epochs                                  config_save_path = os.path.join(curr_dir, 'model_config.json')                                 with open(config_save_path, 'w') as f:                                     json.dump(specific_train_config, f, indent=4)                                 print(f"Config saved to {config_save_path}", flush=True)

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