Statistical Models Interactive

Neural Networks

Deep learning for sports betting. Learn complex patterns from data, but watch for overfitting—small betting datasets are challenging.

📊 Neural Network Basics

📥

Input Layer

Features: stats, odds, matchups

🧠

Hidden Layers

Learn complex patterns

📤

Output Layer

Prediction: prob, points, etc.

Architecture

1 5

8 128

0 0.5

Training

0.001 0.1

50 200

📊 Model Info

Parameters ~2,048

Best Epoch 92

Overfit Risk Medium

Training Curve

✓ Good convergence. Training and validation tracking well.

Network Architecture

Input

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+26

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Output

🧠 Architectures for Betting

MLP

Dense layers, good for tabular data

Ex: Player projections

RNN/LSTM

Sequential data, memory

Ex: Streak prediction

Embedding

Learn player/team representations

Ex: Collaborative filtering

Attention

Variable-length sequences

Ex: Play-by-play analysis

R / Python Code

# Keras (via reticulate or keras R package)
library(keras)

model <- keras_model_sequential() %>%
  layer_dense(units = 32, activation = "relu", input_shape = c(n_features)) %>%
  layer_dropout(rate = 0.2)
  
# Add hidden layers
for (i in 1:1) { 
  model <- model %>%
    layer_dense(units = 32, activation = "relu") %>%
    layer_dropout(rate = 0.2)
}

# Output layer
model <- model %>%
  layer_dense(units = 1, activation = "sigmoid")

model %>% compile(
  optimizer = optimizer_adam(learning_rate = 0.01),
  loss = "binary_crossentropy",
  metrics = c("accuracy")
)

# Train with early stopping
history <- model %>% fit(
  x_train, y_train,
  epochs = 100,
  validation_split = 0.2,
  callbacks = list(callback_early_stopping(patience = 10))
)

✅ Key Takeaways

• Neural nets learn complex nonlinear patterns
• Prone to overfitting with small datasets
• Use dropout and early stopping

• Start simple—often XGBoost beats NNs on tabular
• Embeddings useful for categorical features
• LSTMs for time series (streaks, form)