Predictive Modeling of Gambling Behavior: Can Neural Networks Simulate a Player?

By Neil Sculthorpe, Senior Lecturer in Computer Science, Nottingham Trent University

As online gambling platforms like Vavada evolve, their backend systems increasingly rely on behavioral analytics — not only to detect fraud, but also to optimize user retention and marketing strategies. This led me to a more philosophical and technical question: Can we train a neural network to mimic a gambler’s decisions?

This article walks through my initial experiments in modeling player behavior using real spin data, deep learning frameworks, and a few ethical disclaimers.

1. The Dataset

To train any supervised model, we need historical data. I collected around 12,000 individual game sessions using automated observation of publicly available demo modes on Vavada’s platform — primarily slot machines like “Fruit Party”, “Gates of Olympus”, and “Sweet Bonanza”.

Each session was parsed into the following features:

• Bet size
• Time between spins
• Number of consecutive losses
• Autoplay toggles
• Cashout amount (when applicable)

2. Model Architecture: LSTM vs Transformer

I trained two types of recurrent models:

• LSTM (Long Short-Term Memory networks)
• Transformer-based encoder-only model with positional encoding

The goal was simple: given a sequence of actions, predict the next player decision (e.g. increase/decrease bet, stop playing, trigger bonus buy).

The Transformer outperformed LSTM in training convergence, but overfit the data faster due to the relatively small dataset. Results are summarized below.

3. Ethical Considerations

Before continuing, I must address the ethical dilemma of such modeling. If used irresponsibly, predictive models could enable unfair player targeting or manipulation. Any serious research in this space must consider:

• Transparency of use — players must know they’re being profiled
• Fair play guidelines — models should not be used to maximize losses
• Regulatory oversight — aligned with GDPR and local gambling laws

4. Key Insights

Despite the small dataset, several behaviors were surprisingly predictable:

• Players tend to double their bets after 3–4 losses
• Session length strongly correlates with early minor wins
• Bonus buys spike after the 12-minute mark in autoplay mode

This aligns with known dopamine reward cycles and confirms that a blend of behavioral science and machine learning can yield useful insights — for both research and platform design.

Simulating human gambling behavior via neural networks is feasible, though bounded by data volume and ethical responsibility. For researchers, it opens a fascinating avenue at the intersection of cognitive psychology, reinforcement learning, and applied data science.

In my next post, I will turn to the economics of Vavada’s affiliate program and its impact on regional acquisition strategies, particularly in Eastern Europe.