Mastering the Art of Football Simulation

In the realm of sports betting, precision and predictability are paramount. This is particularly true for football, a sport renowned for its intricate tactics and unpredictable outcomes. At DraftKings, the unique challenge of simulating football games is not just a necessity—it's an art form tackled by the brightest in sports data science. Let's dive into how our teams combine ML models and Monte Carlo simulations for one of the most challenging sports to model to fuel our betting markets.

The Complexity of Football Modeling

Football is not only central to our offerings but is also arguably the most complex sport to model. Each play in a football game combines many variables: player positions, play calls, game context, and more, making each simulation a detailed exploration of the countless variables within the game.

The Monte Carlo Play-by-Play Simulation

We employ a Monte Carlo simulation method, simulating football games play-by-play. This approach is also used in other sports, like tennis, because it can incorporate the numerous layers of decision-making in football. The process starts by deciding the type of play—whether a team will pass or run—which is determined by a machine-learning model trained on various situational factors such as the down, field position, and score difference.

All in the Details: The Simulation Process

Once the play type is decided, the simulation branches into further complexities. For example, if the model predicts a pass, it then estimates the yards attempted, and another model determines the outcome—completed, incomplete, intercepted, or a sack. This level of detail is crucial, as each play’s outcome significantly influences subsequent simulations.

The simulation continues beyond just modeling plays. It also accounts for penalties and particular scenarios like fourth downs. Models predict the likelihood of penalties before the snap and their impact, which can drastically alter the course of a game. On fourth downs, decision models evaluate whether the team will likely attempt a conversion, punt, or go for a field goal.

The Bigger Picture

This sophisticated simulation framework supports the creation of accurate betting markets and enhances the customer experience by providing deeper insights into the game's dynamics. The data science team continuously refines these models, using advanced machine learning techniques and extensive historical data to improve accuracy and relevance.

What’s Next?

Future enhancements will focus on integrating more granular data and refining models to reflect the evolving strategies of football teams and operations. In the intricate science of football simulation, our engineers and data scientists play crucial roles in guiding each play to capture the true essence of the game.

Want to know more about our dynamic Engineering team? Learn more about our products and technology on the DraftKings Tech blog!