AI-Driven Race Scheduling Engine

The Challenge: A High-Stakes Balancing Act

Racing Queensland oversees one of the largest and most complex racing calendars in Australia, with over 32 meetings each week across three codes and 120 clubs. Every scheduling decision carries weight, influencing wagering turnover, prize money allocation, horse welfare, and the balance between metropolitan and regional clubs.

The process also lacked transparency, leaving stakeholders uncertain about how schedules were built. Racing Queensland required a system that could predict wagering turnover with accuracy, simulate multiple scenarios at once, and deliver results quickly enough to support strategic decision-making.

Our FDE Approach: Engineering a Living Data Product

We developed an AI-powered scenario engine designed specifically for Racing Queensland. At its core is a machine learning model trained on years of racing and wagering data, capturing the patterns and variables that influence turnover. More than 75 operational factors were integrated into the system, from race type and timing through to geographic spread and historical wagering behaviour.

The solution included:

• Machine learning forecasting capable of predicting turnover for each potential race meeting.
• Scenario engine that generated thousands of candidate schedules and ranked them by financial and fairness metrics.
• Real-time dashboard enabling schedulers to test "what-if" scenarios and view projected outcomes in hours rather than weeks.
• Continuous retraining so that models adapt as new racing and wagering data is recorded.

From Weeks to Hours: Empowering Decision-Makers

Previously, the scheduling process was a bottleneck. Decision-makers had to wait weeks for analysts to manually compile data and model the potential impact of a single calendar change. This slow feedback loop limited strategic exploration and forced a reliance on historical precedent over data-driven forecasting.

Our AI Scenario Engine transforms this workflow. By automating data integration and forecasting, it provides a 'digital twin' of the entire racing ecosystem. Schedulers and executives can now test dozens of 'what-if' scenarios, from adjusting prize money to shifting a race time, and receive a comprehensive financial and logistical impact assessment in a matter of hours, not weeks.

This radical acceleration doesn't just save time; it changes the nature of strategic planning. Instead of a slow, high-stakes annual process, scheduling becomes an agile, iterative dialogue with the data. Decision-makers are empowered to explore more possibilities, understand complex trade-offs instantly, and build optimised, resilient calendars with confidence.