U of T students Juliette Zaccour and Cynthia Yip win Performance and Innovation Awards at Forecasting Competition

A team of University of Toronto students has won two out of three awards at the 2023 CANSSI Ontario French Trot Horse Racing Forecasting Competition: the Performance Award and the Innovation Award. The team included PhD students Cynthia Yip and Juliette Zaccour, both from the Faculty of Information. Juliette Zaccour is now affiliated with the Oxford Internet Institute at the University of Oxford.

As winners of the Performance and Innovation Awards, the team received a combined prize of $8,500. 

Cynthia and Juliette presented a robust, iterative solution for predicting race outcomes, emphasizing data cleaning, feature engineering, and modeling. Their approach involved refining variables, imputing missing values, and crafting insightful features such as race seasonality, relative horse age, preferred surface type, rest period, and past performance metrics for horses, jockeys, and trainers.

Preprocessing steps accounted for both categorical and numerical variables, addressed missing values in engineered features, and made other minor adjustments. The XGBoost algorithm was selected for its balance of performance and efficiency. They focused on optimizing log loss while maintaining transparency through interpretable features.

Model evaluation demonstrated high accuracy (89.3%), though precision and recall were more modest. The model correctly predicted the winner for 631 out of 2,140 races—approximately 29.5% of races. Their solution emphasized real-world adaptability and included a dedicated notebook for testing the model on new datasets.

About the Competition

The French Trot Horse Racing Forecasting Competition, launched by CANSSI Ontario in 2023, invited undergraduates, graduate students and postdoctoral fellows across Ontario to showcase their skills in statistics and machine learning. Participants were tasked to build the best models for accurately predicting French Trot Horse Racing. The competition evaluated submissions across three categories:

• • Performance: Achieving the most accurate winner forecasts on a three-month validation dataset ($5,000).
  • Innovation: Developing novel features, exploring new methodologies, or presenting compelling narratives ($3,500).
  • Best Entry by Undergraduates: Undergraduates were judged in either category—performance or innovation—the award was reserved for the best undergraduate entry ($1,500).
    
    

For more information about the 2023 CANSSI Ontario Forecasting Competition, visit the Competition webpage.

About the University of Toronto

The University of Toronto is one of the world’s leading institutions in research and education, renowned for its innovation and excellence in a wide range of fields, including statistical sciences and computer science.

About CANSSI Ontario

The Canadian Statistical Sciences Institute (CANSSI) Ontario fosters collaboration in the statistical sciences across Canada, supporting research and professional development in the field.>