Rugby-Bot: Utilizing Multi-Task Learning & Fine-Grained Features for Rugby League Analysis
This work addresses the need for consistent multi-prediction analysis in sports analytics, specifically for rugby league, but it appears incremental as it applies known techniques to a new domain.
The authors tackled the problem of generating consistent multiple predictions for complex sporting events by developing a multi-task learning method that uses fine-grained spatial data and a wide-and-deep learning approach, resulting in a system called Rugby-Bot that predicts distributions rather than single values for rugby league analysis.
Sporting events are extremely complex and require a multitude of metrics to accurate describe the event. When making multiple predictions, one should make them from a single source to keep consistency across the predictions. We present a multi-task learning method of generating multiple predictions for analysis via a single prediction source. To enable this approach, we utilize a fine-grain representation using fine-grain spatial data using a wide-and-deep learning approach. Additionally, our approach can predict distributions rather than single point values. We highlighted the utility of our approach on the sport of Rugby League and call our prediction engine "Rugby-Bot".