2019/10/16 by Matt Holbrook, Matthew Holbrook, Jennifer Hobbs +4
Computer Science · Economics, Econometrics and Finance · Mathematics · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Sports Analytics and Performance #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1910.07410
arxiv created 2019/10/16 · openalex publication_date 2019/10/16 · arxiv updated 2019/10/17 · openalex created_date 2023/02/28 · openalex updated_date 2026/07/28
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".