Deep Similarity Learning for Sports Team Ranking

TL;DR

This paper explores the use of Siamese Neural Networks combined with LightGBM and XGBoost, employing triplet loss, to improve sports team ranking accuracy in Rugby and Basketball, achieving state-of-the-art results.

Contribution

It introduces a novel combination of SNN with gradient boosting models using triplet loss for sports ranking, demonstrating superior performance over traditional methods.

Findings

Triplet loss enhances ranking performance.

LightGBM (Triplet loss) achieves state-of-the-art in NBA ranking.

SNN (Triplet loss) outperforms in Rugby ranking.

Abstract

Sports data is more readily available and consequently, there has been an increase in the amount of sports analysis, predictions and rankings in the literature. Sports are unique in their respective stochastic nature, making analysis, and accurate predictions valuable to those involved in the sport. In response, we focus on Siamese Neural Networks (SNN) in unison with LightGBM and XGBoost models, to predict the importance of matches and to rank teams in Rugby and Basketball. Six models were developed and compared, a LightGBM, a XGBoost, a LightGBM (Contrastive Loss), LightGBM (Triplet Loss), a XGBoost (Contrastive Loss), XGBoost (Triplet Loss). The models that utilise a Triplet loss function perform better than those using Contrastive loss. It is clear LightGBM (Triplet loss) is the most effective model in ranking the NBA, producing a state of the art (SOTA) mAP (0.867) and NDCG (0.98) respectively. The SNN (Triplet loss) most effectively predicted the Super 15 Rugby, yielding the SOTA mAP (0.921), NDCG (0.983), and $r_s$ (0.793). Triplet loss produces the best overall results displaying the value of learning representations/embeddings for prediction and ranking of sports. Overall there is not a single consistent best performing model across the two sports indicating that other Ranking models should be considered in the future.