# Deep Similarity Learning for Sports Team Ranking

arXiv: https://arxiv.org/abs/2103.13736 · HTML: https://mlsanity.com/paper/2103.13736/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 31 references that are in the map.
- First posted on arXiv 25 Mar 2021.

## Builds on (most cited first, 4 of 4)

- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [Evaluating time series forecasting models: An empirical study on performance estimation methods](https://mlsanity.com/paper/1905.11744/) (2019)
- [An Introductory Study on Time Series Modeling and Forecasting](https://mlsanity.com/paper/1302.6613/) (2013)
- [Boosting Standard Classification Architectures Through a Ranking Regularizer](https://mlsanity.com/paper/1901.08616/) (2019)

## Built on by (most cited first, 1 of 1)

- [SUMMIR: A Hallucination-Aware Framework for Ranking Sports Insights from LLMs](https://mlsanity.com/paper/2604.04947/) (2026)

## Abstract (arXiv)

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 rs (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.