  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.
