Network analysis and link prediction in competitive women's basketball

Network structure and its role in prediction are examined in competitive basketball at the team and player levels. Adversarial game outcome networks from NCAA Division I women’s basketball from 2021 to 2024 are used to compute the common out-neighbor score and PageRank, which are combined into a low-key leader strength that identifies competitors influential through structural similarity despite relatively low centrality. This measure is related to changes in NCAA NET rankings by grouping teams into quantiles and comparing average rank changes across seasons for both previous-to-current and current-to-next transitions. Link prediction is then studied using node2vec embeddings across three interaction settings. For NCAA regular-season game networks, cosine similarity between team embeddings is used in a logistic regression model to predict March Madness matchups. For WNBA shot-blocking networks, future directed blocking interactions are predicted via logistic regression on concatenated source-target player embeddings. For WNBA passing networks, region embeddings learned from first-quarter passes are evaluated for their ability to predict subsequent passing connections. Across NCAA and WNBA settings, embedding-based models provide statistically significant evidence that higher-order network structure contains predictive signals for future interactions, while the passing experiment shows weaker predictive performance but yields interpretable similarity patterns consistent with passing feasibility.

💡 Research Summary

The paper investigates how network‑based metrics and embedding techniques can improve outcome prediction in competitive women’s basketball at both the team and player levels. Using adversarial game‑outcome networks constructed from NCAA Division I women’s basketball (2021‑2024) and two WNBA interaction datasets (shot‑blocking and passing), the authors first compute two classic centrality measures: the Common Out‑Neighbor (CON) score, which counts how many opponents a team shares as defeated rivals, and PageRank, applied to the reversed‑edge graph. By normalizing both scores to the