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_posts/research/2020-03-01-soccer-team-vectors.md

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 title: "Soccer Team Vectors"
 categories: research
 tags: machine-learning representation-learning sports-analytics similarity-search
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 *   **Team Ranking:** The embeddings provide a basis for generating data-driven team rankings.
 
 Across these application domains, STEVE demonstrated superior performance compared to competing approaches evaluated in the study. This work provides a valuable tool for quantitative analysis in sports analytics, enabling various machine learning tasks related to team comparison and prediction. For a comprehensive description of the methodology and results, please refer to the publication by {% cite muller2020soccer %}.
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-title:  "Learning Soccer-Team Vecors"
-categories: research 
-excerpt: "Team market value estimation, similarity search and rankings."
-header:
-  teaser: assets/figures/2_steve_algo.jpg
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-In this work we present STEVE - Soccer TEam VEctors, a principled approach for learning real valued vectors for soccer teams where similar teams are close to each other in the resulting vector space. STEVE only relies on freely available information about the matches teams played in the past. These vectors can serve as input to various machine learning tasks. Evaluating on the task of team market value estimation, STEVE outperforms all its competitors. Moreover, we use STEVE for similarity search and to rank soccer teams.
-{% cite muller2020soccer %}
-
-![STEVE Algorithm](\assets\figures\2_steve_algo.jpg){:style="display:block; margin-left:auto; margin-right:auto;"}
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