23 lines
1.9 KiB
Markdown
23 lines
1.9 KiB
Markdown
---
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layout: single
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title: "Soccer Team Vectors"
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categories: research
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tags: machine-learning representation-learning sports-analytics similarity-search
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excerpt: "STEVE learns soccer team embeddings from match data for analysis."
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header:
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teaser: /assets/figures/2_steve_algo.jpg
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scholar_link: "https://scholar.google.de/citations?user=NODAd94AAAAJ&hl=en"
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---
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This research introduces **STEVE (Soccer Team Vectors)**, a novel methodology for learning meaningful, real-valued vector representations (embeddings) for professional soccer teams. The primary goal is to capture intrinsic team characteristics and relationships within a continuous vector space, such that teams with similar playing styles, strengths, or performance levels are positioned closely together.
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Leveraging widely available public data from soccer matches (e.g., results, possibly performance statistics), STEVE employs machine learning techniques to generate these low-dimensional team vectors.
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The utility of these learned representations is demonstrated through several downstream applications:
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{:style="display:block; width:60%" .align-right}
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* **Team Market Value Estimation:** The vectors serve as effective features for predicting team market values, outperforming baseline models.
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* **Similarity Search:** The vector space allows for efficient identification of teams similar to a given query team based on proximity.
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* **Team Ranking:** The embeddings provide a basis for generating data-driven team rankings.
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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 %}. |