26 lines
2.0 KiB
Plaintext
26 lines
2.0 KiB
Plaintext
---
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title: "Soccer Team Vectors"
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tags: [machine-learning, representation-learning, sports-analytics, similarity-search, soccer, embeddings, team-performance, prediction]
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excerpt: "STEVE learns soccer team embeddings from match data for analysis."
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venue: "ECML PKDD 2019"
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teaser: "/figures/2_steve_algo.jpg"
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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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<FloatingImage
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src="/figures/2_steve_algo.jpg"
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alt="Diagram of the STEVE methodology showing data input, model training, and applications like similarity search and ranking."
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width={1024}
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height={768}
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float="right"
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/>
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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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* **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. <Cite bibtexKey="muller2020soccer" /> |