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<ol class="bibliography"><li><span id="muller2020soccer"><span style="font-variant: small-caps">Müller, R., Langer, S., Ritz, F., Roch, C., Illium, S., and Linnhoff-Popien, C.</span> 2020. Soccer Team Vectors. <i>Machine Learning and Knowledge Discovery in Databases: International Workshops of ECML PKDD 2019, Würzburg, Germany, September 1620, 2019, Proceedings, Part II</i>, Springer International Publishing, 247257.</span>
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<p>This research introduces <strong>STEVE (Soccer Team Vectors)</strong>, 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.</p>
<p>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.</p>
<p>The utility of these learned representations is demonstrated through several downstream applications:</p>
<p><img src="\assets\figures\2_steve_algo.jpg" alt="alt text" style="display:block; width:60%" class="align-right" /></p>
<ul>
<li><strong>Team Market Value Estimation:</strong> The vectors serve as effective features for predicting team market values, outperforming baseline models.</li>
<li><strong>Similarity Search:</strong> The vector space allows for efficient identification of teams similar to a given query team based on proximity.</li>
<li><strong>Team Ranking:</strong> The embeddings provide a basis for generating data-driven team rankings.</li>
</ul>
<p>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 <a class="citation" href="/publications#muller2020soccer">[Müller et al. 2020]</a>.</p>
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<p class="page__date"><strong><i class="fas fa-fw fa-calendar-alt" aria-hidden="true"></i> Updated:</strong> <time class="dt-published" datetime="2020-03-01T00:00:00+01:00">March 1, 2020</time></p>
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