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        <datestamp>2026-09-18T18:01:22Z</datestamp>
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        <oai_dc:dc xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"  xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
          <dc:title>&lt;p&gt;Home advantage by season.&lt;/p&gt;</dc:title>
          <dc:creator>John David Johnson II (25073610)</dc:creator>
          <dc:creator>Michael Hales (16071776)</dc:creator>
          <dc:creator>Joshua Pitts (25073613)</dc:creator>
          <dc:subject>Cell Biology</dc:subject>
          <dc:subject>Evolutionary Biology</dc:subject>
          <dc:subject>Environmental Sciences not elsewhere classified</dc:subject>
          <dc:subject>Sociology</dc:subject>
          <dc:subject>Biological Sciences not elsewhere classified</dc:subject>
          <dc:subject>Science Policy</dc:subject>
          <dc:subject>teams changed formations</dc:subject>
          <dc:subject>routine contextual factor</dc:subject>
          <dc:subject>descriptive associations rather</dc:subject>
          <dc:subject>cramer &amp;# 8217</dc:subject>
          <dc:subject>4 &amp;# 8211</dc:subject>
          <dc:subject>2017 &amp;# 8211</dc:subject>
          <dc:subject>2 &amp;# 8211</dc:subject>
          <dc:subject>wide formation distributions</dc:subject>
          <dc:subject>retrospective observational study</dc:subject>
          <dc:subject>analyzed using chi</dc:subject>
          <dc:subject>hybrid category represented</dc:subject>
          <dc:subject>formation usage patterns</dc:subject>
          <dc:subject>3 &amp;# 8211</dc:subject>
          <dc:subject>identifiable surface ),</dc:subject>
          <dc:subject>selected performance metrics</dc:subject>
          <dc:subject>formation usage</dc:subject>
          <dc:subject>performance metrics</dc:subject>
          <dc:subject>study evaluated</dc:subject>
          <dc:subject>starting formation</dc:subject>
          <dc:subject>formation findings</dc:subject>
          <dc:subject>001 ),</dc:subject>
          <dc:subject>attacking performance</dc:subject>
          <dc:subject>surface type</dc:subject>
          <dc:subject>playing surface</dc:subject>
          <dc:subject>home surface</dc:subject>
          <dc:subject>using 6</dc:subject>
          <dc:subject>tactical behavior</dc:subject>
          <dc:subject>strong overall</dc:subject>
          <dc:subject>square tests</dc:subject>
          <dc:subject>single venue</dc:subject>
          <dc:subject>significant hybrid</dc:subject>
          <dc:subject>professional football</dc:subject>
          <dc:subject>match outcomes</dc:subject>
          <dc:subject>match observations</dc:subject>
          <dc:subject>match location</dc:subject>
          <dc:subject>level data</dc:subject>
          <dc:subject>home advantage</dc:subject>
          <dc:subject>goal scoring</dc:subject>
          <dc:subject>findings indicate</dc:subject>
          <dc:subject>differ significantly</dc:subject>
          <dc:description>&lt;div&gt;&lt;p&gt;Playing surface is a routine contextual factor in professional football, yet its association with tactical behavior and match outcomes remains unclear. The aim of this retrospective observational study was to examine whether playing surface is associated with starting formation, performance metrics, and match outcomes in professional football. Using 6,764 team-match observations from 3,383 Major League Soccer (MLS) matches (2017–2024; 6,761 team-match observations with identifiable surface), this study evaluated how surface type (grass, turf, hybrid) is associated with formation usage, performance metrics, and match outcomes. Team-level data were analyzed using chi-square tests, ANOVA, and logistic regression models incorporating surface type, match location, and their interaction. Surface type was not significantly associated with match outcomes (χ²(4) = 7.71, p = 0.103; Cramer’s V = 0.034), suggesting similar competitive outcomes across modern playing surfaces. However, differences in league-wide formation distributions and aspects of attacking performance were observed across surface categories. Formation usage varied significantly by surface (χ², p &lt; 0.001), with the 4–3–3 more common on grass and the 4–2–3–1 more prevalent on turf. Modest differences in goal scoring were observed, while possession remained largely unchanged. Home advantage was strong overall and did not differ significantly between grass and turf; a significant hybrid-by-location interaction was observed, although the hybrid category represented a single venue and should not be generalized. These findings indicate that playing surface was not meaningfully associated with competitive outcomes but was associated with differences in league-wide formation distributions and selected performance metrics. Because home surface is closely tied to club identity and the observational design cannot establish tactical adaptation, formation findings should be interpreted as descriptive associations rather than evidence that teams changed formations in response to surface conditions.&lt;/p&gt;&lt;/div&gt;</dc:description>
          <dc:date>2026-09-18T18:01:10Z</dc:date>
          <dc:type>Dataset</dc:type>
          <dc:type>Dataset</dc:type>
          <dc:identifier>10.1371/journal.pone.0358364.t004</dc:identifier>
          <dc:relation>https://figshare.com/articles/dataset/_p_Home_advantage_by_season_p_/33940284</dc:relation>
          <dc:rights>CC BY 4.0</dc:rights>
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