{"id":8444,"date":"2025-11-19T07:10:25","date_gmt":"2025-11-19T07:10:25","guid":{"rendered":"https:\/\/www.ultrax.ai\/?p=8444"},"modified":"2026-03-11T14:21:11","modified_gmt":"2026-03-11T14:21:11","slug":"does-running-performance-reflect-game-performance","status":"publish","type":"post","link":"https:\/\/www.ultrax.ai\/de\/trainings\/does-running-performance-reflect-game-performance\/","title":{"rendered":"Association Between Running Performance and Match Effectiveness in Football"},"content":{"rendered":"<p>Insights from Modri\u0107 et al. (2019)<em> <\/em><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Einf\u00fchrung<\/strong> <\/h2>\n\n\n\n<p>Soccer is a complex, dynamic sport that requires a combination of technical skill, tactical awareness, and physical performance. Each position on the field imposes unique demands, from central defenders reacting rapidly to offensive transitions to wide midfielders covering long distances at high speeds. Over the past decade, the development of Global positioning systems (<strong>GPS technology) <\/strong>has revolutionized performance analysis by providing objective insights into the running patterns and intensities that define elite play. <\/p>\n\n\n\n<p>However, while external load data, such as total distance or sprint counts, are now standard in elite monitoring systems, <strong>the actual relationship between these running performance (RP) metrics and game performance indicators (GPI) remains<\/strong> less clear. Modri\u0107 et al. (2019) addressed this gap with a analysis of professional Croatian football players, bridging the physical and tactical domains through the integration of GPS data and the <strong>InStat Index<\/strong>, a position-specific measure of match performance.  <\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Study Design and Methodology<\/strong> <\/h2>\n\n\n\n<p>The study included <strong>101 match performances<\/strong> in Croatia\u2019s First Division during the 2018\/2019 season. The players were divided into five positions: <\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Central Defenders (CD; n = 26) <\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Full-Backs (FB; n = 24) <\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Central Midfielders (CM; n = 33) <\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Wide Midfielders (WM; n = 10) <\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Forwards (FW; n = 8) <\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<figure class=\"wp-block-image size-full is-resized\"><img fetchpriority=\"high\" decoding=\"async\" width=\"496\" height=\"287\" src=\"https:\/\/www.ultrax.ai\/wp-content\/uploads\/2025\/11\/Table-no-shadow3.png\" alt=\"running\" class=\"wp-image-8448\" style=\"width:768px;height:auto\" srcset=\"https:\/\/www.ultrax.ai\/wp-content\/uploads\/2025\/11\/Table-no-shadow3.png 496w, https:\/\/www.ultrax.ai\/wp-content\/uploads\/2025\/11\/Table-no-shadow3-300x174.png 300w, https:\/\/www.ultrax.ai\/wp-content\/uploads\/2025\/11\/Table-no-shadow3-18x10.png 18w\" sizes=\"(max-width: 496px) 100vw, 496px\" \/><figcaption class=\"wp-element-caption\">Table 1. Sociodemographic and anthropometric characteristics of the studied players with differences among playing positions (F-test).<\/figcaption><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<p>GPS devices (Catapult S5, 10 Hz) captured external load variables: <\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Total distance covered<\/strong> <\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Distances in five speed zones: walking (&lt;7.1 km\/h), jogging (7.2\u201314.3 km\/h), running (14.4\u201319.7 km\/h), high-speed running (19.8\u201325.1 km\/h), and sprinting (&gt;25.2 km\/h) <\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Accelerations and decelerations<\/strong> (total and high-intensity, defined as &gt;3 m\/s\u00b2 and &lt;\u20133 m\/s\u00b2, respectively) <\/li>\n<\/ul>\n\n\n\n<p>Meanwhile, <strong>game performance indicators (GPI)<\/strong> were derived from the <em>InStat Index<\/em>, a platform integrating technical-tactical data (passes, dribbles, tackles, aerial duels, finishing actions, etc.) that varies by position. For example: <\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>CDs: tackles, aerial duels, interceptions <\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>FBs: pressing, crosses, passes into the penalty area <\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>CMs: playmaking, key passes, finishing <\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>WMs: pressing, dribbling, counterattacks <\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>FWs: shooting, finishing, pressing <\/li>\n<\/ul>\n\n\n\n<p>To ensure statistical robustness, the study used ANOVA for between-position comparisons and <strong>Pearson\u2019s correlation<\/strong> to assess associations between GPS and InStat variables. <\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Key Findings<\/strong> <\/h2>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>1. Positional Differences in Running Performance<\/strong> <\/h3>\n\n\n\n<p>The study revealed <strong>significant positional differences (p &lt; 0.05)<\/strong> across nearly all physical performance metrics. <\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Central Midfielders (CM)<\/strong> covered the <strong>greatest total distance<\/strong> (\u224811,155 m), followed by wide and full-backs. <\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Central Defenders (CD)<\/strong> had the <strong>lowest total distance<\/strong> (~9,313 m) but showed the <strong>highest number of accelerations and decelerations<\/strong>, reflecting their reactive defensive duties. <\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Wide Midfielders (WM)<\/strong> recorded the <strong>greatest high-speed running and sprinting distances<\/strong>, consistent with wide play demands in modern football <br>ijerph-16-04032-v3 <br>. <\/li>\n<\/ul>\n\n\n\n<p>Croatian First Division players displayed similar total distances to those in elite European leagues (England, Spain, Germany), yet the <strong>intensity of effort was notably lower, only<\/strong> 6.4% of their total distance was covered at high intensity, compared to 10% in top-tier leagues.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full is-resized\"><img decoding=\"async\" width=\"685\" height=\"456\" src=\"https:\/\/www.ultrax.ai\/wp-content\/uploads\/2025\/11\/Table-no-shadow2.png\" alt=\"running \" class=\"wp-image-8449\" style=\"width:840px;height:auto\" srcset=\"https:\/\/www.ultrax.ai\/wp-content\/uploads\/2025\/11\/Table-no-shadow2.png 685w, https:\/\/www.ultrax.ai\/wp-content\/uploads\/2025\/11\/Table-no-shadow2-300x200.png 300w, https:\/\/www.ultrax.ai\/wp-content\/uploads\/2025\/11\/Table-no-shadow2-18x12.png 18w\" sizes=\"(max-width: 685px) 100vw, 685px\" \/><figcaption class=\"wp-element-caption\">Table 2. Descriptive statistics for running performances and game performance indicator (InStat).<\/figcaption><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>2. Correlations Between Running and Game Performance Indicators<\/strong> <\/h3>\n\n\n\n<p>The analysis found several <strong>position-dependent correlations<\/strong> between running performance and match efficacy: <\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Central Defenders:<\/strong> <br>Running distance in the 14.4\u201319.7 km\/h zone (r = 0.42, p = 0.03) and <strong>high-intensity accelerations<\/strong> (r = 0.49, p = 0.01) were both <strong>positively correlated with InStat index<\/strong>. This suggests that rapid acceleration and active positioning,  essential for pressing and intercepting ,  strongly influence defenders\u2019 game impact. <\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Full-Backs:<\/strong> <br><strong>Number of decelerations<\/strong> showed a <strong>negative correlation<\/strong> with InStat (r = \u20130.43, p = 0.04). High braking frequency likely reduces their attacking involvement (crosses, forward presses), showing that efficiency \u2014 not just effort , drives success for FBs <\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Forwards:<\/strong> <br><strong>Sprinting distance<\/strong> strongly correlated with match performance (r = 0.80, p = 0.02). Given that nearly every sprint represents a scoring or pressing opportunity, this aligns with the decisive value of high-intensity output in attacking play. <br> <\/li>\n<\/ul>\n\n\n\n<p>Interestingly, for <strong>midfielders (CM, WM)<\/strong>, there were <strong>no significant correlations<\/strong> between GPS metrics and the InStat index implying their success depends more on <strong>technical-tactical efficiency<\/strong> (passes, dribbles, key actions) rather than physical outputs alone.<\/p>\n\n\n\n<p><\/p>\n\n\n\n<figure class=\"wp-block-image size-full is-resized\"><img decoding=\"async\" width=\"642\" height=\"444\" src=\"https:\/\/www.ultrax.ai\/wp-content\/uploads\/2025\/11\/Table-no-shadow-1.png\" alt=\"running high speed runacceleration\" class=\"wp-image-8450\" style=\"width:840px;height:auto\" srcset=\"https:\/\/www.ultrax.ai\/wp-content\/uploads\/2025\/11\/Table-no-shadow-1.png 642w, https:\/\/www.ultrax.ai\/wp-content\/uploads\/2025\/11\/Table-no-shadow-1-300x207.png 300w, https:\/\/www.ultrax.ai\/wp-content\/uploads\/2025\/11\/Table-no-shadow-1-18x12.png 18w\" sizes=\"(max-width: 642px) 100vw, 642px\" \/><figcaption class=\"wp-element-caption\">Table 3. Pearson&#8217;s product moment correlations between running performances and game performance indicator (InStat) for different playing positions. <\/figcaption><\/figure>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Diskussion<\/strong> <\/h2>\n\n\n\n<p>Modri\u0107 et al. emphasize that soccer success is <strong>multifactorial and context-dependent<\/strong>. While running performance contributes to match success, <strong>its influence varies by positional role<\/strong>. The findings support the concept of <em>functional running<\/em> , where the <em>quality<\/em> und <em>timing<\/em> of movement outweigh sheer volume. <\/p>\n\n\n\n<p>For defenders, frequent high-velocity accelerations reflect aggressive defensive engagements, improving match outcomes. Forwards benefit from well-timed sprints, directly tied to goal-scoring situations. Meanwhile, midfielders\u2019 performance is more tactical, demanding spatial awareness and technical precision over raw physical output. <\/p>\n\n\n\n<p>The authors also note that Croatian league players, though physically capable, operate at lower high-intensity thresholds compared to their European counterparts.  <\/p>\n\n\n\n<p>Based on the findings of Modri\u0107 et al. (2019), several applied recommendations can be made for practitioners :  <\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Positional differences:<\/strong> Clear, statistically significant differences in total distance, high-speed running, sprinting, accelerations, and decelerations. <\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Position-specific correlations:<\/strong> <\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Forwards \u2013 sprinting distance strongly correlated with InStat index (r = 0.80). <\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Central defenders \u2013 accelerations and moderate-intensity distance correlated positively with InStat index. <\/li>\n<\/ul>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Full-backs \u2013 decelerations negatively correlated with InStat index. <\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<blockquote class=\"wp-block-quote has-text-align-center is-layout-flow wp-block-quote-is-layout-flow\">\n<p> <br><strong><em>\u201cSuccess in soccer is multi-dimensional and depends on physical, technical, and tactical factors that differ between playing positions.\u201d<\/em><\/strong> <br><strong><em>(Modri\u0107 et al., 2019, p. 8)<\/em><\/strong> <\/p>\n<\/blockquote>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Future Directions and Reflections<\/strong> <\/h2>\n\n\n\n<p>It\u2019s important to note that this study was published in <strong>2019<\/strong>, using data from the <strong>2018\/2019 Croatian First Division season<\/strong>. In football terms, that\u2019s already several tactical and physical \u201cgenerations\u201d ago. The evolution of <strong>match tempo, pressing intensity, and sprint demands<\/strong> across top European leagues over the past seven years has been significant research in the Premier League, Bundesliga, and UEFA Champions League consistently reports increases in <strong>high-intensity running and sprinting distances<\/strong> per match, as well as greater <strong>frequency of accelerations and decelerations within<\/strong> short intervals. <\/p>\n\n\n\n<p>Given those global trends, it would now be extremely valuable to <strong>replicate the Modri\u0107 et al. (2019) study<\/strong> within the <strong>current Croatian football context<\/strong>. Such a follow-up would show whether domestic performance patterns have evolved in line with the international trend  or whether differences have widened. <\/p>\n\n\n\n<p>If the same league today still exhibits <strong>lower relative proportions of high-intensity activity<\/strong> (as Modri\u0107 et al. originally found), it might reflect persistent differences in tactical tempo, player conditioning, or match style. On the other hand, if high-intensity metrics have increased, that would signal meaningful progress in <strong>physical preparation, training methodologies, and game evolution. <\/strong> <\/p>\n\n\n\n<p>Ultimately, repeating this kind of research now would not only serve as a benchmark for <strong>league development<\/strong>, but also as a feedback loop for <strong>academy programs, physical preparation strategies, and recruitment standards<\/strong> across clubs. It would allow practitioners to quantify the progress that has been intuitively observed on the pitch, and to identify areas where the domestic game still lags behind Europe\u2019s elite competitions. <\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Referenzen<\/strong> <\/h3>\n\n\n\n<p>Modri\u0107, T., Ver\u0161i\u0107, \u0160., Sekuli\u0107, D., &amp; Lipo\u0161ek, S. (2019). <em>Analysis of the Association Between Running Performance and Game Performance Indicators in Professional Soccer Players.<\/em> <em>International Journal of Environmental Research and Public Health<\/em>, 16(21), 4032. <a href=\"https:\/\/doi.org\/10.3390\/ijerph16204032\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.3390\/ijerph16204032<\/a> <\/p>\n\n\n\n<p><\/p>","protected":false},"excerpt":{"rendered":"<p>Insights from Modri\u0107 et al. (2019) Introduction Soccer is a complex, dynamic sport that requires a combination of technical skill, tactical awareness, and physical performance. Each position on the field imposes unique demands, from central defenders reacting rapidly to offensive transitions to wide midfielders covering long distances at high speeds. Over the past decade, the [&hellip;]<\/p>","protected":false},"author":7,"featured_media":8456,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[6],"tags":[],"class_list":["post-8444","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-trainings"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Association Between Running Performance and Match Effectiveness in Football | Ultrax AI<\/title>\n<meta name=\"description\" content=\"Does running performance reflect real game performance in football? 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