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Tactical interaction of offensive and defensive teams in team handball analysed by artificial neural networks

机译:人工神经网络分析团队手球攻防队的战术互动。

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The interaction between teams behaviour is from high relevance for success in sports games. Since the analysis of this interaction is not well established, the present study attempts to model the interaction between opposing teams in team handball. Offensive and defensive playing patterns were determined by means of artificial neural networks from position data of 723 offensive action sequences and the corresponding defensive players, respectively. The most common combinations of these patterns were then analysed statistically. Pattern efficiency was assessed by scoring rate, distance between shooting position and nearest defensive player and distance to goal. No statistically significant relation between pattern combinations and efficiency was found. However, results revealed tendencies to higher efficiency of some tactical patterns. Furthermore, odds ratio analysis revealed advantageous defensive tactics against specific offensive behaviour. Summarizing, results indicate that artificial neural networks are appropriate to model the interaction between teams based on players' positions.
机译:团队行为之间的相互作用源于与体育游戏成功的高度相关性。由于这种互动的分析还没有很好地建立,因此本研究试图对团队手球中相对球队之间的互动进行建模。通过人工神经网络分别从723个进攻动作序列的位置数据和相应的防守球员中确定进攻和防守的比赛方式。然后对这些模式的最常见组合进行统计分析。通过得分率,射击位置与最近的防守队员之间的距离以及到球门的距离来评估模式效率。在模式组合和效率之间没有发现统计学上的显着关系。但是,结果表明某些战术模式有更高效率的趋势。此外,优势比分析揭示了针对特定进攻行为的有利防御策略。总而言之,结果表明,人工神经网络适合于根据玩家的位置对团队之间的互动进行建模。

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