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Predicting match outcome according to the quality of opponent in the English premier league using situational variables and team performance indicators

机译:根据使用情境变量和团队绩效指标,根据英国英超联赛的对手质量预测匹配结果

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The purpose of this research is to investigate the situational variables and performance indicators that significantly affect the match outcome (win, loss or draw) based on the quality of opposition. The data consisted of the situational variables and performance indicators of the matches in the English Premier League for the 2017-2018 season. One-way ANOVA, Tukey HSD, k-means clustering and decision tree approaches were implemented in the analyses. Scoring first was found as the most influential on match outcome in each decision tree, while the effects of clearances, shots, shots on target, possession percentage and match location on the match outcome varied according to the quality of opponent. An average of 2.43, 0.53 and 0.97 goals were scored by the teams that won, lost and drawn, respectively and teams that scored first won 67% of the matches. The decision trees based on the quality of opponent correctly predicted 67.9, 73.9 and 78.4% of the results in the games played against balanced, stronger and weaker opponents, respectively, while in all games (regardless of the quality of opponent) this rate is only 64.8%, implying the importance of considering the quality of opponent in the analyses. Coaches and managers can use these findings to create targets for players and teams during training and matches, and also can be prepared for these different competitive scenarios.
机译:本研究的目的是调查基于反对质量的局势变量和性能指标,从而显着影响匹配结果(胜利,亏损或抽签)。这些数据包括2017-2018赛季英超联赛中英国英超联赛的境地和绩效指标。单向ANOVA,Tukey HSD,K-Means集群和决策树方法是在分析中实现的。第一次评分被发现是每种决策树中匹配结果的最有影响力,而间隙,镜头,射击对目标的影响,占有率百分比和匹配位置根据对手的质量而变化。平均赢得了2.43,0.53和0.97个进球,分别获得,丢失和绘制,队伍分别赢得67%的比赛。基于对手的质量的决策树正确预测67.9,73.9和78.4%的结果分别在所有游戏中分别与平衡,更强和较弱的对手一起播放,而无论对手的质量如何)这个速度只是64.8%,意味着考虑分析中对手质量的重要性。教练和经理可以使用这些调查结果为培训和比赛期间为玩家和团队创建目标,也可以为这些不同的竞争情景做好准备。

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