The availability of massive data about sports activities offers nowadays the opportunity to quantify the relation between performance and success. In this study, we analyze more than 6000 games and 10 million events in six European leagues and investigate this relation in soccer competitions. We discover that a team’s position in a competition’s final ranking is significantly related to its typical performance, as described by a set of technical features extracted from the soccer data. Moreover, we find that, while victory and defeats can be explained by the team’s performance during a game, it is difficult to detect draws by using a machine learning approach. We then simulate the outcomes of an entire season of each league only relying on technical data and exploiting a machine learning model trained on data from past seasons. The simulation produces a team ranking which is similar to the actual ranking, suggesting that a complex systems’ view on soccer has the potential of revealing hidden patterns regarding the relation between performance and success.

中文翻译：

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量化足球表现和成功之间的关系

如今，有关体育活动的大量数据的可用性为量化表现与成功之间的关系提供了机会。在这项研究中，我们分析了六个欧洲联赛的 6000 多场比赛和 1000 万场赛事，并调查了足球比赛中的这种关系。我们发现，一支球队在比赛最终排名中的位置与其典型表现显着相关，正如从足球数据中提取的一组技术特征所描述的那样。此外，我们发现，虽然胜利和失败可以用球队在比赛中的表现来解释，但使用机器学习方法很难检测平局。然后，我们仅依靠技术数据和利用过去赛季数据训练的机器学习模型来模拟每个联赛整个赛季的结果。