The emerging data explosion in sports field has created new opportunities to practice data science and analytics for deeper and larger scale analysis of games. With collaborating and competing 22 players on the field, soccer is often considered as a complex system. More specifically, each game is usually modeled as a network with players as nodes and passes between them as the edges. The number of passes usually define the weight of each edge, and these weights are employed to identify the key players using network modeling theory. However, the number of passes metric considers each pass the same and cannot differentiate players who are making ordinary passes, usually in their own pitch to a close teammate, from those who make key passes that start or improve an attack. As a solution, in this paper, we present a descriptive model to quantify the effectiveness of passes in soccer to differentiate between key passes and regular passes with not much contribution to the play of a team. Our model captures the perception of domain experts with a careful combination of risk and gain assessments. We have implemented our model in a soccer data analytics software. We performed a user study with domain experts, and the results show that our model captures domain expert evaluations of a number of example scenarios with 94% accuracy. The proposed model is not computationally demanding which allows real-time pass assessment during games on commodity hardware as demonstrated by our software prototype.

中文翻译：

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足球传球效率的计算模型

体育领域新兴的数据爆炸为实践数据科学和分析以更深入和更大规模地分析比赛创造了新的机会。由于球场上有 22 名球员合作和竞争，足球通常被认为是一个复杂的系统。更具体地说，每个游戏通常被建模为一个网络，玩家作为节点，在他们之间传递作为边。通过次数通常定义了每条边的权重，这些权重用于使用网络建模理论来识别关键参与者。然而，传球次数指标认为每次传球都是相同的，并且无法区分进行普通传球的球员，通常是在他们自己的球场上向亲密的队友传球，以及那些进行关键传球以开始或改善进攻的球员。作为解决方案，在本文中，我们提出了一个描述性模型来量化足球传球的有效性，以区分关键传球和常规传球，而这对球队的比赛贡献不大。我们的模型通过仔细结合风险和收益评估来捕捉领域专家的看法。我们已经在足球数据分析软件中实现了我们的模型。我们与领域专家进行了用户研究，结果表明我们的模型以 94% 的准确率捕获了领域专家对许多示例场景的评估。所提出的模型对计算要求不高，如我们的软件原型所示，它允许在商品硬件上的游戏期间进行实时通过评估。我们的模型通过仔细结合风险和收益评估来捕捉领域专家的看法。我们已经在足球数据分析软件中实现了我们的模型。我们与领域专家进行了用户研究，结果表明我们的模型以 94% 的准确率捕获了领域专家对许多示例场景的评估。所提出的模型对计算要求不高，如我们的软件原型所示，它允许在商品硬件上的游戏期间进行实时通过评估。我们的模型通过仔细结合风险和收益评估来捕捉领域专家的看法。我们已经在足球数据分析软件中实现了我们的模型。我们与领域专家进行了用户研究，结果表明我们的模型以 94% 的准确率捕获了领域专家对许多示例场景的评估。所提出的模型对计算要求不高，如我们的软件原型所示，它允许在商品硬件上的游戏期间进行实时通过评估。