体育赛事中的一种概率匹配分类模型
A probabilistic match classification model for low-scoring sports
AI总结：
本文提出一种概率模型，用于分类体育赛事中的比赛，以解决现有模型在参赛者差异和激励区分上的不足，并通过实证分析揭示了不完整循环联赛中潜在串通比赛的风险。
摘要：
现有赛事设计文献中的匹配分类模型有两个主要局限性：只有当未来不确定的结果不会影响其奖品时，参赛者才被视为无差异，且竞争性比赛在参赛者的激励方面未被区分。我们提出了一种概率框架来解决这两个问题。对于每场比赛，我们的方法依赖于模拟所有同时或稍后进行的其他比赛，以计算在三种主要结果（胜、平、负）下的资格概率，这使每场比赛可以被分类为六个不同的类别。所提出的模型应用于之前的小组赛阶段和新的不完整循环联赛，该联赛在2024/25赛季的欧足联俱乐部比赛中引入。不完整循环赛被发现包含更少的无利害比赛，其中双方参赛者都是无差异的，以及大量双方都应进攻的比赛。然而，潜在串通比赛的比例显著增加可能会引发严重丑闻。
英文摘要：
All existing match classification models in the tournament design literature suffer from two major limitations: a contestant is considered indifferent only if uncertain future results do never affect its prize, and competitive matches are not distinguished with respect to the incentives of the contestants. We propose a probabilistic framework to address both issues. For each match, our approach relies on simulating all other matches played simultaneously or later to compute the qualifying probabilities for the three main outcomes (win, draw, loss), thereby classifying each match into six categories. The suggested model is applied to the last round of the previous group stage and the new incomplete round-robin league, introduced in the 2024/25 season of UEFA club competitions. The incomplete round-robin tournament is found to contain fewer unimportant matches with two indifferent teams, and substantially more matches where both teams should play offensively. However, the robustly higher proportion of potentially collusive matches can threaten with serious scandals.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
