2020ICM问题D真题

问题标题：团队策略

年份：2020
学生等级：本科生
来源：ICM

问题

随着社会联系越来越紧密，社会面临的挑战也变得越来越复杂。我们依靠拥有不同专业知识和不同观点的跨学科团队来解决许多最具挑战性的问题。在过去 50 多年里，我们对团队成功的概念理解有了显著的进步，这使得更好的科学、创意或物理团队能够解决这些复杂的问题。研究人员已经报告了组建团队的最佳策略、队友之间的最佳互动以及理想的领导风格。所有部门和领域的强大团队都能够完成复杂的任务，而这些任务是无法通过个人努力或队友的一系列附加贡献来实现的。

探索团队过程最具启发性的场景之一是竞技团队运动。团队运动必须遵守严格的规则，这些规则可能包括但不限于球员人数、他们的角色、球员之间允许的接触、他们的位置和动作、获得的分数以及违规的后果。团队的成功不仅仅是单个球员能力的总和。相反，它取决于许多其他因素，包括队友之间的配合程度。这些因素可能包括团队是否拥有多样化的技能（一个人可能速度快，而另一个人则精准）、团队在个人表现和集体表现之间的平衡程度（明星球员可能有助于利用所有队友的技能）以及团队在一段时间内有效协调的能力（当一名球员从对手手中抢球时，另一名球员准备进攻）。

鉴于您的建模技能，您家乡的足球队（在欧洲和其他地方称为 football）哈士奇队的教练请您的公司I ntrepid C hampion M odeling (ICM) 帮助了解球队的动态。具体来说，教练请您探索场上球员之间的复杂互动如何影响他们的成功。目标不仅是研究直接导致得分的互动，而且要探索整场比赛和整个赛季的球队动态，以帮助确定可在下个赛季改善团队合作的具体策略。教练要求 ICM 量化和形式化球队成功（和失败）的结构和动态特征。哈士奇队提供了详细数据[1]，包括他们与 19 个对手进行的全部 38 场比赛（与每个对手球队交手两次）。总体而言，数据涵盖 366 名球员（30 名哈士奇队球员和 336 名对方球队球员）之间的 23,429 次传球，以及 59,271 场比赛。

为了响应 Huskie 教练的请求，您的 ICM 团队应该使用提供的数据来解决以下问题：
为球员之间的传球创建一个网络，其中每个球员都是一个节点，每次传球都构成球员之间的联系。使用传球网络来识别网络模式，例如二元和三元配置以及团队阵型。还要考虑比赛中的其他结构指标和网络属性。在观察互动时，您应该探索多个尺度，例如（但不限于）微观（成对）到宏观（所有球员），以及时间，例如短时间（每分钟）到长时间（整场比赛或整个赛季）。

确定反映成功团队合作的绩效指标（除了得分或胜利之外），例如比赛类型的多样性、球员之间的协调或贡献的分配。您还可以考虑其他团队级别的流程，例如适应性、灵活性、节奏或流程。澄清策略是否普遍有效或取决于对手的反击策略可能很重要。使用您确定的绩效指标和团队级别流程创建一个模型，以捕捉团队合作的结构、配置和动态方面。

利用从团队合作模式中获得的见解，告知教练哪些结构策略对哈士奇队有效。建议教练根据网络分析结果，在下个赛季应做出哪些改变来提高团队成功率。

通过对哈士奇队的分析，你能够在团队运动的受控环境中考虑群体动态。了解导致某些群体表现优于其他群体的复杂因素对于社会的发展和创新至关重要。随着我们的社会越来越多地解决涉及团队的问题，你能否概括你的研究结果，说明如何设计更有效的团队？为了开发团队绩效的通用模型，还需要捕捉团队合作的哪些其他方面？

您的提交内容应包括：

单页摘要表
目录
您的解决方案不超过 20 页，包括摘要和目录，最多 22 页。
注意：参考文献列表和任何附录不计入页数限制，应在完成解决方案后显示。您不应使用未经授权的图像和材料，这些图像和材料的使用受版权法限制。确保您引用了您的想法和报告中使用的材料的来源。

2020_Problem_D_DATA.zip
fullevents.csv
matches.csv
passingevents.csv
README.txt

该数据集是从一个更大的数据集处理而来的，涵盖了欧洲五大国家足球比赛以及 2018 年世界杯的近 2000 场比赛。

词汇表

二元配置：涉及成对玩家的关系。

三元配置：涉及三名玩家组成的群体的关系。
引用的参考文献

[1] Pappalardo, L.、Cintia, P.、Rossi, A.等人。足球比赛中时空比赛事件的公共数据集。科学数据6, 236 (2019)。

可选资源

对足球网络的研究已经产生了许多讨论相关主题的文章。下面列出了一些文章。您不需要在解决方案中使用任何这些示例文章，这也不是一份完整的列表。我们鼓励团队利用任何支持他们解决问题方法的期刊文章。

可到文末下载完整版中英文真题

以下是英文版真题

Problem

As societies become more interconnected, the set of challenges they face have become increasingly complex. We rely on interdisciplinary teams of people with diverse expertise and varied perspectives to address many of the most challenging problems. Our conceptual understanding of team success has advanced significantly over the past 50+ years allowing for better scientific, creative, or physical teams to address these complex issues. Researchers have reported on best strategies for assembling teams, optimal interactions among teammates, and ideal leadership styles. Strong teams across all sectors and domains are able to perform complex tasks unattainable through either individual efforts or a sequence of additive contributions of teammates.

One of the most informative settings to explore team processes is in competitive team sports. Team sports must conform to strict rules that may include, but are not limited to, the number of players, their roles, allowable contact between players, their location and movement, points earned, and consequences of violations. Team success is much more than the sum of the abilities of individual players. Rather, it is based on many other factors that involve how well the teammates play together. Such factors may include whether the team has a diversity of skills (one person may be fast, while another is precise), how well the team balances between individual versus collective performance (star players may help leverage the skills of all their teammates), and the team's ability to effectively coordinate over time (as one player steals the ball from an opponent, another player is poised for offense).

In light of your modeling skills, the coach of the Huskies, your home soccer (known in Europe and other places as football) team, has asked your company, Intrepid Champion Modeling (ICM), to help understand the team's dynamics. In particular, the coach has asked you to explore how the complex interactions among the players on the field impacts their success. The goal is not only to examine the interactions that lead directly to a score, but to explore team dynamics throughout the game and over the entire season, to help identify specific strategies that can improve teamwork next season. The coach has asked ICM to quantify and formalize the structural and dynamical features that have been successful (and unsuccessful) for the team. The Huskies have provided data[1] detailing information from last season, including all 38 games they played against their 19 opponents (they played each opposing team twice). Overall, the data covers 23,429 passes between 366 players (30 Huskies players, and 336 players from opposing teams), and 59,271 game events.

To respond to the Huskie coach's requests, your team from ICM should use the provided data to address the following:

Create a network for the ball passing between players, where each player is a node and each pass constitutes a link between players. Use your passing network to identify network patterns, such as dyadic and triadic configurations and team formations. Also consider other structural indicators and network properties across the games. You should explore multiple scales such as, but not limited to, micro (pairwise) to macro (all players) when looking at interactions, and time such as short (minute-to-minute) to long (entire game or entire season).

Identify performance indicators that reflect successful teamwork (in addition to points or wins) such as diversity in the types of plays, coordination among players or distribution of contributions. You also may consider other team level processes, such as adaptability, flexibility, tempo, or flow. It may be important to clarify whether strategies are universally effective or dependent on opponents' counter-strategies. Use the performance indicators and team level processes that you have identified to create a model that captures structural, configurational, and dynamical aspects of teamwork.

Use the insights gained from your teamwork model to inform the coach about what kinds of structural strategies have been effective for the Huskies. Advise the coach on what changes the network analysis indicates that they should make next season to improve team success.

Your analysis of the Huskies has allowed you to consider group dynamics in a controlled setting of a team sport. Understanding the complex set of factors that make some groups perform better than others is critical for how societies develop and innovate. As our societies increasingly solve problems involving teams, can you generalize your findings to say something about how to design more effective teams? What other aspects of teamwork would need to be captured to develop generalized models of team performance?

Your submission should consist of:

One-page Summary Sheet
Table of Contents
Your solution of no more than 20 pages, for a maximum of 22 pages with your summary and table of contents.

Note: Reference List and any appendices do not count toward the page limit and should appear after your completed solution. You should not make use of unauthorized images and materials whose use is restricted by copyright laws. Ensure you cite the sources for your ideas and the materials used in your report.

Attachment

2020_Problem_D_DATA.zip
fullevents.csv
matches.csv
passingevents.csv
README.txt

This data set was processed from a much larger dataset covering nearly 2000 matches from five European national soccer competitions, as well as the 2018 World Cup.

Glossary