2020美赛D题原题+翻译

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 andvaried 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:

1. One-page Summary Sheet
2. Table of Contents
3. 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

1. 2020_Problem_D_DATA.zip
2. fullevents.csv
3. matches.csv
4. passingevents.csv
5. README.txt

Glossary

1. Dyadic Configurations: relationships involving pairs of players.
2. Triadic Configurations: relationships involving groups of three players.

Cited Reference

[1] Pappalardo, L., Cintia, P., Rossi, A. et al. A public data set of spatio-temporal match events in soccer competitions. Sci Data 6, 236 (2019).

Optional Resources

Research in football (soccer) networks has led to many articles that discuss related topics. A few articles are listed below. You are not required to use any of these sample articles in your solution, nor is it a comprehensive list. We encourage teams to utilize any journal article that supports their approach to the problem.

1. Buldú, J.M., Busquets, J., Echegoyen, I. et al. (2019). Defining a historic football team: UsingNetwork Science to analyze Guardiola’s F.C. Barcelona. Sci Rep, 9, 13602.
2. Cintia, P., Giannotti, F., Pappalardo, L., Pedreschi, D., & Malvaldi, M. (2015). The harsh rule of the goals: Data-driven performance indicators for football teams. 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 1-10, 7344823.
3. Duch J., Waitzman J.S., Amaral L.A.N. (2010). Quantifying the performance of individual players in a team activity. PLoS ONE, 5: e10937.
4. GÜRSAKAL, N., YILMAZ, F., ÇOBANOĞLU, H., ÇAĞLIYOR, S. (2018). Network Motifs in Football. Turkish Journal of Sport and Exercise, 20 (3), 263-272.

 

 

随着社会之间的联系越来越紧密，它们面临的一系列挑战也越来越复杂。我们依靠具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性理解有了显著的进步，使得更好的科学、创新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、团队成员之间的最佳互动以及理想的领导风格。跨部门和领域的强大团队能够执行复杂的任务，无论是通过个人努力还是通过团队成员的一系列额外贡献都无法实现。

 

探索团队过程的信息量最大的环境之一是在竞技团队运动中。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的数量、他们的角色、球员之间允许的接触、他们的位置和移动、赢得的分数以及违规的后果。团队的成功不仅仅是个人能力的总和。相反，这是基于许多其他因素，涉及到如何发挥队友在一起。这些因素可能包括团队是否拥有多种技能（一个人可能速度快，而另一个人则精确），团队在个人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），以及球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走球时，另一名球员准备进攻）。

 

根据你的建模技巧，哈士奇，你的家乡足球队（在欧洲和其他地方被称为足球队）的教练，已经要求你的公司，无畏冠军模型（ICM），帮助了解球队的动态。特别是，教练让你去探索场上球员之间复杂的互动如何影响他们的成功。目标是

 

不仅要检查直接导致得分的互动，还要探索整个比赛和整个赛季的团队动态，帮助确定可以在下个赛季提高团队合作的具体策略。教练要求ICM量化和形式化团队成功（和失败）的结构和动态特征。哈士奇队提供了上个赛季的详细资料，包括他们与19名对手的38场比赛（每队打两次）。总的来说，数据涵盖了366名球员（30名哈士奇球员，336名对手球员）之间的23429次传球，以及59271个比赛项目。

 

为了响应Huskie教练的请求，ICM的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，每个球员都是一个节点，每个传球都构成球员之间的链接。使用你的传递网络来识别网络模式，如二元和三元结构以及团队队形。同时考虑其他结构指标和整个奥运会的网络属性。你应该探索多个尺度，例如，但不限于，微观（成对）到宏观（所有玩家）的互动，以及时间，例如短（分钟到分钟）到长（整个游戏或整个赛季）。

 

·确定反映成功团队合作的绩效指标（除了分数或胜利），例如游戏类型的多样性、玩家之间的协调或贡献的分配。你也可以考虑其他团队级的过程，比如适应性、灵活性、节奏或流程。澄清战略是否普遍有效或取决于对手的反战略可能很重要。使用您确定的绩效指标和团队级流程创建一个模型，该模型捕获团队合作的结构、配置和动态方面。

 

利用从团队合作模式中获得的洞察力，告知教练什么样的结构策略对哈士奇犬有效。告诉教练网络分析表明他们应该在下个赛季做出哪些改变来提高球队的成功率。

 

你对哈士奇犬的分析使你能够在团队运动的受控环境中考虑群体动力学。了解使某些群体比其他群体表现更好的一系列复杂因素，对于社会如何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，你能概括一下你的发现吗

 

谈谈如何设计更有效的团队？开发团队绩效的通用模型还需要了解团队合作的哪些方面？

 

你的意见应包括：

一页摘要表

目录

你的解答不超过20页，最多22页，附有摘要和目录。

 

注意：参考列表和任何附录不计入页面限制，应在完成解决方案后显示。您不应使用未经授权的图片和材料，其使用受到版权法的限制。确保你引用了你的观点的来源和你报告中使用的材料。

 

附件

2020_Problem_D_DATA.zip

fullevents.csv

matches.csv

passingevents.csv

README.txt

 

词汇表

 

二元结构：涉及成对玩家的关系。

三元结构：三人一组的关系。

 

引用参考文献

[1] Pappalardo, L., Cintia, P., Rossi, A. et al. A public data set of spatio-temporal match events in soccer competitions. Sci Data 6, 236 (2019).

可选资源

Research in football (soccer) networks has led to many articles that discuss related topics. A few articles are listed below. You are not required to use any of these sample articles in your solution, nor is it a comprehensive list. We encourage teams to utilize any journal article that supports their approach to the problem.

1. Buldú, J.M., Busquets, J., Echegoyen, I. et al. (2019). Defining a historic football team: UsingNetwork Science to analyze Guardiola’s F.C. Barcelona. Sci Rep, 9, 13602.
2. Cintia, P., Giannotti, F., Pappalardo, L., Pedreschi, D., & Malvaldi, M. (2015). The harsh rule of the goals: Data-driven performance indicators for football teams. 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 1-10, 7344823.
3. Duch J., Waitzman J.S., Amaral L.A.N. (2010). Quantifying the performance of individual players in a team activity. PLoS ONE, 5: e10937.
4. GÜRSAKAL, N., YILMAZ, F., ÇOBANOĞLU, H., ÇAĞLIYOR, S. (2018). Network Motifs in Football. Turkish Journal of Sport and Exercise, 20 (3), 263-272.