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:
- 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
Glossary
- Dyadic Configurations: relationships involving pairs of players.
- 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.
- 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.
- 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.
- Duch J., Waitzman J.S., Amaral L.A.N. (2010). Quantifying the performance of individual players in a team activity. PLoS ONE, 5: e10937.
- GÜRSAKAL, N., YILMAZ, F., ÇOBANOĞLU, H., ÇAĞLIYOR, S. (2018). Network Motifs in Football. Turkish Journal of Sport and Exercise, 20 (3), 263-272.
隨着社會之間的聯繫越來越緊密，它們面臨的一系列挑戰也越來越複雜。我們依靠具有不同專業知識和不同觀點的跨學科團隊來解決許多最具挑戰性的問題。在過去50多年裏，我們對團隊成功的概念性理解有了顯著的進步，使得更好的科學、創新或物理團隊能夠解決這些複雜的問題。研究人員已經報告了組建團隊的最佳策略、團隊成員之間的最佳互動以及理想的領導風格。跨部門和領域的強大團隊能夠執行復雜的任務，無論是通過個人努力還是通過團隊成員的一系列額外貢獻都無法實現。
探索團隊過程的信息量最大的環境之一是在競技團隊運動中。團隊運動必須遵守嚴格的規則，這些規則可能包括但不限於球員的數量、他們的角色、球員之間允許的接觸、他們的位置和移動、贏得的分數以及違規的後果。團隊的成功不僅僅是個人能力的總和。相反，這是基於許多其他因素，涉及到如何發揮隊友在一起。這些因素可能包括團隊是否擁有多種技能（一個人可能速度快，而另一個人則精確），團隊在個人和集體表現之間的平衡程度（明星球員可能有助於利用所有隊友的技能），以及球隊在一段時間內有效協調的能力（當一名球員從對手手中搶走球時，另一名球員準備進攻）。
根據你的建模技巧，哈士奇，你的家鄉足球隊（在歐洲和其他地方被稱爲足球隊）的教練，已經要求你的公司，無畏冠軍模型（ICM），幫助瞭解球隊的動態。特別是，教練讓你去探索場上球員之間複雜的互動如何影響他們的成功。目標是
不僅要檢查直接導致得分的互動，還要探索整個比賽和整個賽季的團隊動態，幫助確定可以在下個賽季提高團隊合作的具體策略。教練要求ICM量化和形式化團隊成功（和失敗）的結構和動態特徵。哈士奇隊提供了上個賽季的詳細資料，包括他們與19名對手的38場比賽（每隊打兩次）。總的來說，數據涵蓋了366名球員（30名哈士奇球員，336名對手球員）之間的23429次傳球，以及59271個比賽項目。
爲了響應Huskie教練的請求，ICM的團隊應該使用提供的數據來解決以下問題：
爲球員之間的傳球創建一個網絡，每個球員都是一個節點，每個傳球都構成球員之間的鏈接。使用你的傳遞網絡來識別網絡模式，如二元和三元結構以及團隊隊形。同時考慮其他結構指標和整個奧運會的網絡屬性。你應該探索多個尺度，例如，但不限於，微觀（成對）到宏觀（所有玩家）的互動，以及時間，例如短（分鐘到分鐘）到長（整個遊戲或整個賽季）。
·確定反映成功團隊合作的績效指標（除了分數或勝利），例如遊戲類型的多樣性、玩家之間的協調或貢獻的分配。你也可以考慮其他團隊級的過程，比如適應性、靈活性、節奏或流程。澄清戰略是否普遍有效或取決於對手的反戰略可能很重要。使用您確定的績效指標和團隊級流程創建一個模型，該模型捕獲團隊合作的結構、配置和動態方面。
利用從團隊合作模式中獲得的洞察力，告知教練什麼樣的結構策略對哈士奇犬有效。告訴教練網絡分析表明他們應該在下個賽季做出哪些改變來提高球隊的成功率。
你對哈士奇犬的分析使你能夠在團隊運動的受控環境中考慮羣體動力學。瞭解使某些羣體比其他羣體表現更好的一系列複雜因素，對於社會如何發展和創新至關重要。隨着我們的社會越來越多地解決涉及團隊的問題，你能概括一下你的發現嗎
談談如何設計更有效的團隊？開發團隊績效的通用模型還需要了解團隊合作的哪些方面？
你的意見應包括：
一頁摘要表
目錄
你的解答不超過20頁，最多22頁，附有摘要和目錄。
注意：參考列表和任何附錄不計入頁面限制，應在完成解決方案後顯示。您不應使用未經授權的圖片和材料，其使用受到版權法的限制。確保你引用了你的觀點的來源和你報告中使用的材料。
附件
2020_Problem_D_DATA.zip
fullevents.csv
matches.csv
passingevents.csv
README.txt
詞彙表
二元結構：涉及成對玩家的關係。
三元結構：三人一組的關係。
引用參考文獻
[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).
可選資源
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.
- 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.
- 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.
- Duch J., Waitzman J.S., Amaral L.A.N. (2010). Quantifying the performance of individual players in a team activity. PLoS ONE, 5: e10937.
- GÜRSAKAL, N., YILMAZ, F., ÇOBANOĞLU, H., ÇAĞLIYOR, S. (2018). Network Motifs in Football. Turkish Journal of Sport and Exercise, 20 (3), 263-272.
