Since no databases exists containing news articles around the national teams, one of the central tasks of this dissertation is building a customized corpus consisting of news reports written and published between 2006 - 2018. The corpus consists of news reports collected from the LexisNexis database. This database contains enormous amount of data and is tricky to navigate. Therefore, I developed a strategy where I first read a sample collection of articles, documented and categorized key terms used in these articles, and eventually wrote three search strings to test which one gives me the best search results. Using the Precision at k metric, I determined the best search string that can be applied to LexisNexis’ database. This innovative approach on extracting data from a rather complicated database could also be applied to other research in the future. As for now, I have completed data collection. To complete my dissertation, I need to finish data cleaning, and then conduct topic modeling and co-occurrence network analysis. Latent Dirichlet Allocation (LDA) is a widely used technique for topic modeling, which is the process of uncovering hidden topics in a collection of documents. Co-occurrence network analysis can help uncover hidden relationships and provide insights into the structure of my corpus. In this project, I use it to show the relationship between certain players and language used to describe them. These results could help me locate individual narratives that require closer examination. My analysis will then combine this qualitative approach with closely reading representative news articles. With this goal in mind, my work hopes to bring new perspectives to understand the complex role of the multicultural <hi xmlns="http://www.tei-c.org/ns/1.0" rend="italic">Mannschaft</hi> in the process of shaping contemporary German identity.