Big data and artificial intelligence (AI) now characterise the game of football. The mass collection of data streams in the professional game forms the basis for the data revolution in football. Position data of all players is recorded during the game. In addition, there is event data that records facts such as passes, shots, duels and substitutions. In conjunction with AI, football data is already changing scouting, match, player and opponent analysis, the way coaches intervene during the game, live commentary and how spectators experience the game. As great as the application potential of AI-supported data analysis in football is, the use of data is just as demanding. For example, data collection cannot always be automated and data sometimes has to be cleansed or validated. In addition, the collection of data requires technologies such as camera-based tracking systems or a trained team for live recording. Furthermore, the availability of data - position data is predominantly available for the top men's leagues - and its comparability are limited, as is the case with event data from different providers. An additional challenge is the lack of consensus on what performance indicators are relevant for player or team success and how these can be uniformly defined and thus modelled. In addition, the use of the data requires a high level of data and football expertise. Although the measurement of football suggests that it is becoming more predictable, it can be assumed that even in the age of AI and big data, chance will continue to play a decisive role in football results. So today, as in 2006, "That's football. Sometimes the better team wins!" (Lukas Podolski).