What types of decisions are made by football managers

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Q1. What types of decisions are made by football managers? Would you characterise these decisions as structured or unstructured problems? Explain.

Q2. Describe how big data can help football managers to make better decisions and how these has an affect on the decision-making process.

Q3. Do you think it is appropriate for football managers to use only quantitative information to evaluate their players' performance during a season? Why or why not?

CASE STUDY ANALYSIS

Manchester City: Football Big Data Champions In most football teams, the minutes before the match are spent in the locker room where the coach provides last minute tips and delivers a motivational speech to the players. However, for Manchester City Football Club the ritual is a bit different. The team spends 15 minutes before each match meeting the club's performance analyst team, discussing things they had done well or wrong in previous matches. For instance, the defense examines several factors - the number of crosses, effective or ineffective tackles, balls lost or recovered, the relationship with midfield, and movements in protecting their penalty area. The day after the match, the analysis team, headed by Gavin Fleigh, gives each player a detailed and personalized report of all their movements during the match, thus, enabling each player to get an accurate feedback on improvements required. In a 2012 interview released to Forbes, Fleigh declared that the goal of the performance analysis unit is both to help the club make smarter decisions by relying on objective and more informative data, and to enhance players' performance by helping them to become more reflective and aware of their unique features, actions, and movements on the pitch. To illustrate how the performance analysis team helps better the team performance, let's look at Manchester City's performance and the set goal-piece goals scored in the 2010-11 season. According to the analyst team, City was underperforming more than any other club in the Premier League with only one set-piece goal scored over 21 matches. To understand what led to the goals scored across several European leagues, the analyst team studied more than 500 corner kicks. The players were then presented with video illustrating the best tactics and movements applied by other teams. This helped City to score 9 goals in the first 15 matches of the next season from corners, which represents a tremendous improvement in their performance. Data analysis is a critical decision-making support tool for Manchester City's managers at all levels, including for youth teams. For example, future young players are helped in understanding their strengths and weaknesses within the different formation plays and what aspects they need to focus on to develop their talent. It is important to note that big data is just a means to facilitate the achievement of Manchester City's strategic goals concerning youth development, which is to integrate young homegrown-talents into the first team's formation. The performance analysts have helped the team to become very successful - Manchester City got the best defensive records for two consecutive years since 2012, and it won the title in the seasons 2011-12 and 2013-14 after more than four decades of no wins. Of course, big data is not the only factor behind these successes, but it was very important. To continue being a leader in football big data, in 2016, Manchester City organised a global Hackathon, with more than 400 applications received from all over the world, where data and football experts created algorithms and simulations using data from real players that have never before been available to external actors. The challenge was to create algorithms that could help identify new movements, passes, runs and pressure to be more effective on the pitch. The winning team, who received a cash prize of £7000 and the promise to collaborate with the performance analysis team, created a learning machine algorithm that tracks decision making during the games.

Reference no: EM132676548

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