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For the new year, Six Sports Management is bringing you a series of articles on data management in professional sports clubs, in partnership with<\/em> Alexis Barat<\/a>, Data Scientist. This first article aims to provide an overview of the main challenges faced by clubs.<\/em><\/p>\n Articles describing how professional sports clubs are currently using data to improve their performance are full of information that can make us all dream. At Manchester City, each player has a tablet to review their performance and their opponent’s weaknesses at half-time. At TFC, algorithms are used to identify undervalued players in order to build a competitive team at a lower cost.<\/p>\n Positioned at the intersection of three key areas \u2014 transfer strategy, on-field performance optimisation and squad management \u2014 data has become a fundamental component in clubs’ pursuit of performance. And there are numerous data analysis tools available to support this objective. Yet while the digital transformation of sport in recent years has made these tools more accessible, clubs are far from equally equipped.<\/p>\n The use of data in sport emerged in the early 2000s in American baseball with Billy Beane’s Oakland Athletics. Data analysis became a solution that enabled this low-budget club to compete with teams operating on astronomical budgets, such as the New York Yankees. The idea was to identify market inefficiencies that could be used to recruit high-potential players at a lower cost.<\/p>\n It is clear that the trend has now reversed, with data becoming a tool that enables the wealthiest clubs to gain an advantage in an area of performance that others simply cannot afford. But beyond the financial challenges involved, the clubs that use data most effectively are those that have structured themselves accordingly. Other clubs attempt to cobble together solutions that are sometimes far more costly and poorly understood than they are effective.<\/p>\n Looking beyond British football and a few exceptions found in Ligue 1, the major American leagues or the more discreet Australian football ecosystem, it quickly becomes clear that the reality of data analysis is far removed from this idealised picture.<\/p>\n Clubs face genuine difficulties when it comes to using data. While data is abundant \u2014 even for spectators watching a game on television \u2014 the challenge is not so much collecting it as analysing the raw data.<\/p>\n The first reason is a lack of resources, both financial and human. With data provider fees reaching exorbitant levels, clubs rely on their \u201canalysts\u201d to code matches using specialised software. As these software solutions continue to multiply, analysts attempt to consolidate data from different databases, often manually, before they can finally analyse it.<\/p>\n Inefficiency is therefore at its peak: on the one hand, people specialising in video editing find themselves cross-referencing and analysing data, while on the other, analysts who specialise in interpreting data spend as much as 80% of their working time compiling it.<\/p>\n The lack of training on the software, the proliferation of databases and the absence of historical data regularly distort analyses and make the entire process counterproductive. While everyone agrees on the need to cross-reference the data generated by the club’s different departments, clubs do not allocate the resources required to do so thoroughly.<\/p>\n The limited resources dedicated to data analysis create situations of constant pressure in which analysts can end up working 70 hours a week. Every week, the weekend match is the analysts’ priority, as they spend hours breaking down video of both the opponent and their own team.<\/p>\n But the slightest unexpected event can disrupt this balance. A player gets injured and the coach wants to know why. He also wants to know the performance levels of the player expected to replace him the following weekend, while the sporting director requests a comprehensive analysis of a player who is still unknown to the club’s databases in order to sign him as a replacement player.<\/p>\n Establishing processes to move data from its various sources using appropriate collection and storage tools therefore appears essential to give analysts more time to focus on high-value-added tasks.<\/p>\n Yet all these risks can be avoided by bringing in the right people at the right time when building a data analysis project.<\/p>\n As mentioned above, it is most often the Sports Scientist (generally with a background in sports science, such as STAPS in France) who also takes on the role of Data Analyst\/Scientist (generally with a background in statistics or computer science).<\/p>\n These two professions are very often \u2014 and incorrectly \u2014 conflated, because data within an organisation needs to be handled by different specialists.<\/p>\n The Data Engineer<\/strong> is responsible for database architecture and consolidation.<\/p>\n The Data Analyst<\/strong> ensures that the organisation’s data capabilities meet the needs of the business \u2014 for example, a coach or manager within a club \u2014 and therefore tends to focus primarily on descriptive analysis.<\/p>\n Finally, the Data Scientist<\/strong>, who specialises in complex analysis and algorithm development, is responsible for addressing predictive analytics challenges.<\/p>\n In conclusion, the journey from data collection to decision-making requires several different resources and areas of expertise.<\/p>\n The resource challenge is compounded by another factor that is pervasive throughout the sports industry. While major clubs can afford to analyse almost everything, clubs with more limited resources struggle to prioritise their use of data.<\/p>\n Indeed, there are countless potential applications for data: analysing player performance, phases of play, injury trends, and so on \u2014 with just as many opportunities to lose sight of the coach’s priorities.<\/p>\n Putting data at the heart of a club’s strategy and becoming \u201cdata-driven\u201d requires genuine organisational structure, with a performance analysis department made up of key profiles and capable of working over the long term rather than focusing solely on the weekend match.<\/p>\n In-depth data analysis is an excellent way to understand the origins of certain problems or identify new ones that had previously gone unnoticed. But to achieve this, the analyst must be regarded as a researcher \u2014 a fundamental status for maintaining a competitive advantage and having the luxury of taking the time to identify opportunities for improvement where no one has previously dared to look.<\/p>\n Furthermore, the need to structure a department around a genuine data strategy also appears to be a way of avoiding the pitfalls of data. By placing too much importance on certain datasets, organisations can eventually find themselves trying to answer questions that no coach would normally ask.<\/p>\n However, establishing a performance analysis department, while necessary to ensure effective data analysis, can sometimes be more complicated than it initially appears.<\/p>\n Unless they report directly to the club’s senior management, these departments are often independent and poorly understood. As a result, they can depend heavily on the goodwill of a Head of Performance who, when leaving the club, creates numerous problems by taking their analysts and databases with them.<\/p>\n These departments rarely survive such departures.<\/p>\n Yet they are genuinely necessary because they act as a bridge between all the data providers within the organisation: strength and conditioning coaches, video analysts, the medical department, scouts, HR, and others.<\/p>\n They also have the advantage of being objective and not reporting directly to a coach who may sometimes use analysts to confirm a tactical decision rather than explore new approaches.<\/p>\n Because they remain consistent over time, these departments can build genuine long-term databases, addressing one of the key challenges of data analysis: the lack of historical data required to make analyses statistically viable.<\/p>\n These departments make it possible to ensure the long-term sustainability of tools and transfer knowledge with a view to achieving long-term performance.<\/p>\n Data analysis comes at a cost, but it should be viewed as an investment. In most cases, it can prove profitable by preventing player injuries or identifying less expensive players who can deliver the same level of performance.<\/p>\n Although we have not gone into the subject in depth here, data offers genuine opportunities to improve performance and assess an organisation in order to make it more productive.<\/p>\n Data is relatively accessible, but it requires thoughtful organisation and appropriate tools.<\/p>\n In their next article, the teams at Six Sports Management<\/a> and Alexis Barat<\/a> will provide practical guidance on how to structure your organisation effectively in order to make the most of your data.<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":" For the new year, Six Sports Management is bringing you a series of articles on data management in professional sports clubs, in partnership with Alexis Barat, Data Scientist. 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