Sports analytics machine learning, as the name suggests, uses a machine to learn without the need for human programming or intervention. Algorithms analyze large data sets for meaningful patterns from which future events can be predicted or classified. Its advantage over traditional statistical methods is the ability to detect patterns which are apparently random, as commonly encountered in sports.
While the human brain has its limits, machine learning is an extremely efficient tool for processing complex information, especially in the time required during sports games. Its advantage is manifested even more clearly in team sports, where the coach must monitor a team of players at once, often with much difficulty and requiring much time.
Among the various uses of sports analytics, machine learning is more commonly used in analyzing and predicting the performance of a team or individual players and strategy, and more interestingly, for the optimization of player positioning for team sports.
To elaborate on its use in analyzing and predicting performance, coaches gain access to information and vital statistics, such as how fast players are running, the distance they are covering in a game, and their levels of fatigue both in real-time and after a match. To turn this information into actionable insights, the coach can then identify weaker or stronger players, and make data-driven decisions when it comes to whom to replace during a match. Studying historical patterns of play and player movements also allows coaches to modify, evaluate and implement new strategies to harness each player’s strengths for optimal team performance.
Machine learning prides itself in several aspects in its use in sports analytics. First and foremost, its power is demonstrated in its ability to handle and manage large volumes of data, which will continue to rise as the world continues to embrace data-driven sports analytics.
Second, instead of having to watch many hours of recorded game film, machine learning can help the manager derive crucial insights without having to spend that amount of time. Coaches benefit from having more personal time with their players as machine learning provides them with answers very quickly. Third, machine learning is critical in analyzing data for complex and continuous team sports like soccer where the data is often unstructured and/or binary, i.e. either 0 (no goal) or 1 (goal). It creates high-level data points for easy analysis of the data which can help managers gain valuable insights in understanding their team’s performance. Machine learning transforms the data into a form (through objective measures) which is easily understood by coaches and managers.
Given the immense capabilities of machine learning, it comes as no surprise that there have been numerous examples of its use in various kinds of sports. Take soccer, probably the world’s most popular sport. Manchester City Football Club is known for its use of data analysis (including machine learning, of course) to provide insight not only pertaining to the team’s performance, but also in recruitment and marketing. STATS, a Chicago-based firm known as an authority in sports analytics, had also previously installed cameras in some soccer stadiums in Europe, tracking player and ball movement. These cameras are an integral part of its proprietary SportsVU system which it sells to sports clubs.
Other types of sports have also seen a dramatic rise in the use of machine learning. Consider rugby. Accenture, a global professional services firm, is currently using its big data tech to the Six Nations Tournament for varied uses, including the prediction of match results and analysis of performance of individual players.
More revolutionary would be PIQ’s development of the world’s first Al-powered wearable for combat sports like boxing. It is crafted using GAIA intelligence (a powerful machine learning platform for sports analytics), and claims to boast may capabilities, including the ability to analyze ‘microscopic variations in boxing movements’, ultimately to improve training regimen and efficiency.
To conclude, machine learning is definitely a revolutionary tool to be harnessed in sports analytics, especially when complemented with artificial intelligence (Al). It is a serious game-changer in the sporting world for coaches and managers of professional sporting clubs.
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