There has been a surge in sports scientists or data scientists, striving to integrate data in sports. Data collected, such as players’ movements and vital stats in training and on game day, are analyzed to match strategies and enhance the performance of the players. The quality of the prediction is associated with the quality of data that is put into the machine. The machine learning process allows to understand non-linear systems within the sports industry. Machine learning algorithms identify meaningful patterns in the complex data, which is then classified to predict future events. Sports organizations worldwide are utilizing productivity solutions and cloud to improve the team and player’s performance, connect with fans, and manage their operations in fresh ways.
Some of the promising cases of machine learning in sports are listed below:
In December 2018, Opta Sports partnered with the Danish SuperLiga to collect live data for every league match to the different customers worldwide.
In March 2019, Stats announced an agreement with the Spring League (U.S.) to provide live coverage of every event happening during the spring league next season, including play-by-play, box scores, and drive charts.
In April 2018, Oracle partnered with the NBA to provide oracle analytics cloud, which helps to analyze the winning percentage of different teams and get an overview for supporters of those teams.
In June 2019, IBM partnered with England Lawn Tennis Club (AELTC) to build and integrate a system, which will use AI-powered automated video highlights for the Wimbledon fans in every tennis match.
SAS Institute Inc.
In August 2018, SAS collaborated with the Samford University Center for Sports Analytics to support learning and research, teaching in various areas where analytics affects sports, such as sponsorship, sports medicine, fan engagement, player tracking, sports media and operations.
Machine learning in sports organizations can use their data to improve every area of their operations. Predictive analytics is used to make targeted decisions and strategic changes, including the recruitment of players and performance to ticket sales. Machine learning also helps in understanding the actual value of a player and make better decisions related to their existing rosters and while signing new players.