With AI and machine learning in sports applications, organizations can use their data to improve every area of their operations. From player recruitment and performance to ticket sales, predictive analytics can help make targeted decisions and strategic changes that impact every area of a sports organization.
Teams strive to get the best players for the lowest cost and least risk. Being able to accurately evaluate a player’s dollar value — along with their risks — can save significant money for an organization while opening up payroll flexibility to build sustainable success. These optimal valuations provide guidance to arbitration hearings, contract negotiations, trade opportunities, free agent signings, and international player acquisitions for the right price.
Once a team has the ability to measure the value of a player, they can use that information to create a training strategy for each of their players that will maximize a player’s future value. Additionally, with insights from AI, organizations can provide rapid feedback on a player’s game or practice performance to highlight what they are doing well and what they can do to improve.
If you can better predict the strengths, weaknesses, and tendencies of your opponents and your own personnel, you can identify the right strategy for each game situation. Data science in sports can maximize your wins—offering trusted insights on what will likely happen after each decision in order to get the best performance.
Machine Learning can be used to predict the results of matches. For instance, in soccer, where massive sets of data are available, a model outcome can be created to predict future confrontations. AI Sport can predict the percentage of chances of winning sides of both sides, and helps by calculating the real probabilities of all the soccer matches.