Various high-tech technologies are used extensively around the world in sports competitions especially in athletic events where scientific real-time decision-making is essential to improve competitive efficiency and match outcomes. This research proposes the introduction of the concept of entropy by applying the ID3 algorithm with the use of the attribute entropy value change as the selection criterion to develop the decision tree model for real-time sports competition data processing. Meanwhile, an enhanced Monte Carlo tree search algorithm can select the maximum UCT function node to ensure the optimal solution. The study shows that there is a percentage of players who have used advanced strategies in their basketball games ranging from 0 to 0.04. The developed decision system can make real-time strategy evaluation for sports competitions, taking a decision-making time of about 3.13 seconds on average. In addition, the system makes a 74% win rate and 84% decision rationality which indicates that the decision system is quite ideal and can serve as a good reference in future sports competition real-time decision-making practices.
