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TRUVACE RECORD VERSION record: TRV-2026-0237 version: 1 kind: certified reason: Certified into the record timestamp: 2026-07-17T14:31:20.520947Z status: published lens: g_space sector: sports headline: Application of Artificial Intelligence in Real-Time Strategic Decision Support System for Sports Competitions dek: 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 Mon… gain_title: An AI decision support system using ID3 entropy and enhanced Monte Carlo tree search delivered real-time basketball strategy evaluation in about 3.13 seconds with 74% win rate and 84% decision rationality. problem_title: (none) trace_subject: (none) gain_reading: An AI decision support system using ID3 entropy and enhanced Monte Carlo tree search delivered real-time basketball strategy evaluation in about 3.13 seconds with 74% win rate and 84% decision rationality. gain_evidence: decision-making time of about 3.13 seconds on average | system makes a 74% win rate and 84% decision rationality problem_reading: (none) problem_evidence: (none) quick_read: Researchers built a real-time strategic decision support system for sports that combines an ID3 decision tree using entropy change with an enhanced Monte Carlo tree search that picks the maximum UCT node. Tested in basketball contexts, the system averaged about 3.13 seconds per decision and was reported to reach 74% win rate and 84% decision rationality. The result suggests automated tactical advice can be generated fast enough for in-game use and correlate with better outcomes, but the source gives limited detail on sample size, opponent strength, or deployment conditions, and notes advanced strategy use was rare (0 to 0.04), leaving generalizability and real-world coaching integration uncertain. limitation: tag: Evidence-backed gain key_points: Study applied ID3 algorithm with attribute entropy value change as selection criterion to build decision tree for real-time sports data. | Enhanced Monte Carlo tree search selects the maximum UCT function node to ensure optimal solution. | System evaluated on basketball games where advanced strategy use ranged from 0 to 0.04 among players. rundown: The authors describe a system that processes live competition data using entropy-based splitting and UCT maximization, aiming to improve competitive efficiency and match outcomes in athletic events. Testing focused on basketball, reporting both speed and quality metrics for the automated recommendations and noting low baseline adoption of advanced strategies among players. sources: - peer_reviewed | Ingegneria Sismica | https://doi.org/10.65102/is2026367 | 2026-04-30 prev: 0000000000000000000000000000000000000000000000000000000000000000
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