This study proposes a statistically grounded framework for real-time win probability evaluation and player assessment in score-based team sports, based on minute-by-minute cumulative box-score data. We introduce a continuous dominance indicator (T-score) that maps final scores to real values consistent with win/lose outcomes, and formulate it as a time-evolving stochastic representation (T-process) driven by standardized cumulative statistics. This structure captures temporal game dynamics and enables sequential, analytically tractable updates of in-game win probability. Through this stochastic formulation, competitive advantage is decomposed into interpretable statistical components. Furthermore, we define a latent contribution index, STATS X, which quantifies a player's involvement in favorable dominance intervals identified by the T-process. This allows us to separate a team's baseline strength from game-specific performance fluctuations and provides a coherent, structural evaluation framework for both teams and players. While we do not implement AI methods in this paper, our framework is positioned as a foundational step toward hybrid integration with AI. By providing a structured time-series representation of dominance with an explicit probabilistic interpretation, the framework enables flexible learning mechanisms and incorporation of high-dimensional data, while preserving statistical coherence and interpretability. This work provides a basis for advancing AI-driven sports analytics.

翻译：本研究提出了一种基于统计学的框架，用于在计分制团队运动中实现实时胜率评估与球员能力评估，该框架依托于逐分钟累积的比赛数据。我们引入了一个连续优势指标（T分数），该指标将最终比分映射到与胜/负结果一致的实际数值，并将其表述为由标准化累积统计数据驱动的时间演化随机表示（T过程）。该结构捕捉了比赛的时间动态，并实现了比赛中胜率的顺序性、解析可处理的更新。通过这种随机表述，竞争优势被分解为可解释的统计成分。此外，我们定义了一个潜在贡献指数STATS X，用于量化球员在由T过程识别的有利优势区间内的参与程度。这使得我们能够将团队的基础实力与比赛特定的表现波动分离开来，并为团队和球员提供了一个连贯的、结构化的评估框架。尽管本文未实现人工智能方法，但我们的框架被定位为迈向与人工智能混合集成的基础性一步。通过提供一个具有明确概率解释的结构化时间序列优势表示，该框架支持灵活的学习机制和高维数据的整合，同时保持了统计的一致性和可解释性。此项工作为推进人工智能驱动的体育分析奠定了基础。