Real-time Win Probability and Latent Player Ability via STATS X in Team Sports

• URL: http://arxiv.org/abs/2602.19513v1
• Date: Mon, 23 Feb 2026 05:00:44 GMT
• Title: Real-time Win Probability and Latent Player Ability via STATS X in Team Sports
• Abstract summary: This study proposes a statistically grounded framework for real-time win probability evaluation and player assessment in team sports.<n>We introduce a continuous dominance indicator (T-score) that maps final scores to real values consistent with win/lose outcomes.<n>We also define a latent contribution index, STATS X, which quantifies a player's involvement in favorable dominance intervals.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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