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

TL;DR

This paper introduces a probabilistic framework for real-time win probability and player ability assessment in team sports, using a continuous dominance indicator and latent contribution index based on cumulative game data.

Contribution

It develops a novel stochastic model capturing game dynamics and introduces STATS X for quantifying individual player contributions, enhancing interpretability and structural evaluation.

Findings

Defines a continuous dominance indicator (T-score) aligned with game outcomes.

Formulates a time-evolving stochastic process for real-time win probability updates.

Introduces STATS X to quantify player involvement and separate team strength from fluctuations.

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.