Research on Predicting Tennis Movements Based on Transformer Deep Learning

Abstract

Tennis, a prototypical turn-based sport, poses challenges in momentum prediction due to dynamic nonlinear interactions and ambiguous momentum definitions. Existing models exhibit static limitations, failing to capture real-time momentum shifts and nonlinear technical interactions. This study addresses these gaps by developing a Transformer-based framework integrating 12 dynamic features like break efficiency  and serve velocity volatility , and defining momentum as the second derivative of win probability as , validated via non parametric tests . The model employs exponential moving average (EMA) and Bézier curve analysis for dynamic weighting, overcoming static parameter constraints. Empirical validation using 2023 Wimbledon (Nadal vs. Sinner) and US Open data demonstrates second critical point prediction, identifying serve-break interaction as the momentum core. The framework advances prior art by operationalizing momentum scientifically, enabling real-time tactical tools such as 1st serve  alerts and aligning with ATP training protocols. For new matches, it recommends monitoring technical stability (e.g., Nadal’s baseline consistency) and contextual adaptation. Model limitations like extreme score volatility highlight the need for physiological (HRV) and environmental (temperature humidity friction) integration. This research establishes a universal paradigm for quantifying momentum in tennis and other turn-based sports, bridging empirical observation and data-driven competitive strategy.