Physical performance estimation in practice: A systematic review of advancements in performance prediction and modeling in cycling.

This systematic review focuses on the application of mathematical modeling in cycling performance assessment as an alternative or complement to traditional gold-standard laboratory testing, which is often costly, invasive, and time-consuming. It synthesizes findings from 52 studies that explore models for determining key performance markers such as critical power (CP), maximal lactate steady state (MLSS), and lactate threshold (LT), as well as models characterizing physiological responses, monitoring training effects, energy balance, and predicting competitive performance. While mathematical models, including those integrating wearable technology and machine learning, offer practical advantages for regular monitoring and training optimization, they currently do not replace gold-standard methods due to limitations in accuracy and individual variability. The review highlights the importance of individualized, goal-oriented modeling approaches that balance simplicity and precision, and underscores the potential of emerging technologies to enhance real-time, field-based performance evaluation in cycling and related endurance sports.