## 4 Discussion
The findings of this study have significant practical applications for optimizing training and performance in cross-country skiing. Our model demonstrates that the gross cycle kinematics, specifically cycle length, exert a greater influence on skiing speed than the more complex angular kinematic parameters, such as joint angles. This suggests that, for daily training regimens, focusing on the analysis of cycle characteristics like cycle length may be more efficient and practical than dedicating time to the collection and analysis of joint angles. The relationship between these cycle characteristics and speed is straightforward and can be leveraged to refine technique effectively. Regarding physiological parameters, while they do influence speed, their impact is less pronounced than that of the kinematic factors when considered alongside each other. This may be due to the relatively late emergence of cross-country skiing in China, where many athletes have transitioned from other sports disciplines. Consequently, compared to physiological capabilities, technique is a critical factor in influencing performance. Interestingly, when examining the effects of aerobic and anaerobic capacities, this study found that their impact on speed was roughly equivalent, suggesting that both capacities are equally important for optimizing performance. Strength training, especially focusing on heavy strength, explosive strength, or sprint endurance, could therefore be beneficial in improving overall performance [39; 40], as these elements enhance both the aerobic and anaerobic energy systems. Moreover, our model can be used to monitor and evaluate the effectiveness of training programs by predicting a skier's speed based on movement patterns. The ability of neural networks to model the adaptive behavior of athletes is already demonstrated in other sports, such as swimming, where non-linear models have been used to track training progress in high-level female swimmers [41]. Similarly, our model can be iterated upon, continuously updating to reflect training outcomes and predict future performance, offering coaches a powerful tool for training optimization. While the neural network model demonstrates high accuracy in predicting skiing performance, its applicability is limited by the narrow demographic focus of the sample. The dataset used in this study consists of elite Chinese athletes, which limits the generalizability of the findings to other athlete groups, such as amateur skiers, athletes from different countries, or those with diverse skill levels. There might be significant differences in the biomechanical [21] and physiological [9] characteristics of faster and slower cross-countryskiers. Therefore, the model's predictions may not apply directly to non-elite athletes with different training backgrounds or physiological characteristics. Future studies should aim to include a broader, more diverse sample of athletes to validate the model's applicability across different demographics. Incorporating data from amateur skiers and athletes from different countries would provide valuable insights into the model's generalizability. Although our study focused on key physiological parameters such as VO2 max and 30 s anaerobic capacity, we did not include lactate threshold or blood lactate measurements, which are often used to assess endurance capacity and the shift between aerobic and anaerobic energy systems. Future studies could explore these metrics to provide additional insights into how the anaerobic threshold relates to skiing performance, especially when considering the differences between male and female athletes.