Factors that determine individual age-related decline rates in physical performance are poorly understood and prediction poses a challenge. Linear and quadratic regression models are usually applied, but often show high prediction errors for individual athletes. Machine learning approaches may deliver more accurate predictions and help to identify factors that determine performance decline rates. We hypothesized that it is possible to predict the performance development of a master athlete from a single measurement, that prediction by a machine learning approach is superior to prediction by the average decline curve or an individually shifted decline curve, and that athletes with a higher starting performance show a slower performance decline than those with a lower performance. The machine learning approach was implemented using a multilayer neuronal network. Results showed that performance prediction from a single measurement is possible and that the prediction by a machine learning approach was superior to the other models. The estimated performance decline rate was highest in athletes with a high starting performance and a low starting age, as well as in those with a low starting performance and high starting age, while the lowest decline rate was found for athletes with a high starting performance and a high starting age. Machine learning was superior and predicted trajectories with significantly lower prediction errors compared to conventional approaches. New insights into factors determining decline trajectories were identified by visualization of the model outputs. Machine learning models may be useful in revealing unknown factors that determine the age-related performance decline.

决定个人与年龄相关的身体表现下降率的因素知之甚少，预测提出了挑战。通常应用线性和二次回归模型，但通常对单个运动员显示出很高的预测误差。机器学习方法可以提供更准确的预测并帮助识别决定性能下降率的因素。我们假设可以通过单次测量来预测大师级运动员的表现发展，机器学习方法的预测优于平均下降曲线或单独移动的下降曲线的预测，并且起点较高的运动员性能显示出比性能较低的性能下降更慢。机器学习方法是使用多层神经网络实现的。结果表明，单一测量的性能预测是可能的，并且机器学习方法的预测优于其他模型。起跑成绩高、起跑年龄低的运动员、起跑成绩低、起跑年龄高的运动员估计成绩下降率最高，起跑成绩高、起跑年龄高的运动员下降率最低。高起点年龄。与传统方法相比，机器学习具有优越性，并且预测的轨迹具有显着降低的预测误差。通过模型输出的可视化确定了对决定下降轨迹的因素的新见解。