With the development of wearable sensors, it is now possible to assess the dynamic progression of physiological rhythms such as heart rate, breathing rate or galvanic skin response in ways and places that were previously impractical. This paper presents a new application that synchronizes the emotional patterns from these time-series in order to model athletes’ emotion during physical activity. This data analysis computes a best-fitting model for analyzing the patterns given by these measurements “in the wild”. The recording setup used to measure and synchronize multiple biometric physiological sensors can be called a BAN (Body Area Network) of personal measurements. By monitoring physical activity, it is now possible to calculate optimal patterns for managing athletes’ emotion. The data provided here are not restricted by a lab environment but close to the “ground truth” of ecologically valid physiological changes. The data allow the provision of accurate feedback to athletes about their emotion (e.g. in cases such as an unexpected increase or an expected decrease of physiological activity).