Quantitative sensory evaluation of tennis rackets

This research aimed to develop reliable methods to quantitatively capture subjective data, which can then be used to enable more sophisticated decision-making during the development process of tennis rackets. All of the main categories of sensory evaluation were applied, making this research the most comprehensive work to date in the area of tennis racket feel. The main part of this research investigated the effectiveness of a trained panel in evaluating racket feel. This panel went through a process of screening, training, and calibration.The panel was significantly more reliable compared to naïve sensory assessors. The most effective element of the process seemed to be the exclusion of insensitive assessors. There was, however, some form of training effect present, particularly for assessors without prior experience. The panel data also proved to be externally valid. Naïve consumers were able to generate reliable data when using rapid sensory profiling techniques. Moreover, the reliability of several protocol conditions was examined. Whether to use an open protocol, in which assessors hit with each other without instructions, or a closed protocol, in which conditions are more controlled, did not affect the reliability of ratings. However, the type of assessment scale significantly affected the reliability of ratings. Paired comparisons yielded significantly more reliable data compared to Visual Analogue Scales.The results of various discrimination tests also revealed that tennis players are more sensitive to changes in swing weight compared to frame stiffness. Moreover, the sound and damping properties of specific frame or string modes seem to influence our perception of tennis rackets. Future research should therefore focus on sensory cues used by players when making judgments and how these cues affect preferences. These findings will support racket engineers in making the development process more effective by collecting reliable subjective data and fine-tuning the most important parameters.