This study explores the utility of electrodermal activity (EDA) potentials, specifically Differential Dermal Potentials (DDP) in distinguishing sports performers from non-performers. The DDP signals are acquired non-invasively from the fingers of the sportspersons while in resting state. For this purpose, a particular protocol to acquire the data has been devised. This includes a 10 minute long initial supine rest period, then a contoured guided meditation session of 10 min Bhramari followed by 5 min Aum Chanting and finally another 10 min long supine rest period. Thereafter, Extended Kalman Filter (EKF) models are fitted to each 2 min segment of the 10 min pre-meditation and 10 min post-meditation acquired data. Certain key signal features are extracted from these models in line with the EKF-Estimated Measurements (EKF-EM) paradigm. It is observed that a particular parameter denoted as \(p_{com}\) belongs to two totally segregated clusters for the two classes of sports performers and non-performers. Thereafter, a classification study is performed to validate the goodness of assessing a new candidate as a performer or a non-performer based on the derived EKF-EM model features. As expected, this classification is also 100 \(\%\) accurate without any false positives or negatives. More exhaustive studies using this methodology might be useful in sports talent identification.