Olympic-style weightlifting involves complex and technical movements where accurate tracking of barbell motion is crucial for performance analysis. In this paper, we present a computer vision based framework that first corrects for perspective distortion caused by varying camera height and distance, then employs a rule-based algorithm to classify snatch trajectories into four distinct types. Preliminary investigation on 6000 frames suggests 70% classification accuracy. Building on these labels, eight key barbell kinematic variables were calculated and utilized three—vertical peak height ( \(Y_{\text {max}}\) ), initial horizontal setup ( \(X_{\text {1}}\) ), and bar drop efficiency ( \(Y_{\text {catch}}\) ) to generate a consolidated 0–4 performance score, mapped to five qualitative categories from “Very Bad” to “Excellent”. This two fold approach, comprising trajectory classification and score calculation, was validated by a sports scientist, ensuring its reliability in helping athletes optimize lifting techniques by providing insights into barbell trajectory patterns.