Motion tracking software for assessing laparoscopic surgical proficiency has been proven to be effective in differentiating between expert and novice performances. However, with several indices that can be generated from the software, there is no set threshold that can be used to benchmark performances. The aim of this study was to identify the best possible algorithm that can be used to benchmark expert, intermediate and novice performances for objective evaluation of psychomotor skills. 12 video recordings of various surgeons were collected in a blinded fashion. Data from our previous study of 6 experts and 23 novices was also included in the analysis to determine thresholds for performance. Video recording were analyzed both by the Kinovea 0.8.15 software and a blinded expert observer using the CAT form. Multiple algorithms were tested to accurately identify expert and novice performances. ½ L + $$ \raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$3$}\right. $$ A + $$ \raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$6$}\right. $$ J scoring of path length, average movement and jerk index respectively resulted in identifying 23/24 performances. Comparing the algorithm to CAT assessment yielded in a linear regression coefficient R² of 0.844. The value of motion tracking software in providing objective clinical evaluation and retrospective analysis is evident. Given the prospective use of this tool the algorithm developed in this study proves to be effective in benchmarking performances for psychomotor skills evaluation.