Minimally invasive surgery (MIS) is becoming increasing popular and widely adopted on account of relative post-operative advantages (shorter hospital stays, less scarification, trauma and post-operative pain) it offers patients when compared to open surgery. Nonetheless, the surge in demand for MIS procedures are not met with commensurate availability of experts in the field, thus leading to excessive stress attributable to increased case load, and an increase in the number of surgery interns requiring training with far less MIS experts to provide it. Also, musculoskeletal discomforts experienced by MIS surgeons have been attributed to ergonomic factors among other causes, and a required verification of the viability of fundamentals of laparoscopic surgery (FLS) as a valid ergonomic discriminator between traditional MIS and robot-assisted MIS was tested and validated. Real-time, subject-centered, and objective quantification of surgical skills has long been a challenge. A proposed solution is presented here involving the application of complexity theory (time delay and Hurst exponent principles) to the analysis of phase space reconstructions of time series data, generated by periodic changes in Euler coordinates of surgical graspers while being used by MIS novices and experts. A comparison of Hurst exponent and time delay values over multiple iterations of the same task provides quantitative insight on MIS skills improvement and experience.