Introduction: The behavior of force variables measured with linear position and / or velocity transducers (LPT) is important to characterize determining qualities in sport. Monitoring through this technology in road cycling opens the picture to a possible way to do it safely associated with anthropometric variables that can predict the load to choose. Materials and methods: Cross-sectional observational study with database analysis of half squat strength tests performed with TPL (T-Force System ®) and anthropometric assessment with measurement of 5 components in 22 road cyclists of a professional team carried out in the high-performance center of the Ministry of Sports in December 2019. Results: From the anthropometric point of view, an average height of 172.36 cm (SD 5.61) was found, total weight of 63.2 Kg (SD 5.44), fat weight 13.1 Kg (SD 1.58), muscle weight 30.94 Kg (SD 3.15), residual weight 7.53 Kg (SD 0.9), bone weight 8 .01 Kg (SD 0.95), skin weight of 3.56 Kg (SD 0.25) fat percentage of 20.7% (SD 2.02), muscle percentage 48.9% (SD 2.15), residual percentage 11.9% (SD 0.76), bone percentage 12.6% (SD 0.97), percentage of skin 5.6% (SD 0.29). Strength tests with a half-squat linear position transducer showed a mean peak propulsive power (PMPP) of 414.5 Watts (SD 81.04), load estimation at a maximum repetition of 78.9 Kg (SD 13, 89), load in peak mean propulsive power (CPMPP) 63.6 Kg (SD 12.15). A strong correlation was found in favor of PMPP with muscle weight (R2 = 0.26), and also with CPMPP with muscle percentage (R2 = 0.20). Discussion and conclusions: This work shows a description of the strength tests with TPL and its association with anthropometric variables, which represents a very important aid in determining the load to choose in squat training in elite Colombian road cyclists, since it allows an adequate control of the same and a possible improvement in sports performance associated with a probable decrease in the risk of injury. Keywords: (Road cycling, strength training, anthropometry, velocity-based training)
