Studded football boots and their interaction with the pitch surface play a major role in generating traction and on the risk of injuries and performance. The aim of this study was to establish a methodological framework to predict a safe zone of traction for different specific football movements in natural preloads. We measured peak pressure distribution among 17 male football players in four specific football movements (cutting 135°, sprinting, turning, and penalty kick) on artificial turf using a baseline football boot with an insole pressure sensor. A mechanical prosthetic foot was adjusted to replicate similar peak pressure distribution based on these four movements. Traction was measured under three preloads: 400, 600, and 800 N. They were lower than those measured with the players to avoid damage to the mechanical test device. This procedure was conducted for seven different outsole configurations. Rotational and translational traction was estimated for high preloads (above 2000 N) using an artificial neural network. Our findings show pressure distribution is an important bridge between subjective measurement (field tests) and objective measurement (laboratory tests) for accurate traction measurement. Artificial neural networks can aid in finding the upper and lower ranges of traction in the natural preloads. Such findings could help footwear developers, trainers, players, and governing institutions to choose an appropriate football boot outsole according to the safe zone of traction established in this study.
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