• KTM
• motorbike
• motogp
• cornering
• tyre wear
• correlation index
• telemetry

Wanting to maximize the race performance, acquiring better understanding of how the bike behaviour elvolves during a race is of big interest for MotoGP teams. The main reason behind bike behaviour changes during races is to find in tyre wear: in motorsport tyres are used at high stress levels for long periods of time, inevitably causing the visco-elastic rubber to change its properties and thereby affecting the overall vehicle performance. Tyre performance and its evolution depend on many aspects, like their temperature and pressure, track conditions, track layout, riding style and how all these variables evolve through the race. Many of these variables are semi-random or very difficult to model, so a model-based approach to study this phenomena has been discarded. In this work, carried out at KTM motorsport, a statistical approach based on race telemetry data has been preferred. The aim of the thesis is to define indexes to evaluate how the vehicle behaviour and the performance in corners are changing during the race, with particular focus on tyre wear. The telemetry data collected during the races of the 2020, 2021 and 2022 MotoGP seasons have been processed, for a total of 81 races. The analysis started in the middle of the corner and then extended to corner entry, a much more complicated phase to study and model but that has a huge impact on the whole cornering performance. At first a quite simple model has been developed to have a qualitative idea of what to look for, and then data have processed also using correlation indexes. The work was totally developed using MATLAB functions and scripts.