It is largely known that the propagation rate coefficients of monomers depend on a series of factors. Molecular weight is certainly important. Similarly, resonance stability of the propagating radical plays a major role. Also, H-bonding has been identified to cause significant rate effects and polarity is speculated to impact kp. These effects have, however, always only been investigated as insular effects, and no general theory could so far be formed that would unify all different aspects in one approach. Part of this issue might be that in the classical approach, physical chemists look for scientific understanding rather than for a correlation or pure association. In complex interdependent systems, this can be a difficult endeavour since accurate data is often difficult to find, and actual causations might not be obvious. Association is much simpler to establish though via purely statistical approaches. Machine-learning (ML) harnesses this relative simplicity to predict complex behaviour of systems. Hence, the question could be raised if it is possible to correlate complex propagation rate coefficients with fundamental and readily available information of monomers without the attempt to establish exact equations reflecting the underpinning processes. If such an approach was successful the resulting correlation can ideally be used to reach a better theoretical understanding. More importantly though, if statistical association is successful (without necessarily identifying the underlying causal mechanism), then rate coefficients would in principle become predictable. Propagation rate coefficients provide an ideal scenario for testing this hypothesis since relatively accurate coefficients are available for a series of molecules. In the following, we discuss if these kinetic rate coefficients can indeed be predicted on a purely statistical basis rather than using high level ab initio calculations using transition state theory. It should be mentioned that very recently, work has been published that follows a in principle very similar approach, which underpins the validity of our methodology.