Cross-lingual transfer has become an effective way of transferring knowledge between languages. In this paper, we explore an often-overlooked aspect in this domain: the influence of the source language of the base language model on transfer performance. We conduct a series of experiments to determine the effect of the script and tokenizer used in the pre-trained model on the performance of the downstream task. Our findings reveal the importance of the tokenizer as a stronger factor than the sharing of the script, the language typology match, and the model size.