EXplainable AI (XAI) was created to address the issue of Machine Learning's lack of transparency. Its methods are expanding, as are the ways of evaluating them, including human performance-based evaluations of explanations. These evaluations allow us to quantify the contribution of XAI algorithms to human decision-making. This work performs accuracy and response time measurements to evaluate SHAP explanations on an e-sports prediction task. The results of this pilot experiment contradict our intuitions about the beneficial potential of these explanations and allow us to discuss the difficulties of this evaluation methodology.