Background/Objectives: The recommendations included in medical guidelines (GLs) provide important help to medical professionals for making clinical decisions regarding the diagnosis and treatment of various diseases. However, there are no systematic methods to measure the helpfulness of GLs. Thus, we developed an objective assessment of GLs which indicates their helpfulness and quality. We hypothesized that a simple mathematical analysis of ‘Recommendations’ and ‘Evidence’ would suffice. Methods: As a proof of concept, a mathematical analysis was conducted on the ‘2020 European Society of Cardiology Guidelines on Sports Cardiology and Exercise in Patients with Cardiovascular Disease Guideline’ (SCE-guideline). First, the frequencies of Classes of Recommendations (CLASS) and the Levels of Evidence (LEVEL) (n = 159) were analysed. Then, LEVEL areas under CLASS were calculated to form a certainty index (CI: −1 to +1). Results: The frequency of CLASS I (‘to do’) and CLASS III (‘not to do’) was relatively high in the SCE-guideline (52.2%). Yet, the most frequent LEVEL was C (41.2–83.8%), indicating only a relatively low quality of scientific evidence in the SCE-guideline. The SCE-guideline showed a relatively high CI (+0.57): 78.4% certainty and 21.6% uncertainty. Conclusions: The SCE-guideline provides substantial help in decision making through the recommendations (CLASS), while the supporting evidence (LEVEL) in most cases is of lower quality. This is what the newly introduced certainty index showed: a tool for ‘quality control’ which can identify specific areas within GLs, and can promote the future improvement of GLs. The newly developed mathematical analysis can be used as a Guideline for the Guidelines, facilitating the assessment and comparison of the helpfulness and quality of GLs.