Scientific reasoning in sports and exercise science utilizes inductive and deductive approaches. Models play a major role in both approaches for hypothesis formulation, testing, and refinement. This current opinion proposes guidelines to enhance model evaluation and application. We discuss the complementary nature of inductive and deductive reasoning in model creation and refinement, emphasizing how this complementarity maximizes the progression of scientific knowledge and highlighting the importance of falsifiability in this process. Key model characteristics, including predictive accuracy, representativeness, complexity, intricacy, explanatory power, and generalizability, are examined with examples from human physical performance research. A two-dimensional schematic is presented to evaluate models based on their predictive accuracy and representativeness, categorizing them into two categories: data-driven models and theory-driven models. This categorization ranges from low to high levels of predictability and representativeness, including an idealized target model, illustrating the progression toward ideal characteristics of models. Guidelines for model selection, evaluation, and improvement are provided, emphasizing the importance of maintaining an equilibrium between predictive accuracy and representativeness. We also propose avenues to increase models` predictive accuracy and representativeness following the initial model assessment. The proposed guidelines are intended to promote transparent scientific practices and encourage the use of modelling to help knowledge advancement in sports and exercise science through complementary inductive and deductive approaches.
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