Classification of bloodstain patterns can occur at the scene or through (digital) images submitted for analysis and/or peer review. Capturing high-resolution images requires high-quality cameras or scanners and can be time consuming, especially when image stitching is needed. With the increasing use of machine learning (ML) for image analysis, handling large images is also computationally demanding. This raises the question of whether high-resolution images are necessary for accurate ML based pattern classification. In this study, we explore the role of image size on bloodstain classification by replicating an existing experiment distinguishing impact versus forward spatter, using both original and resized images. Despite expectations, reducing resolution did not significantly affect classification accuracy. This suggests that modern ML techniques may be less reliant on fine image detail than human analysts. We also examined the importance of feature selection in classification. Using only four key features showed results comparable to those obtained using all 58 from the original model. This points to the potential for simpler, more efficient models without sacrificing accuracy. These results raise important questions, while the model performed well under reduced input conditions, it is essential to understand what it relies on and why. Rather than applying ML models blindly, we emphasize the need to understand their behavior, for responsible and effective use in forensic workflows.