This paper introduces a new benchmarking protocol for camera calibration in sports, named ProCC, which is designed to evaluate camera calibration methods more fairly and comprehensively. The protocol is model-agnostic, meaning it works with any camera model, and it evaluates calibration using the reprojection of accurately known 3D objects. This approach addresses the limitations of existing benchmarks that rely on homography-based methods, which are insufficient for capturing the full capabilities of camera calibration in bridging the 3D world to the image. The paper highlights the importance of using more accurate and diverse ground-truth data, such as semantic annotations of sports field elements, rather than relying solely on image annotations. The proposed protocol is tested on the World Cup 2014, CARWC, and SoccerNet datasets, demonstrating its effectiveness in providing fairer evaluations of camera calibration methods. The results show that the new protocol outperforms existing benchmarks, particularly in handling non-planar sports field elements and image distortions. The paper also discusses the need for more accurate camera models, such as those incorporating radial distortion, to meet the high standards required in sports broadcasting. The proposed protocol enables a more systematic and comprehensive evaluation of camera calibration, paving the way for improved accuracy in sports analytics.This paper introduces a new benchmarking protocol for camera calibration in sports, named ProCC, which is designed to evaluate camera calibration methods more fairly and comprehensively. The protocol is model-agnostic, meaning it works with any camera model, and it evaluates calibration using the reprojection of accurately known 3D objects. This approach addresses the limitations of existing benchmarks that rely on homography-based methods, which are insufficient for capturing the full capabilities of camera calibration in bridging the 3D world to the image. The paper highlights the importance of using more accurate and diverse ground-truth data, such as semantic annotations of sports field elements, rather than relying solely on image annotations. The proposed protocol is tested on the World Cup 2014, CARWC, and SoccerNet datasets, demonstrating its effectiveness in providing fairer evaluations of camera calibration methods. The results show that the new protocol outperforms existing benchmarks, particularly in handling non-planar sports field elements and image distortions. The paper also discusses the need for more accurate camera models, such as those incorporating radial distortion, to meet the high standards required in sports broadcasting. The proposed protocol enables a more systematic and comprehensive evaluation of camera calibration, paving the way for improved accuracy in sports analytics.