In this work we present SPL-BEV, a method to localize soccer players on a pitch from a monocular RGB camera. SPL-BEV features a network with few parameters that does not need to make any explicit object detection before localization is made. With SPL-BEV we show increased performance on the Spiideo SoccerNet SynLoc dataset compared to the best provided baseline result. The SPL-BEV system samples features from the U-Net feature space using bi-linear interpolation, guided by camera calibration, to generate features at grid points across multiple planes in a 3D world coordinate system. This forms a voxel feature space, which is then processed into grid cell detections on the ground plane, with final location refinement through x/y correction. The code for SPL-BEV is also published open source on GitHub.