  Players and ball detection are among the first required steps on a football
analytics platform. Until recently, the existing open datasets on which the
evaluations of most models were based, were not sufficient. In this work, we
point out their weaknesses, and with the advent of the SoccerNet v3, we propose
and deliver to the community an edited part of its dataset, in YOLO normalized
annotation format for training and evaluation. The code of the methods and
metrics are provided so that they can be used as a benchmark in future
comparisons. The recent YOLO8n model proves better than FootAndBall in
long-shot real-time detection of the ball and players on football fields.
