  Humans are routinely asked to evaluate the performance of other individuals,
separating success from failure and affecting outcomes from science to
education and sports. Yet, in many contexts, the metrics driving the human
evaluation process remain unclear. Here we analyse a massive dataset capturing
players' evaluations by human judges to explore human perception of performance
in soccer, the world's most popular sport. We use machine learning to design an
artificial judge which accurately reproduces human evaluation, allowing us to
demonstrate how human observers are biased towards diverse contextual features.
By investigating the structure of the artificial judge, we uncover the aspects
of the players' behavior which attract the attention of human judges,
demonstrating that human evaluation is based on a noticeability heuristic where
only feature values far from the norm are considered to rate an individual's
performance.
