  Predicting athletes' performance has relied mostly on statistical data.
Besides the traditional data, various types of data, including video, have
become available. However, it is challenging to use them for deep learning,
especially when the size of the athletes' dataset is small. This research
proposes a feature-selection strategy based on the criteria used by insightful
people, which could improve ML performance. Our ML model employs features
selected by people who correctly evaluated the athletes' future performance. We
tested out a strategy to predict the LPGA players' next day performance using
their interview video. We asked study participants to predict the players' next
day score after watching the interviews and asked why. Using combined features
of the facial landmarks' movements, derived from the participants, and
meta-data showed a better F1-score than using each feature separately. This
study suggests that the human-in-the-loop model could improve algorithms'
performance with small-dataset.
