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.

翻译：预测运动员表现主要依赖统计数据。除传统数据外，包括视频在内的多类型数据现已可用。然而，在运动员数据集规模较小时，如何利用这些数据进行深度学习仍面临挑战。本研究提出一种基于洞察力判断者筛选标准的特征选择策略，可提升机器学习性能。我们的机器学习模型采用能够正确评估运动员未来表现的专家所选择特征。通过分析LPGA球员采访视频，我们测试了预测其次日表现的策略。实验要求参与者观看采访后预测球员次日得分并说明理由。将来自参与者的面部特征点运动特征与元数据结合使用，其F1分数优于单独使用任一特征。研究表明，人机协同模型能够提升算法在小型数据集上的性能表现。