论文标题
脸可以告诉我们有关NBA前景的任何信息吗？ - 一种深度学习的方法
Can a face tell us anything about an NBA prospect? -- A Deep Learning approach
论文作者
论文摘要
统计分析和建模在世界领先的组织中越来越受欢迎，特别是对于专业的NBA团队。为此，创建了体育才能评估的精致方法和模型。在这项研究中，我们介绍了与统计数据分析的主要策略不同的观点。基于NBA团队过去遵循的策略，聘请了人类专业人士，我们部署了图像分析和卷积神经网络，以预测每个选秀课程中新起草的球员的职业轨迹。我们创建了一个数据库，该数据库由自1990年以来每名选秀的大约1500个图像数据组成。然后，我们根据他们的预期NBA职业将玩家分为五个不同的质量班级。接下来，我们在数据中培训了受欢迎的预训练的预训练的图像分类模型，并进行了一系列测试，以创建模型，从而为新秀球员的职业提供可靠的预测。这项研究的结果表明，面部特征与运动人才之间存在潜在的相关性，值得进一步研究。
Statistical analysis and modeling is becoming increasingly popular for the world's leading organizations, especially for professional NBA teams. Sophisticated methods and models of sport talent evaluation have been created for this purpose. In this research, we present a different perspective from the dominant tactic of statistical data analysis. Based on a strategy that NBA teams have followed in the past, hiring human professionals, we deploy image analysis and Convolutional Neural Networks in an attempt to predict the career trajectory of newly drafted players from each draft class. We created a database consisting of about 1500 image data from players from every draft since 1990. We then divided the players into five different quality classes based on their expected NBA career. Next, we trained popular pre-trained image classification models in our data and conducted a series of tests in an attempt to create models that give reliable predictions of the rookie players' careers. The results of this study suggest that there is a potential correlation between facial characteristics and athletic talent, worth of further investigation.
