Value Analysis and Prediction Through Machine Learning Techniques for Popular Basketball Brands

Jason P. Michaud
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Abstract

For popular sports brands such as Nike, Adidas, and Puma, value often depends upon the performance of star athletes and the success of professional leagues. These leagues and players are watched closely by many around the world, and exposure to a brand may ultimately cause someone to buy a product. This can be explored statistically, and the interconnectedness of brands, athletes, and the sport of basketball are covered in this chapter. Specifically, data about the NBA and Google Ngrams data are explored in relation to the stock price of these various sports brands. This is done through both statistical analysis and machine learning models. Ultimately, it was concluded that these factors do influence the stock price of Nike, Adidas, and Puma. This conclusion is supported by the machine learning models where this diverse dataset was utilized to accurately predict the stock price of sports brands.
基于机器学习技术的热门篮球品牌价值分析与预测
对于耐克、阿迪达斯和彪马等受欢迎的运动品牌来说,其价值往往取决于明星运动员的表现和职业联赛的成功。这些联赛和球员受到世界各地许多人的密切关注,对一个品牌的曝光可能最终会导致某人购买该产品。这可以从统计上进行探讨,本章将讨论品牌、运动员和篮球运动之间的相互联系。具体来说,我们将NBA和b谷歌Ngrams的数据与这些不同运动品牌的股价进行了探讨。这是通过统计分析和机器学习模型来完成的。最终得出结论,这些因素确实影响了耐克,阿迪达斯和彪马的股价。这一结论得到了机器学习模型的支持,该模型利用这种多样化的数据集来准确预测运动品牌的股票价格。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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