In this study we focus on the prediction of basketball games in the Euroleague competition using machine learning modelling. The prediction is a binary classification problem, predicting whether a match finishes 1 (home win) or 2 (away win). Data is collected from the Euroleague's official website for the seasons 2016-2017, 2017-2018 and 2018-2019, i.e. in the new format era. Features are extracted from matches' data and off-the-shelf supervised machine learning techniques are applied. We calibrate and validate our models. We find that simple machine learning models give accuracy not greater than 67% on the test set, worse than some sophisticated benchmark models. Additionally, the importance of this study lies in the "wisdom of the basketball crowd" and we demonstrate how the predicting power of a collective group of basketball enthusiasts can outperform machine learning models discussed in this study. We argue why the accuracy level of this group of "experts" should be set as the benchmark for future studies in the prediction of (European) basketball games using machine learning.

A joint venture between AGT International and the WME-IMG sports and entertainment agency, HEED's partnership with Euroleague extends long-term beyond the Final Four in Belgrade in May. The cameras and microphones will measure the energy and excitement of players and coaches, with the AI able to distinguish cheers, chants and boos. "We are looking at the fans and saying the fans are the storytellers of the game," HEED co-founder Mati Kochavi told a SportsPro Live conference in London last week. He likened the app, which has been in the works for some time, to having a robot watching the game from all angles and sending regular updates based on AI rather than human observation. "He's going to tell you that something is shifting and changing in the game, or something special is happening. He's also going to connect it to the visual in order to represent it," he said.