Oliver’s four-factor model: Validation through causality

While there has been a growing interest in sports analysis in recent years, much research first focused on a classical statistical approach and later on an artificial intelligence approach. This article aims instead to propose a causal inference approach to sports analysis. In particular, the present article intends to review the famous four-factor model proposed by Dean Oliver for assessing the winning ability of National Basketball Association (NBA) teams through a causal inference approach. A structural equation model is used to validate Oliver’s model. The present paper considers the winning percentage and the factors’ statistics over entire seasons from [Formula: see text] to [Formula: see text]. The statistics for the [Formula: see text] season are considered only on a subset of the games. This is because the games played in the Orlando bubble under the particular COVID-19 situation have been regarded as outliers compared to the games played in the other NBA seasons, hence they have not been taken into account. The second goal of the article is to analyse if the fitting ability of the four-factor model changes when it is fitted over the pre[Formula: see text] and post[Formula: see text] basketball eras datasets, considering the year [Formula: see text] as the turning point for the NBA playing style.