The Causal Effect of the Two-For-One Strategy in the National Basketball Association

TL;DR

This paper assesses the causal impact of the two-for-one strategy in NBA games using a causal inference approach, revealing its overall positive effect on game outcomes and minimal variation across contexts.

Contribution

It introduces a causal inference framework to evaluate the two-for-one strategy's effectiveness in professional basketball, incorporating heterogeneity analysis with causal forests.

Findings

Two-for-one strategy positively affects game outcomes

No significant heterogeneity in effectiveness across contexts

Provides tactical insights for NBA teams

Abstract

This study evaluates the effectiveness of the two-for-one strategy in basketball by applying a causal inference framework to play-by-play data from the 2018-19 and 2021-22 National Basketball Association regular seasons. Incorporating factors such as player lineup, betting odds, and player ratings, we compute the average treatment effect and find that the two-for-one strategy has a positive impact on game outcomes, suggesting it can benefit teams when employed effectively. Additionally, we investigate potential heterogeneity in the strategy's effectiveness using the causal forest framework, with tests indicating no significant variation across different contexts. These findings offer valuable insights into the tactical advantages of the two-for-one strategy in professional basketball.