In sports, players transitioning between leagues often experience changes in performance statistics due to differences in competition level and player pools. For example, G League players (part of the NBA`s minor league system) may see declines in performance metrics when called up to the NBA. Quantifying league translation factors, i.e., the expected difference in performance between leagues, is crucial for accurately contextualizing player performance and understanding key differences between leagues. We present a new method for constructing league translation factors using a matching method and difference-in-differences (DD) estimator, providing a causal estimate of how a player`s existing statistics might have appeared in a different league. Unlike traditional approaches that rely on league-wide averages or -scores, our method constructs a comparable player pool and accounts for potential aging effects. We apply this approach to construct G League-to-NBA translation factors and compare it to the "same season" method, which examines players competing in both leagues within the same season. Our findings show that most performance statistics decline when players transition from the G League to the NBA. The DD approach produces translation factors that are directionally similar but generally smaller in magnitude than the same season approach, providing a more conservative and stable estimate.
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