In baseball, WAR (Wins Above Replacement) is a comprehensive metric to quantify a player’s contribution to team success, relative to a replacement-level player. The idea has slowly migrated to other sports. In cricket, however, WAR remains underdeveloped. Most public evaluations still rely on averages, strike rates, or wickets — useful,

In machine learning, a common deployment strategy is to replace an existing model with one that performs better overall. Another common strategy refines this approach by limiting deployment to user segments or regions where the improvements are clear. Both approaches allow regressions: new errors on cases that the old model

Machine learning models don't improve like traditional software. When we "update" a model, it sometimes begins to mishandle cases it previously solved—an outcome known as regression or “forgetting.” This issue is well-studied in continual learning, where models learn multiple tasks sequentially (French, 1999). Standard solutions

The Hungarian algorithm (Kuhn–Munkres) efficiently finds optimal one-to-one matches between treated and control units by minimizing total matching cost (typically Euclidean distance in covariate space). It has been used for estimating treatment effects via matching. But it has a limitation: it is strictly one-to-one. In many causal inference settings,

K-Means performance depends heavily on how clusters are initialized. While k-means++ improves over random starts by spreading centroids apart, it’s still greedy and can lock into suboptimal configurations—especially in noisy or high-dimensional data. This post explores a simple tweak: lookahead initialization. For each candidate seed, we simulate a