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arXiv Machine Learning By Robert Jimerson Jr

A Statistical and Machine Learning Framework for Quantifying Offensive Impact in Professional Box Lacrosse

Read the original on arXiv Machine Learning →

The paper presents a statistical and machine‑learning framework for estimating expected goals (xG) and attributing offensive involvement in professional box lacrosse. Using 1,006 manually annotated shot attempts from 13 Rochester Knighthawks games, the authors evaluate logistic regression, random forest, and extremely randomized trees, finding that a contextual baseline random forest achieves the lowest log loss and Brier score. They also introduce a shot‑based expected assist metric and an exploratory expected pick value (xPV) metric, noting that xPV’s magnitude is indistinguishable from noise but shows a directional pattern in permutation tests.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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