Sports Analytics Weekly by kubeia.io - 40/2024
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🎲 Betting
Sports Betting Apps Are Even More Toxic Than You Imagined
bloomberg.com - Ira Boudway
Pro bettors have taken to disguising themselves as gambling addicts so sportsbooks keep the free money flowing.
🧮 Statistics
When Data Is Missing, Scientists Guess. Then Guess Again.
quantamagazine.org - Matt Von Hippel
Across the social and biological sciences, statisticians use a technique that leverages randomness to deal with the unknown.
Classical Statistical (In-Sample) Intuitions Don't Generalize Well: A Note on Bias-Variance Tradeoffs, Overfitting and Moving from Fixed to Random Designs
arxiv.org - Alicia Curth
Abstract:The sudden appearance of modern machine learning (ML) phenomena like double descent and benign overfitting may leave many classically trained statisticians feeling uneasy -- these phenomena appear to go against the very core of statistical intuitions conveyed in any introductory class on learning from data. The historical lack of earlier observation of such phenomena is usually attributed to today's reliance on more complex ML methods, overparameterization, interpolation and/or higher data dimensionality. In this note, we show that there is another reason why we observe behaviors today that appear at odds with intuitions taught in classical statistics textbooks, which is much simpler to understand yet rarely discussed explicitly. In particular, many intuitions originate in fixed design settings, in which in-sample prediction error (under resampling of noisy outcomes) is of interest, while modern ML evaluates its predictions in terms of generalization error, i.e. out-of-sample prediction error in random designs. Here, we highlight that this simple move from fixed to random designs has (perhaps surprisingly) far-reaching consequences on textbook intuitions relating to the bias-variance tradeoff, and comment on the resulting (im)possibility of observing double descent and benign overfitting in fixed versus random designs.