Sports Analytics Weekly by kubeia.io - 37/2024
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🎲 Betting
Sports Betting Markets Are NOT Efficient - Here's Why (Explained)
youtube.com - Peter Webb
In this video, we explore the fascinating question: Are betting markets truly efficient? Many academics argue that betting markets are highly efficient, but after over 20 years of consistently beating the market, I can confidently say otherwise.Join me as I share my journey from the early days of predicting football draws to becoming a master of Betfair trading. Along the way, I reveal key strategies and insights that prove the inefficiencies present in betting markets, allowing savvy bettors to consistently find value.
đź“ť Sports Analytics
Netherlands – Germany 2-2: Important Lessons In Transition Versus Structure
betweentheposts.net - Sander IJtsma
The Nations League gives us very interesting fixtures, and this edition of Netherlands – Germany was no exception. In a match with a very high level of play, both on an individual level and from a tactical perspective, it was a classic structure versus transition battle that ran out to a balanced score line. Both teams showed what they can do when they click, but struggle to maintain the balance.
mlsedigital/mplbasketball: Basketball plotting library for use with matplotlib
github.com
A Python plotting library to visualize basketball data, created by the Sport Performance Lab (SPL) at Maple Leaf Sports
SPL-Open-Data: A collection of open-source sports biomechanics datasets collected by MLSE's Sport Performance Lab.
github.com
The SPL Open Data repository acts as a collection of biomechanics datasets collected by Maple Leaf Sports
🤖 Machine Learning
Convex Optimization Live Blog
argmin.net - Ben Recht
I’ll be live blogging my graduate course on convex optimization this semester (Fall 2024). The course is based on the text Convex Optimization by Stephen Boyd and Lieven Vandenberghe.
🧮 Statistics
Jensen’s Inequality As An Intuition Tool
moontower.ai - Kris Abdelmessih
I came across a tool from mathematics called Jensen’s Inequality. I’m going to explain the rule, provide intuitive examples, then end by pointing you to real-world applications.A warning to math whizzes — I don’t have formal math training so this post is divorced from pedagogical context. Yes, there will be numerical examples. But the real goal is for readers to recognize when the domain they are reasoning about is subject to the surprising predictions of Jensen’s Inequality. For most of us, the value of this tool is how it nudges our intuition to better predictions, not in the direct application of a formula.
The fourth quadrant: a map of the limits of statistics
edge.org - Nassim Taleb
Statistical and applied probabilistic knowledge is the core of knowledge; statistics is what tells you if something is true, false, or merely anecdotal; it is the "logic of science"; it is the instrument of risk-taking; it is the applied tools of epistemology; you can't be a modern intellectual and not think probabilistically—but... let's not be suckers. The problem is much more complicated than it seems to the casual, mechanistic user who picked it up in graduate school. Statistics can fool you. In fact it is fooling your government right now. It can even bankrupt the system (let's face it: use of probabilistic methods for the estimation of risks did just blow up the banking system).