Sports Analytics Weekly by kubeia.io - 44/2024
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đź“ť Sports Analytics
How To Get a Job in Sports Analytics
youtube.com
On this episode, Ted and Ravi answer frequently asked questions about getting started in sports analytics using their extensive knowledge and experience in the industry. They get into how to look for a job, the skills you need, and what to expect once you land a job. If you have more questions you want answered, leave them in the comments down below!
đź’° Quantitative Finance
Understanding the Invisible Tail of a Power Law
quantatrisk.com - Pawel Lachowicz
Understanding the “invisible tail” of a power law distribution is crucial for accurate extreme value analysis, especially in fields where rare, extreme events have a large impact. In finance, natural disaster modeling, and engineering, rare events, or outliers, are disproportionately impactful. The theory behind the invisible tail demonstrates that traditional methods for estimating risk or central moments often overlook the real extent of extreme values’ influence.In this article, we’ll explore the concept of invisible tails in power law distributions, how they are mathematically defined and decomposed, and why they are essential for managing and estimating risk. Additionally, we’ll illustrate this with a Python example to show the decomposition of moments into visible and hidden parts in a financial time-series context.
🤖 Machine Learning
Solve Any Data Analysis Problem
manning.com - David Asboth
Complete eight data science projects that lock in important real world skills–along with a practical process you can use to learn any new technique quickly and efficiently.Solve Any Data Analysis Problem guides you through eight common scenarios you'll encounter as a data scientist or analyst. As you explore each project, you’ll also master a proven process for quickly learning new skills developed by author and Half Stack Data Science podcast host David Asboth.
Pandas Workout
manning.com - Reuven Lerner
Pandas Workout hones your pandas skills to a professional-level through two hundred exercises, each designed to strengthen your pandas skills. You’ll test your abilities against common pandas challenges such as importing and exporting, data cleaning, visualization, and performance optimization. Each exercise utilizes a real-world scenario based on real-world data, from tracking the parking tickets in New York City, to working out which country makes the best wines. You’ll soon find your pandas skills becoming second nature—no more trips to StackOverflow for what is now a natural part of your skillset.
🧮 Statistics
Moving to a World Beyond “p < 0.05”
tandfonline.com - Ronald L. Wasserstein, Allen L. Schirm & Nicole A. Lazar
Some of you exploring this special issue of The American Statistician might be wondering if it’s a scolding from pedantic statisticians lecturing you about what not to do with p-values, without offering any real ideas of what to do about the very hard problem of separating signal from noise in data and making decisions under uncertainty. Fear not. In this issue, thanks to 43 innovative and thought-provoking papers from forward-looking statisticians, help is on the way.
Dispelling myths about randomisation
bps.org.uk - Marcus Munafò
But when we can randomise, that gives us remarkable inferential power. The lack of causal pathways between how we allocate participants to conditions (our randomisation procedure – hopefully, something more robust than tossing a coin!) and other factors is critical. If our randomisation mechanism influences our exposure (which by definition it should) and nothing else (ditto), and we see a difference in our outcome, then this difference must have been caused by the exposure we manipulated. But a lot remains poorly understood about exactly how and why randomisation has this magic property of allowing us to infer cause and effect. And this leads to misconceptions about what we should report in randomised studies.I want to dispel a couple of common but persistent myths.
🌩 Forecasting
Using mathematics to better understand cause and effect
phys.org
Experts studying large atmospheric circulation patterns and their impacts on global weather would like to know how these systems might change with warming climates. Here, many variables come into play: ocean and air temperatures and pressures, ocean currents and depths, and even details of the Earth's rotation over time. But which variables cause which measured effects?
That is where information theory comes in as the framework to formulate causality. Adrián Lozano-Durán, an associate professor of aerospace at Caltech, and members of his group both at Caltech and MIT have developed a method that can be used to determine causality even in such complex systems.
SURD mathematically breaks down the contributions of each variable in a system to its unique, redundant, and synergistic components of causality. The sum of all these contributions must satisfy a conservation-of-information equation that can then be used to figure out the existence of hidden causality, i.e., variables that could not be measured or that were thought not to be important. If the hidden causality turns out to be too large, the researchers know they need to reconsider the variables they included in their analysis.