Sports Analytics Weekly by kubeia.io - 51/2024
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🎲 Betting
Betfair Scrap Premium Charge!
betangel.com - Peter Webb
The Premium charge has been completely scrapped and replaced with an Expert Fee. Betfair’s revamped pricing structure is built to create a more balanced approach to fees while reducing costs for most of those impacted.
đź“ť Sports Analytics
Why Soccer Players Are Training in the Dark
wired.com - RM Clark
Sports science company Okkulo has shown that its specially lit training environment can improve players’ visual-motor skills—and a growing number of sports are starting to test it out.
The Most Analytical Big Board Around, 2025 Superdraft Edition
americansocceranalysis.com - Paul Harvey
The MLS Superdraft takes place on Friday, and despite MLS’ best effort to make sure you don’t know about it American Soccer Analysis will step in to keep you informed. Thankfully Wyscout keeps an extensive data collection that helps to analyze the 477 players* eligible for the draft and determine who might be the best available.
đź’° Quantitative Finance
Naive Backtesting
substack.com - Anton Vorobets
Although many people are familiar with backtest overfitting and various dangers of generalizing results based on one historical realization, many still happily make conclusions about the quality of different risk measures based on such analysis.I could have done the same.
Long-term expected returns: what the best way to forecast?
alphaarchitect.com - Elisabetta Basilico
This paper examines various frameworks and proxies for forecasting long-term expected returns (E(R)) over periods of 10 to 20 years, focusing on out-of-sample performance and the impact of these forecasts on investment decisions. It compares models based on yield, valuation, and the combination of both to identify the most effective methods for predicting E(R).
🤖 Machine Learning
Maximum Likelihood Estimation and Loss Functions
github.io - Rishab Sharma
When I started learning about loss functions, I could always understand the intuition behind them. For example, the mean squared error (MSE) for regression seemed logical—penalizing large deviations from the ground-truth makes sense. But one thing always bothered me: I could never come up with those loss functions on my own. Where did they come from? Why do we use these specific formulas and not something else?This frustration led me to dig deeper into the mathematical and probabilistic foundations of loss functions. It turns out, the answers lie in a concept called Maximum Likelihood Estimation (MLE). In this blog, I’ll take you through this journey, showing how these loss functions are not arbitrary but derive naturally from statistical principles. I’ll start by defining what Maximum Likelihood Estimation (MLE) is, followed by the intricate connection between Maximum Likelihood Estimation (MLE) and Kullback-Leibler (KL) divergence. To conclude this article, I show how loss functions like Mean Squared Error loss and Binary Cross Entropy can be derived from Maximum Likelihood estimation.
🌩 Forecasting
Deep Learning for predicting rate-induced tipping
arxiv.org - Yu Huang, Sebastian Bathiany, Peter Ashwin, Niklas Boers
Abstract:Nonlinear dynamical systems exposed to changing forcing can exhibit catastrophic transitions between alternative and often markedly different states. The phenomenon of critical slowing down (CSD) can be used to anticipate such transitions if caused by a bifurcation and if the change in forcing is slow compared to the internal time scale of the system. However, in many real-world situations, these assumptions are not met and transitions can be triggered because the forcing exceeds a critical rate. For example, given the pace of anthropogenic climate change in comparison to the internal time scales of key Earth system components, such as the polar ice sheets or the Atlantic Meridional Overturning Circulation, such rate-induced tipping poses a severe risk. Moreover, depending on the realisation of random perturbations, some trajectories may transition across an unstable boundary, while others do not, even under the same forcing. CSD-based indicators generally cannot distinguish these cases of noise-induced tipping versus no tipping. This severely limits our ability to assess the risks of tipping, and to predict individual trajectories. To address this, we make a first attempt to develop a deep learning framework to predict transition probabilities of dynamical systems ahead of rate-induced transitions. Our method issues early warnings, as demonstrated on three prototypical systems for rate-induced tipping, subjected to time-varying equilibrium drift and noise perturbations. Exploiting explainable artificial intelligence methods, our framework captures the fingerprints necessary for early detection of rate-induced tipping, even in cases of long lead times. Our findings demonstrate the predictability of rate-induced and noise-induced tipping, advancing our ability to determine safe operating spaces for a broader class of dynamical systems than possible so far.