Sports Analytics Weekly by kubeia.io - 24/2024
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🎲 Betting
Euro 2024 Outright Predictions
pinnacle.com
The 17th edition of the European Championships takes place this summer. Hosts Germany are among the favorites to win the tournament, as are defending champions Italy. The market leaders are England, though, closely followed by France. But where does the value lie? The Italians were available at around 1200 ahead of Euro 2020 and lifted the trophy, so can a slightly longer-priced team do likewise in Berlin on July 14 this year? Here’s a look at the leading candidates to win Euro 2024, with some longer-priced options considered too.
âš˝ Sports
Ranking all the managers at Euro 2024: Which of them was the best player?
nytimes.com - Tim Spiers
We are about to find out just how good the managers of the 24 nations at this summer’s European Championship are, but what about their playing careers?
Euro 2024 Players to Watch
theanalyst.com - Ryan Benson
With the European Championship just days away, we identify nine stars aged 23 or under as our Euro 2024 players to watch.
đź“ť Sports Analytics
He has some questions about a career in sports analytics.
columbia.edu - Andrew Gelman
Sometimes I get requests from high school students to answer questions.
đź’° Quantitative Finance
Most people don't know how to clean or QA market data
x.com - Christina Qi
Most people don't know how to clean or QA market data. Here's my quickstart guide for researchers, quant devs and engineers. (This is a HUGE thread you have been warned!
Triple Barrier Method: Python
quantinsti.com
Whenever you start using a lot of data to backtest a strategy and you would like to use the triple-barrier method, you’ll face the issue of low time efficiency by running a CPU-based computation. This article provides a great Nvidia-GPU-based solution code that you can implement and get much quicker the desired prediction feature. Quicker sounds great, doesn’t it? Let’s dive in!
🤖 Machine Learning
Understanding CatBoost!
theaiedge.io - Damien Benveniste
Catboost might be the easiest supervised learning algorithm to use today on large tabular data. It is highly parallelizable, it automatically deals with missing values and categorical variables, and even more than Xgboost, it is built to prevent overfitting. If you throw some data into it, without much work, you are pretty much guaranteed to get state-of-the-art results. This assumes your data is training-ready, but even then, it is almost too good to be true!Â