Sports Analytics Weekly by kubeia.io - 42/2023
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🎲 Betting
One day I'll miss 5, 6, or 7 picks that I send you here, and what are now praises will turn into complaints and even insults. And obviously, I don't want that either.
twitter.com - @nishikoripicks
Yesterday, I shared 2 FREE picks on Twitter with odds of 4.16 and 3.27, both winners. This follows wins at 3.2 on October 5th and 2.81 on October 4th.While I'm pleased with you making money, I experienced a bit of imposter syndrome after the reaction on Twitter. Here's why
đź“ť Sports Analytics
The Problem with Outliers
winningwithanalytics.com
Outliers are unusually extreme observations that can potentially cause two problems:Invalidating the homogeneity assumption that all of the observations have been generated by the same behavioural processes; andUnduly influencing any estimated model of the performance outcomesA crucial role of exploratory data analysis is to identify possible outliers (i.e. anomaly detection) to inform the modelling process
How sweeper-keeper Manuel Neuer changed world football
youtube.com - Sam Tighe
In football, there are moments that define a tactical trend that'll come to dominate the game for years to come. On WhoScored Explained this week, Sam Tighe takes a look at the role of sweeper-keeper and the moment Manuel Neuer changed world football.
The Reep Fallacy
winningwithanalytics.com - Bill Gerrard
Charles Reep was the pioneer of soccer analytics, using statistical analysis to support the effectiveness of the long-ball gameReep’s principal finding was that most goals are scored from passing sequences with fewer than five passesHughes and Franks have shown that Reep’s interpretation of the relationship between the length of passing sequences and goals scored is flawed – the “Reep fallacy” of analysing only successful outcomesReep’s legacy for soccer analytics is mixed; partly negative because of its association with a formulaic approach to tactics but also positive legacy in developing a notational system, demonstrating the possibilities for statistical analysis football and having a significant impact on practitioners
🧮 Statistics
Causal inference as a blind spot of data scientists | Science
dzidas.com - Dzidas Martinaitis
Throughout much of the 20th century, frequentist statistics dominated the field of statistics and scientific research. Frequentist statistics primarily focus on the analysis of data in terms of probabilities and observed frequencies. Causal inference, on the other hand, involves making inferences about cause-and-effect relationships, which often goes beyond the scope of traditional frequentist statistical methods.Causal inference has a long history, but it gained more prominent attention in the latter half of the 20th century. This increased interest was partly due to advancements in statistical methods and the development of causal inference frameworks. In the 1980s, the work of Judea Pearl on causal inference significantly contributed to the field which continued into the 21st century. Economists and social scientists were among the first to recognize the advantages of these emerging causal inference techniques and incorporated in their research.
Aliens made this rock: The post-hoc probability fallacy in biology, finance and cosmology
mathinvestor.org - David H Bailey
What is the fallacy in the above argument? First of all, modeling each measurement as a random variable of four equiprobable digits, and then assuming all measurements are independent (so that we can blithely multiply probabilities) is a very dubious reckoning. In real rocks, the measurement at one point is constrained by physics and geology to be reasonably close to that of nearby points. Presuming that every instance in the space of 1080 theoretical digit strings is equally probable as a set of rock measurements is an unjustified and clearly invalid assumption. Thus the above reckoning must be rejected on this basis alone.