Sports Analytics Weekly by kubeia.io - 9/2024

I have recently been working on my AE horse ratings, looking at a new and perhaps more productive way of combining the AE values assigned to each attribute, than simply multiplying them together. This blog entry will examine my work with a neural network approach, the Weka freeware software and the results I have achieved.

Liverpool are over performing their expected points, does that make them lucky?

In the fast-evolving landscape of professional basketball, particularly within the NBA, the concept of "pace" has surged to the forefront as a critical element shaping the modern game. In this article we use seconds per possession as a measure of pace. This measure not only quantifies the speed of play but also illuminates how teams strategize to maximize their scoring opportunities within the constraints of the shot clock.

Until recently, soccer referees operated with minimal technology assistance. Following the successful introduction of communication devices and goal-line technology, as well as the use of video review systems in various other athletic competitions, soccer leagues around the world implemented Video Assistant Referee (VAR) technology to further reduce mistakes and improve referee performance. This paper investigates the effect of Video Assistant Referee systems on home field advantage in soccer using a staggered adoption difference-in-differences framework and data from 16 leagues between 2009 and 2019. We find that the implementation of VAR had negligible effects on home field advantage despite impacting various match statistics for both home and away teams. These results have important implications for the impact of referees on home field advantage, especially in light of recent literature.

Unlocking the next level in object detection technology, YOLOv9 emerges as a beacon of innovation in the field. Pioneered by Chien-Yao Wang and his team, this latest iteration of the YOLO series redefines the boundaries of efficiency and accuracy. With revolutionary techniques like Programmable Gradient Information (PGI) and the Generalized Efficient Layer Aggregation Network (GELAN), YOLOv9 tackles the longstanding challenges of information loss and computational overhead head-on. Join us on a journey into the heart of YOLOv9, where breakthroughs pave the way for unparalleled performance in real-time object detection.

The Synthetic Data Vault (SDV) is a Python library designed to be your one-stop shop for creating tabular synthetic data. The SDV uses a variety of machine learning algorithms to learn patterns from your real data and emulate them in synthetic data.