Skip to content
GCC AI Research

Why the World Cup is a random process with a drift

KAUST ·

Research KAUST Math Sports Analysis

Summary

KAUST Professor Peter Markowich discusses the role of mathematics in football, describing a match as a random process with a drift. The randomness stems from player conditions, referee decisions, weather, and more, while the drift represents the higher probability of the better team winning. He notes that the complexity arising from 11 players on each side increases the randomness compared to sports like tennis. Why it matters: This perspective highlights the interplay of chance and skill in sports, offering a mathematical lens for understanding game dynamics.

Keywords

KAUST · football · mathematics · random process · drift

Read original article →

Get the weekly digest

Top AI stories from the GCC region, every week.

Related

Learn to control

MBZUAI ·

Patrick van der Smagt, Director of AI Research at Volkswagen Group, discussed the use of generative machine learning models for predicting and controlling complex stochastic systems in robotics. The talk highlighted examples in robotics and beyond and addressed the challenges of achieving quality and trust in AI systems. He also mentioned his involvement in a European industry initiative on trust in AI and his membership in the AI Council of the State of Bavaria. Why it matters: Understanding control in robotics, along with trust in AI, are key issues for further development of autonomous systems, especially in industrial applications within the GCC region.

SGD from the Lens of Markov process: An Algorithmic Stability Perspective

MBZUAI ·

A Marie Curie Fellow from Inria and UIUC presented research on stochastic gradient descent (SGD) through the lens of Markov processes, exploring the relationships between heavy-tailed distributions, generalization error, and algorithmic stability. The research challenges existing theories about the monotonic relationship between heavy tails and generalization error. It introduces a unified approach for proving Wasserstein stability bounds in stochastic optimization, applicable to convex and non-convex losses. Why it matters: The work provides novel insights into the theoretical underpinnings of stochastic optimization, relevant to researchers at MBZUAI and other institutions in the region working on machine learning algorithms.