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We propose a foundation model for soccer, which is able to predict subsequent actions in a soccer match from a given input sequence of actions. As a proof of concept, we train a transformer architecture on three seasons of data from a professional soccer league. We quantitatively and qualitatively compare the performance of this transformer architecture to two baseline models: a Markov model and a multi-layer perceptron. Additionally, we discuss potential applications of our model.

This paper introduces an approach to predicting the next event in a soccer match, a challenge bearing remarkable similarities to the problem faced by Large Language Models (LLMs). Unlike other methods that severely limit event dynamics in soccer, often abstracting from many variables or relying on a mix of sequential models, our research proposes a novel technique inspired by the methodologies used in LLMs. These models predict a complete chain of variables that compose an event, significantly simplifying the construction of Large Event Models (LEMs) for soccer. Utilizing deep learning on the publicly available WyScout dataset, the proposed approach notably surpasses the performance of previous LEM proposals in critical areas, such as the prediction accuracy of the next event type. This paper highlights the utility of LEMs in various applications, including betting and match analytics. Moreover, we show that LEMs provide a simulation backbone on which many analytics pipelines can be built, an approach opposite to the current specialized single-purpose models. LEMs represent a pivotal advancement in soccer analytics, establishing a foundational framework for multifaceted analytics pipelines through a singular machine-learning model.

It has been known to be difficult to generate adequate sports updates from a sequence of vast amounts of diverse live tweets, although the live sports viewing experience with tweets is gaining the popularity. In this paper, we focus on soccer matches and work on building a system to generate live updates for soccer matches from tweets so that users can instantly grasp a match's progress and enjoy the excitement of the match from raw tweets. Our proposed system is based on a large pre-trained language model and incorporates a mechanism to control the number of updates and a mechanism to reduce the redundancy of duplicate and similar updates.

An object in motion looks fundamentally different from an object at rest -- especially to a computer. To get a better idea of this concept, let's imagine a film strip of a sprinter running: The person and pose in one frame look drastically different from the next frame, right? Making sense of dynamic objects is taking on new importance as cities begin incorporating IoT devices like smart cameras to streamline municipal life. The town of Yuma, Arizona, is a great example of this. The city recently installed cameras on streetlights that can detect when cars, bicycles, and pedestrians travel through intersections, and it uses that data to optimise signal switching.

When the soccer World Cup kicks off this Thursday, imagine watching it as a 3D "hologram" on your kitchen table. That may not be far off thanks to a new technique that turns YouTube videos into 3D reconstructions of matches. The key to the approach is a convolutional neural network--a type of artificial intelligence algorithm loosely modeled on the part of the brain that processes visual data--that researchers trained to estimate how far the surfaces of each player are from the camera that recorded the match. The network analyzed 12,000 2D images of players extracted from the soccer videogame FIFA alongside the corresponding 3D data from the game engine to learn how the two correlate. That allowed it to estimate depth maps for players from unseen 2D images.

Thousands of attendees of the 2017 Champions League final in Cardiff, Wales were mistakenly identified as potential criminals by facial recognition technology used by local law enforcement. According to the Guardian, the South Wales police scanned the crowd of more than 170,000 people who traveled to the nation's capital for the soccer match between Real Madrid and Juventus. The cameras identified 2,470 people as criminals. Having that many potential lawbreakers in attendance might make sense if the event was, say, a convict convention, but seems pretty high for a soccer match. As it turned out, the cameras were a little overly-aggressive in trying to spot some bad guys.

Sports broadcasters are continuously seeking to make their live coverages of soccer matches more attractive. A recent innovation is the “highlight channel,” which shows the most interesting events from multiple matches played at the same time. However, switching between matches at the right time is challenging in fast-paced sports like soccer, where interesting situations often evolve as quickly as they disappear again. This paper presents the POGBA algorithm for automatically predicting highlights in soccer matches, which is an important task that has not yet been addressed. POGBA leverages spatio-temporal event streams collected during matches to predict the probability that a particular game state will lead to a goal. An empirical evaluation on a real-world dataset shows that POGBA outperforms the baseline algorithms in terms of both precision and recall.

Ask any soccer fan who will win the Euro 2016 championship and every one of them will have an opinion, fueled by a combination of patriotism, passion and hope. It's safe to say none of them will offer an opinion based on the results of more than 36,000 soccer matches held during the past 146 years and an analysis of 94 billion outcomes. That's what researcher Michael Feindt, a particle physicist who worked at the European Organization for Nuclear Research (CERN) for six years, has done. At CERN, Feindt created an algorithm to predict collisons of particles inside the Large Hadron Collider. Now he's CEO of Blue Yonder, a startup looking to commercialize the technology in retail, logistics, manufacturing and transportation, a process he describes as finding the "the possibilities of probable futures."