In soccer, contextual player performance metrics are invaluable to coaches. For example, the ability to perform under pressure during matches distinguishes the elite from the average. Appropriate pressure metric enables teams to assess players' performance accurately under pressure and design targeted training scenarios to address their weaknesses. The primary objective of this paper is to leverage both tracking and event data and game footage to capture the pressure experienced by the possession team in a soccer game scene. We propose a player pressure map to represent a given game scene, which lowers the dimension of raw data and still contains rich contextual information. Not only does it serve as an effective tool for visualizing and evaluating the pressure on the team and each individual, but it can also be utilized as a backbone for accessing players' performance. Overall, our model provides coaches and analysts with a deeper understanding of players' performance under pressure so that they make data-oriented tactical decisions.

EA Sports announced Tuesday that the soccer title it publishes in 2023 would be part of the new EA Sports FC brand, doing away with the FIFA name the series has used since the days of the Sega Genesis and Super NES. The announcement marks a significant break for one of the oldest and most popular continuous franchises in video game history. This story originally appeared on Ars Technica, a trusted source for technology news, tech policy analysis, reviews, and more. Ars is owned by WIRED's parent company, Condé Nast. "We're thankful for our many years of great partnership with FIFA," EA CEO Andrew Wilson said in a statement.

RoboCup, originally named the J-League, is an annual robotics and artificial intelligence (AI) competition organized by the International RoboCup Federation. During RoboCup, robots compete with other robots soccer tournaments. The idea for the competition originated in 1992, when Professor Alan Mackworth at University of British Columbia in Canada wrote a paper entitled "On Seeing Robots." In 1993, a research team in Japan drew inspiration from this paper to organize the first robot soccer competition. While RoboCup can be highly entertaining, its main objective is to showcase advancements in robotics and AI in a real-world setting.

Artificial intelligence and machine learning (AI/ML) have already transformed industries and changed the way work gets done across the enterprise. While finance has traditionally lagged behind other departments in the AI adoption curve, that's starting to change. Adoption of AI in finance is being spurred by digital natives (professionals who grew up in a connected world), with tech solutions finally delivering on the promise of AI/ML. Finance professionals accustomed to modern technology experiences in other areas of their lives are no longer willing to endure painstaking manual reviews and the threat of inaccurate data in their forecasts and plans. Outside of finance, many other areas of the businesses are far beyond cutting their teeth when it comes to using AI to improve forecasting and drive decision-making.

Artificial Intelligence (AI) is affecting the way we work, learn, shop, and now creating new opportunities for playing and watching our favorite sports. Using AI and machine learning to mimic the behavior of the likes of Cristiano Ronaldo and Lionel Messi, a team of researchers from the Institute for Big Data Analytics at Dalhousie were recently named as runners up in 2020's largest international AI soccer simulation competition, RoboCup Japan Open. This is the first time a Canadian team has made the finals for more than 10 years. International robotics competition RoboCup uses soccer simulation to promote robotics and AI research with the research findings used to advance many areas. By 2050, the competition aims to train a team of fully autonomous humanoid robots to win a soccer game against the winner of the most recent World Cup.

It is not surprise for machine learning models to provide decent prediction accuracy of soccer games outcomes based on various objective metrics. However, the performance is not that decent in terms of predicting difficult and valuable matches. A deep learning model is designed and trained on a real sequential trading data from the real prediction market, with the assumption that such trading data contain critical latent information to determine the game outcomes. A new loss function is proposed which biases the selection toward matches with high investment return to train our model. Full investigation of 4669 top soccer league matches showed that our model traded off prediction accuracy for high value return due to a certain ability to detect dark horses. A further try is conducted to depict some indicators discovered by our model for describing key features of big dark horses and regular hot horses.

The API provides pre-trained object detection models that have been trained on the COCO dataset. COCO dataset is a set of 90 commonly found objects. See image below of objects that are part of COCO dataset. In this case we care about classes -- persons and soccer ball which are both part of COCO dataset. The API also has a big set of models it supports. See table below for reference. The models have a trade off between speed and accuracy. Since I was interested in real time analysis, I chose SSDLite mobilenet v2. Once we identify the players using the object detection API, to predict which team they are in we can use OpenCV which is powerful library for image processing.

Having notched impressive victories over human professionals in Go, Atari Games, and most recently StarCraft 2 -- Google's DeepMind team has now turned its formidable research efforts to soccer. In a paper released last week, the UK AI company demonstrates a novel machine learning method that trains a team of AI agents to play a simulated version of "the beautiful game." Gaming, AI and soccer fans hailed DeepMind's latest innovation on social media, with comments like "You should partner with EA Sports for a FIFA environment!" Machine learning, and particularly deep reinforcement learning, has in recent years achieved remarkable success across a wide range of competitive games. Collaborative-multi-agent games however remained a relatively difficult research domain.

The API provides pre-trained object detection models that have been trained on the COCO dataset. COCO dataset is a set of 90 commonly found objects. See image below of objects that are part of COCO dataset. In this case we care about classes -- persons and soccer ball which are both part of COCO dataset. The API also has a big set of models it supports. See table below for reference. The models have a trade off between speed and accuracy. Since I was interested in real time analysis, I chose SSDLite mobilenet v2. Once we identify the players using the object detection API, to predict which team they are in we can use OpenCV which is powerful library for image processing.

The FIFA World Cup is about to kick off on Thursday, but researchers have developed an AI that can project a 3D soccer game right on your kitchen table. Researchers from the University of Washington, Facebook and Google have created the first end-to-end, deep learning system that transforms a YouTube video of a soccer game into a moving 3D hologram. They extracted 12,000 2D images of players from FIFA video games, and then trained a convolutional neural network on 3D player data taken from the soccer video games. Recreating something as dynamic as a soccer match isn't easy, the authors explain. 'There are numerous challenges in monocular reconstruction of a soccer game,' the researchers wrote in a new study describing the work.