The 197th Manchester derby takes place at Etihad Stadium on Sunday, but will it be the Blues or the Reds who claim the points - and local bragging rights? This is so hard to call, for many reasons, said BBC Sport football expert Chris Sutton. Manchester United could be buoyed by their win over Burnley before the international break, but I actually have bigger doubts about what we will see from Manchester City after seeing them capitulate the way they did against Brighton. Sutton is making predictions for all 380 Premier League games this season, against AI, BBC Sport readers and a variety of guests. For week four, he takes on Only The Poets frontman Tommy Longhurst. The Reading band are charging £1 a ticket when they play the O2 Academy Brixton, in February 2026.

Fox News senior foreign affairs correspondent Greg Palkot reports on the state of the suspected terrorists in Russia and the Kremlin's'spin machine.' Spain's Ministry of the Interior, on Tuesday, announced that it is on high alert and has activated all alert and response systems to prevent jihadist attacks during the Champions League quarterfinal matches scheduled to take place in Madrid on Tuesday and Wednesday, according to reports. On Tuesday, Real Madrid will take on Manchester City, while on Wednesday, Atlético Madrid will play against Borussia Dortmund. As the quarterfinals approach, threats have been made by the Islamic State terrorist network, which has threatened drone attacks on the soccer tournament, a reminder of the resurgence of the network after several deadly attacks earlier this year in places like Iran and Moscow. The ministry, led by Fernando Grand-Marlask, said the "State Security Forces and Bodies have all their early warning and protection systems activated, as well as their response systems ready" in response to preventing a terrorist attack, according to Spanish newspaper La Vanguardia.

This paper introduces an innovative application of Large Event Models (LEMs), akin to Large Language Models, to the domain of soccer analytics. By learning the "language" of soccer - predicting variables for subsequent events rather than words LEMs facilitate the simulation of matches and offer various applications, including player performance prediction across different team contexts. We focus on fine-tuning LEMs with the WyScout dataset for the 2017-2018 Premier League season to derive specific insights into player contributions and team strategies. Our methodology involves adapting these models to reflect the nuanced dynamics of soccer, enabling the evaluation of hypothetical transfers. Our findings confirm the effectiveness and limitations of LEMs in soccer analytics, highlighting the model's capability to forecast teams' expected standings and explore high-profile scenarios, such as the potential effects of transferring Cristiano Ronaldo or Lionel Messi to different teams in the Premier League. This analysis underscores the importance of context in evaluating player quality. While general metrics may suggest significant differences between players, contextual analyses reveal narrower gaps in performance within specific team frameworks.

Automatic fact verification has received significant attention recently. Contemporary automatic fact-checking systems focus on estimating truthfulness using numerical scores which are not human-interpretable. A human fact-checker generally follows several logical steps to verify a verisimilitude claim and conclude whether its truthful or a mere masquerade. Popular fact-checking websites follow a common structure for fact categorization such as half true, half false, false, pants on fire, etc. Therefore, it is necessary to have an aspect-based (delineating which part(s) are true and which are false) explainable system that can assist human fact-checkers in asking relevant questions related to a fact, which can then be validated separately to reach a final verdict. In this paper, we propose a 5W framework (who, what, when, where, and why) for question-answer-based fact explainability. To that end, we present a semi-automatically generated dataset called FACTIFY-5WQA, which consists of 391, 041 facts along with relevant 5W QAs - underscoring our major contribution to this paper. A semantic role labeling system has been utilized to locate 5Ws, which generates QA pairs for claims using a masked language model. Finally, we report a baseline QA system to automatically locate those answers from evidence documents, which can serve as a baseline for future research in the field. Lastly, we propose a robust fact verification system that takes paraphrased claims and automatically validates them. The dataset and the baseline model are available at https: //github.com/ankuranii/acl-5W-QA

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. As part of the introduction series on sports analytics for beginners, I am writing a series of articles examining the impact and benefits of machine learning and data analytics.

One of the most important and challenging problems in football is predicting future player performance when transferred to another club within and between different leagues. In addition to being the most valuable prediction a team makes, it is also the most complex analytics task to perform as it needs to take into consideration: a) differences in playing style between the player's current team and target team, b) differences in style and ability of other players on each team, c) differences in league quality and style, and d) the role the player is desired to play. In this paper, we present a method which addresses these issues and enables us to make accurate predictions of future performance. Our Transfer Portal model utilizes a personalized neural network accounting for both stylistic and ability level input representations for players, teams, and leagues to simulate future player performance at any chosen club. Furthermore, we use a Bayesian updating framework to dynamically modify player and team representations over time which enables us to generate predictions for rising stars with small amounts of data.

As Manchester City's players returned to the home dressing room after January's exhilarating, exhausting 2-1 win over Liverpool, music shuddered from speakers. A house remix of Gregory Porter's Liquid Spirit mixed with gleeful shouts as the celebrations began. But in one corner, three men huddled quietly together. Ederson and John Stones stared at a big screen as Harry Dunn, a member of manager Pep Guardiola's backroom staff, zipped through a timeline of the match action to show a replay of Stones clearing the ball off his own goalline, with just 11mm to spare. By the time they were showered, changed and back in the tinted privacy of their cars, Ederson, Stones or any of their team-mates could open the Hudl app on their phone and watch that moment, along with every other involvement they had in the game.

As Manchester City's players returned to the home dressing room after January's exhilarating, exhausting 2-1 win over Liverpool, music shuddered from speakers. A house remix of Gregory Porter's Liquid Spirit mixed with gleeful shouts as the celebrations began. But in one corner, three men huddled quietly together. Ederson and John Stones stared at a big screen as Harry Dunn, a member of manager Pep Guardiola's backroom staff, zipped through a timeline of the match action to show a replay of Stones clearing the ball off his own goalline, with just 11mm to spare. By the time they were showered, changed and back in the tinted privacy of their cars, Ederson, Stones or any of their team-mates could open the Hudl app on their phone and watch that moment, along with every other involvement they had in the game.

Manchester City have been cautioned against the introduction of facial recognition technology, which a civil rights group says would risk "normalising a mass surveillance tool". The reigning Premier League champions are considering introducing technology allowing fans to get into the Etihad Stadium more quickly by showing their faces instead of tickets, according to the Sunday Times. If someone is recognised as having bought a ticket, they would be ushered in by a green light, and if not they would be halted with a yellow one. Hannah Couchman, the policy and campaigns officer at Liberty, said: "This is a disturbing move by Manchester City, subjecting football fans to an intrusive scan, much like taking a fingerprint, just so they can go to the Saturday game. "It's alarming that fans will be sharing deeply sensitive personal information with a private company that boasts about collecting and sharing data on each person that walks through the gate, and using this to deny people entry.

From Leicester City winning the title in 2016 to Liverpool overturning a 4-0 defeat to Barcelona in last season's Champions League, football is impossible to predict. Yet, bringing together world leading companies in data, analytics and artificial intelligence (AI), BT Sport has attempted to do just that. Combining historic performance data from sports industry leaders Opta and Squawka, BT Sport has fed the information into a machine learning model. This calculated the attacking and defensive strengths of each team, as well as the likelihood of events such as transfers and injuries, to calculate the probable scoreline of each match in the season. So where does the AI model's Premier League predictions 2019-20 place your team; where will they claim their biggest victory, and how will they perform against their biggest rivals?