No matter the sport, every team is trying to get an edge over the competition. The front office of any organization is always looking for innovative ways to make sure the product on the field reaches its peak. That's why Sevilla FC, one of La Liga's top soccer clubs, has teamed with IBM and its watsonx generative AI to develop a new way of evaluating players in the scouting department. Sevilla FC introduced Scout Advisor Tuesday. It's an innovative tool built by IBM's watsonx to revamp its recruitment process.

Fox News Flash top sports headlines are here. Check out what's clicking on Foxnews.com. The PGA Tour is making major changes to its schedule and how several of its events are played as LIV Golf's second season gets underway. The PGA Tour ratified a motion Tuesday that reduces fields for eight designated events in 2024 to between 70 and 80 golfers with no 36-hole cuts. The tour has not announced which events will be affected, but the majors, the FedEx Cup Playoffs and the Players Championship will not be included in the changes.

An artificial intelligence has beaten four of the world's best human drivers on three different tracks in the racing video game Gran Turismo Sport, by gaining ground at the most difficult parts of a track. The AI, named GT Sophy, was able to execute tactical moves such as using an opponent's slipstream to boost itself forwards and block its opponents from passing. Peter Wurman at Sony AI in New York and his colleagues trained the system using deep reinforcement learning, a type of machine learning that uses rewards and penalties to teach the AI's neural network how to win. During training, GT Sophy, which was running on a separate computer, played the game on up to 20 PlayStation 4 consoles simultaneously. The team gave the AI the ability to accelerate, brake and steer, along with real-time information on the position of the cars in the game, including its own, and a map of the next 6 seconds of the track, which meant sight of a longer distance ahead when the AI travelled faster.

Technology helps in tracking things ball speed, camera in the stump, third umpiring, etc. And now this has taken a new form. In this article, we will focus on IPL 2021 and the use of AI and data science. Without an umpire we cannot imagine cricket, right? But with technology, this won't be so far.

One of the main goals of online competitive games is increasing player engagement by ensuring fair matches. These games use rating systems for creating balanced match-ups. Rating systems leverage statistical estimation to rate players' skills and use skill ratings to predict rank before matching players. Skill ratings of individual players can be aggregated to compute the skill level of a team. While research often aims to improve the accuracy of skill estimation and fairness of match-ups, less attention has been given to how the skill level of a team is calculated from the skill level of its members. In this paper, we propose two new aggregation methods and compare them with a standard approach extensively used in the research literature. We present an exhaustive analysis of the impact of these methods on the predictive performance of rating systems. We perform our experiments using three popular rating systems, Elo, Glicko, and TrueSkill, on three real-world datasets including over 100,000 battle royale and head-to-head matches. Our evaluations show the superiority of the MAX method over the other two methods in the majority of the tested cases, implying that the overall performance of a team is best determined by the performance of its most skilled member. The results of this study highlight the necessity of devising more elaborated methods for calculating a team's performance -- methods covering different aspects of players' behavior such as skills, strategy, or goals.

Online competitive games have become a mainstream entertainment platform. To create a fair and exciting experience, these games use rating systems to match players with similar skills. While there has been an increasing amount of research on improving the performance of these systems, less attention has been paid to how their performance is evaluated. In this paper, we explore the utility of several metrics for evaluating three popular rating systems on a real-world dataset of over 25,000 team battle royale matches. Our results suggest considerable differences in their evaluation patterns. Some metrics were highly impacted by the inclusion of new players. Many could not capture the real differences between certain groups of players. Among all metrics studied, normalized discounted cumulative gain (NDCG) demonstrated more reliable performance and more flexibility. It alleviated most of the challenges faced by the other metrics while adding the freedom to adjust the focus of the evaluations on different groups of players.

A major artificial intelligence milestone has been passed after an AI algorithm was able to defeat some of the world's best players at the real-time strategy game StarCraft II. Researchers at leading AI firm DeepMind developed a programme called AlphaStar capable of reaching the top eSport league for the popular video game, ranking among the top 0.2 per cent of all human players. A paper detailing the achievement, published in the scientific journal Nature, reveals how a technique called reinforcement learning allowed the algorithm to essentially teach itself effective strategies and counter-strategies. "The history of progress in artificial intelligence has been marked by milestone achievements in games. Ever since computers cracked Go, chess and poker, StarCraft has emerged by consensus as the next grand challenge," said David Silver, a principal research scientist at DeepMind.

As Mr. Elias realized, Pluribus knew when to bluff, when to call someone else's bluff and when to vary its behavior so that other players couldn't pinpoint its strategy. "It does all the things the best players in the world do," said Mr. Elias, 32, who has won a record four titles on the World Poker Tour. "And it does a few things humans have a hard time doing." Experts believe the techniques that drive this and similar systems could be used in Wall Street trading, auctions, political negotiations and cybersecurity, activities that, like poker, involve hidden information. "You don't always know the state of the real world," said Noam Brown, the Facebook researcher who oversaw the Pluribus project.

Computer scientists have developed a card-playing bot, called Pluribus, capable of defeating some of the world's best players at six-person no-limit Texas hold'em poker, in what's considered an important breakthrough in artificial intelligence. Two years ago, a research team from Carnegie Mellon University developed a similar poker-playing system, called Libratus, which consistently defeated the world's best players at one-on-one Heads-Up, No-Limit Texas Hold'em poker. The creators of Libratus, Tuomas Sandholm and Noam Brown, have now upped the stakes, unveiling a new system capable of playing six-player no-limit Texas hold'em poker, a wildly popular version of the game. In a series of contests, Pluribus handedly defeated its professional human opponents, at a level the researchers described as "superhuman." When pitted against professional human opponents with real money involved, Pluribus managed to collect winnings at an astounding rate of $1,000 per hour.