Donald Trump wants Washington Commanders to name $3.7billion stadium after him The ugly gossip about Marjorie Taylor Greene swirling in DC... no wonder she's giving this'nothing to see here' performance of a lifetime: KENNEDY Tupac's family hid his final secret for decades. Donald Trump's new city-destroying nuclear missile'is spotted for the first time' as planespotter photographs it on hush-hush test flight The truth about Aaron Rodgers's secret'wife': Family lift the lid on the NFL's biggest mystery... and finally put to bed those swirling rumors Singer Grande shows off her 40 hand'prison' tattoos at Wicked: For Good premiere in Paris Insiders blow lid on top secret actor'blacklist' at Paramount that's tearing Hollywood apart and start naming names White House space sabotage plot EXPOSED: The truth behind the NASA war that tore Trump's inner circle in two Wild image shows how Simone Biles would look next to Olivier Rioux... after he made his college basketball debut Southern city morphs into New York's'tiny twin' as Big Apple residents flock there in droves to escape woke mayor Succession star Sarah Snook's new thriller is the best show of the year - its brings every parent's worst nightmare to life in spectacular fashion and I binged all eight episodes in one sitting Fears as Days of Our Lives is beset by string of tragedies... leaving producers desperately scrambling to save iconic show Soap icon turned ordained minister who flirted with Andy Warhol steps out in LA... can you guess who? She was an award-winning Teacher of the Year. Jeremy Renner's film partner claims he sent her explicit photos and videos to woo her then threatened the unthinkable when they fell out MORE: Scientists discover extraterrestrial relics in the first samples from moon's mysterious far side Two mysterious flashes have been spotted on the moon's surface, sparking a debate over what just struck our nearest neighbor in the solar system. Astronomer Daichi Fujii, curator of the Hiratsuka City Museum in Japan, captured the first of these bright flashes on October 30, revealing a large round dot briefly illuminating the moon's surface before disappearing.

Invasion team sports such as soccer produce a high-dimensional, strongly coupled state space as many players continuously interact on a shared field, challenging quantitative tactical analysis. Traditional rule-based analyses are intuitive, while modern predictive machine learning models often perform pattern-matching without explicit agent representations. The problem we address is how to build player-level agent models from data, whose learned values and policies are both tactically interpretable and robust across heterogeneous data sources. Here, we propose Expandable Decision-Making States (EDMS), a semantically enriched state representation that augments raw positions and velocities with relational variables (e.g., scoring of space, pass, and score), combined with an action-masking scheme that gives on-ball and off-ball agents distinct decision sets. Compared to prior work, EDMS maps learned value functions and action policies to human-interpretable tactical concepts (e.g., marking pressure, passing lanes, ball accessibility) instead of raw coordinate features, and aligns agent choices with the rules of play. In the experiments, EDMS with action masking consistently reduced both action-prediction loss and temporal-difference (TD) error compared to the baseline. Qualitative case studies and Q-value visualizations further indicate that EDMS highlights high-risk, high-reward tactical patterns (e.g., fast counterattacks and defensive breakthroughs). We also integrated our approach into an open-source library and demonstrated compatibility with multiple commercial and open datasets, enabling cross-provider evaluation and reproducible experiments.

Ultimate is a sport where points are scored by passing a disc and catching it in the opposing team's end zone. In Ultimate, the player holding the disc cannot move, making field dynamics primarily driven by other players' movements. However, current literature in team sports has ignored quantitative evaluations of when players initiate such unlabeled movements in game situations. In this paper, we propose a quantitative evaluation method for movement initiation timing in Ultimate Frisbee. First, game footage was recorded using a drone camera, and players' positional data was obtained, which will be published as UltimateTrack dataset. Next, players' movement initiations were detected, and temporal counterfactual scenarios were generated by shifting the timing of movements using rule-based approaches. These scenarios were analyzed using a space evaluation metric based on soccer's pitch control reflecting the unique rules of Ultimate. By comparing the spatial evaluation values across scenarios, the difference between actual play and the most favorable counterfactual scenario was used to quantitatively assess the impact of movement timing. We validated our method and show that sequences in which the disc was actually thrown to the receiver received higher evaluation scores than the sequences without a throw. In practical verifications, the higher-skill group displays a broader distribution of time offsets from the model's optimal initiation point. These findings demonstrate that the proposed metric provides an objective means of assessing movement initiation timing, which has been difficult to quantify in unlabeled team sport plays.

In many real-world complex systems, the behavior can be observed as a collection of discrete events generated by multiple interacting agents. Analyzing the dynamics of these multi-agent systems, especially team sports, often relies on understanding the movement and interactions of individual agents. However, while providing valuable snapshots, event-based positional data typically lacks the continuous temporal information needed to directly calculate crucial properties such as velocity. This absence severely limits the depth of dynamic analysis, preventing a comprehensive understanding of individual agent behaviors and emergent team strategies. To address this challenge, we propose a new method to simultaneously complete the velocity of all agents using only the event-based positional data from team sports. Based on this completed velocity information, we investigate the applicability of existing team sports analysis and evaluation methods. Experiments using soccer event data demonstrate that neural network-based approaches outperformed rule-based methods regarding velocity completion error, considering the underlying temporal dependencies and graph structure of player-to-player or player-to-ball interaction. Moreover, the space evaluation results obtained using the completed velocity are closer to those derived from complete tracking data, highlighting our method's potential for enhanced team sports system analysis.

Soccer is a sport played on a pitch where effective use of space is crucial. Decision-making during transitions, when possession switches between teams, has been increasingly important, but research on space evaluation in these moments has been limited. Recent space evaluation methods such as OBSO (Off-Ball Scoring Opportunity) use scoring probability, so it is not well-suited for assessing areas far from the goal, where transitions typically occur. In this paper, we propose OBPV (Off-Ball Positioning Value) to evaluate space across the pitch, including the starting points of transitions. OBPV extends OBSO by introducing the field value model, which evaluates the entire pitch, and by employing the transition kernel model, which reflects positional specificity through kernel density estimation of pass distributions. Experiments using La Liga 2023/24 season tracking and event data show that OBPV highlights effective space utilization during counter-attacks and reveals team-specific characteristics in how the teams utilize space after positive and negative transitions.

In professional basketball, the accurate prediction of scoring opportunities based on strategic decision-making is crucial for space and player evaluations. However, traditional models often face challenges in accounting for the complexities of off-ball movements, which are essential for accurate predictive performance. In this study, we propose two mathematical models to predict off-ball scoring opportunities in basketball, considering both pass-to-score and dribble-to-score movements: the Ball Movement for Off-ball Scoring (BMOS) and the Ball Intercept and Movement for Off-ball Scoring (BIMOS) models. The BMOS adapts principles from the Off-Ball Scoring Opportunities (OBSO) model, originally designed for soccer, to basketball, whereas the BIMOS also incorporates the likelihood of interception during ball movements. We evaluated these models using player tracking data from 630 NBA games in the 2015-2016 regular season, demonstrating that the BIMOS outperforms the BMOS in terms of scoring prediction accuracy. Thus, our models provide valuable insights for tactical analysis and player evaluation in basketball.

Modeling of real-world biological multi-agents is a fundamental problem in various scientific and engineering fields. Reinforcement learning (RL) is a powerful framework to generate flexible and diverse behaviors in cyberspace; however, when modeling real-world biological multi-agents, there is a domain gap between behaviors in the source (i.e., real-world data) and the target (i.e., cyberspace for RL), and the source environment parameters are usually unknown. In this paper, we propose a method for adaptive action supervision in RL from real-world demonstrations in multi-agent scenarios. We adopt an approach that combines RL and supervised learning by selecting actions of demonstrations in RL based on the minimum distance of dynamic time warping for utilizing the information of the unknown source dynamics. This approach can be easily applied to many existing neural network architectures and provide us with an RL model balanced between reproducibility as imitation and generalization ability to obtain rewards in cyberspace. In the experiments, using chase-and-escape and football tasks with the different dynamics between the unknown source and target environments, we show that our approach achieved a balance between the reproducibility and the generalization ability compared with the baselines. In particular, we used the tracking data of professional football players as expert demonstrations in football and show successful performances despite the larger gap between behaviors in the source and target environments than the chase-and-escape task.

Analysis of invasive sports such as soccer is challenging because the game situation changes continuously in time and space, and multiple agents individually recognize the game situation and make decisions. Previous studies using deep reinforcement learning have often considered teams as a single agent and valued the teams and players who hold the ball in each discrete event. Then it was challenging to value the actions of multiple players, including players far from the ball, in a spatiotemporally continuous state space. In this paper, we propose a method of valuing possible actions for on- and off-ball soccer players in a single holistic framework based on multi-agent deep reinforcement learning. We consider a discrete action space in a continuous state space that mimics that of Google research football and leverages supervised learning for actions in reinforcement learning. In the experiment, we analyzed the relationships with conventional indicators, season goals, and game ratings by experts, and showed the effectiveness of the proposed method. Our approach can assess how multiple players move continuously throughout the game, which is difficult to be discretized or labeled but vital for teamwork, scouting, and fan engagement.

Though last Sunday's Tokyo assembly elections garnered the most media attention, another contest came in a close second, even if only two people were involved. Fourteen-year-old Sota Fujii's record-setting winning streak of 29 games of shogi was finally broken on July 2 when he lost a match to 22-year-old Yuki Sasaki. Fujii has turned into a media superstar in the past year because of his youth and exceptional ability in a game that non-enthusiasts may find too cerebral to appreciate. The speed of Fujii's ascension to headline status has been purposely accelerated by the media, which treats him as not just a prodigy, but as the vanguard figure of a pastime in which the media has a stake. Press photos of Fujii's matches show enormous assemblies of reporters, video crews and photographers hovering over the kneeling opponents.

The record-setting winning streak of a 14-year-old shogi sensation has turned the spotlight on another new phenomenon shaking up the centuries-old Japanese board game -- the use of artificial intelligence to improve players' skills. Sota Fujii, a junior high school student from Seto, Aichi Prefecture, set the all-time record for 29 consecutive victories on Monday, beating Yasuhiro Masuda, a 19-year-old pro. Fujii's victory "symbolizes the beginning of a new era," said Yoshiharu Habu, a shogi legend and ninth dan who became the first player to sweep all seven major titles of the game in 1996, describing it as "a historic feat." And similar to the games chess and go, advanced shogi players, including Fujii, have turned to high-tech machines and computers, utilizing software to brush up their skills. The Japan Shogi Association began organizing matches between top pros and AI-equipped robots in 2012.