Hattrick is a free web-based probabilistic football manager game with over 200,000 users competing for titles at national and international levels. Launched in Sweden in 1997 as part of an MSc project, the game's slow-paced design has fostered a loyal community, with many users remaining active for decades. Hattrick's game-engine mechanics are partially hidden, and users have attempted to decode them with incremental success over the years. Rule-based, statistical and machine learning models have been developed to aid this effort and are widely used by the community. However, these models or tools have not been formally described or evaluated in the scientific literature. This study is the first to explore Hattrick using structure learning techniques and Bayesian networks, integrating both data and domain knowledge to develop models capable of explaining and simulating the game engine. We present a comprehensive analysis assessing the effectiveness of structure learning algorithms in relation to knowledge-based structures, and show that while structure learning may achieve a higher overall network fit, it does not result in more accurate predictions for selected variables of interest, when compared to knowledge-based networks that produce a lower overall network fit. Additionally, we introduce and publicly share a fully specified Bayesian network model that matches the performance of top models used by the Hattrick community. We further demonstrate how analysis extends beyond prediction by providing a visual representation of conditional dependencies, and using the best performing Bayesian network model for in-game decision-making. To support future research, we make all data, graphical structures, and models publicly available online.

Russia pushed Ukrainian forces out of most of the territory they controlled in the Russian region of Kursk during the past week, raising questions about whether a weeklong US intelligence cutoff materially helped the Russian counterattack. The US said it had restored intelligence sharing and military aid to Ukraine on Tuesday night, after Ukraine agreed to a ceasefire plan discussed in Riyadh for nine-and-a-half hours. Russian efforts to recapture Kursk intensified on March 6, a day after the White House cut off military and intelligence assistance to Ukraine. Russian forces attacked 32 times in Kursk, said Ukraine's general staff. According to Russian military reporters, Russia had prioritised that front, moving some of its best drone operators there and deploying electronic warfare to prevent Ukrainian drone counterattacks.

A counterattack in soccer is a high speed, high intensity direct attack that can occur when a team transitions from a defensive state to an attacking state after regaining possession of the ball. The aim is to create a goal-scoring opportunity by convering a lot of ground with minimal passes before the opposing team can recover their defensive shape. The purpose of this research is to build gender-specific Graph Neural Networks to model the likelihood of a counterattack being successful and uncover what factors make them successful in professional soccer. These models are trained on a total of 20863 frames of synchronized on-ball event and spatiotemporal (broadcast) tracking data. This dataset is derived from 632 games of MLS (2022), NWSL (2022) and international soccer (2020-2022). With this data we demonstrate that gender-specific Graph Neural Networks outperform architecturally identical gender-ambiguous models in predicting the successful outcome of counterattacks. We show, using Permutation Feature Importance, that byline to byline speed, angle to the goal, angle to the ball and sideline to sideline speed are the node features with the highest impact on model performance. Additionally, we offer some illustrative examples on how to navigate the infinite solution search space to aid in identifying improvements for player decision making. This research is accompanied by an open-source repository containing all data and code, and it is also accompanied by an open-source Python package which simplifies converting spatiotemporal data into graphs. This package also facilitates testing, validation, training and prediction with this data. This should allow the reader to replicate and improve upon our research more easily.

Infantry Soldiers with 1st Battalion, 8th Infantry Regiment, 3rd Armored Brigade Combat Team, 4th Infantry Division, fire an FGM-148 Javelin during a combined arms live fire exercise in Jordan on August 27, 2019, in support of Eager Lion - file photo. A small group of maneuvering infantry soldiers will soon be able to target and destroy enemy tanks at night from distances up to 4.5 kilometers (2.8 miles) -- by firing portable, man-carried Javelin Anti-Tank Missiles engineered with a new generation of targeting optics. The U.S. Army and Raytheon plan to enter production of a new Lightweight Command Launch Unit for the Javelin designed to bring a new level of "precision lethality to an infantry squad." The new Lightweight CLU unit enables much greater standoff distance for infantry attacking tanks by doubling the attack range from 2.5km to 4.5km, developers said. "You have to be able to speed up the kill chain and detect the adversary before he can detect you. You want to get a launch shot off before he knows you are there. It all starts with sensing," Tommy Boccardi, Javelin Domestic Business Development, Raytheon, told Warrior.