Understanding team formations and player positioning is crucial for tactical analysis in football (soccer). This paper presents a flexible method for formation recognition and player position assignment in football using predefined static formation templates and cost minimization from spatiotemporal tracking data, called EFPI. Our approach employs linear sum assignment to optimally match players to positions within a set of template formations by minimizing the total distance between actual player locations and template positions, subsequently selecting the formation with the lowest assignment cost. To improve accuracy, we scale actual player positions to match the dimensions of these formation templates in both width and length. While the method functions effectively on individual frames, it extends naturally to larger game segments such as complete periods, possession sequences or specific intervals (e.g. 10 second intervals, 5 minute intervals etc.). Additionally, we incorporate an optional stability parameter that prevents unnecessary formation changes when assignment costs differ only marginally between time segments. EFPI is available as open-source code through the unravelsports Python package.

In this article we revise the football's performance score called PlayeRank, designed and evaluated by Pappalardo et al.\ in 2019. First, we analyze the weights extracted from the Linear Support Vector Machine (SVM) that solves the classification problem of "which set of events has a higher impact on the chances of winning a match". Here, we notice that the previously published results include the Goal-Scored event during the training phase, which produces inconsistencies. We fix these inconsistencies, and show new weights capable of solving the same problem. Following the intuition that the best team should always win a match, we define the team's quality as the average number of players involved in the game. We show that, using the original PlayeRank, in 94.13\% of the matches either the superior team beats the inferior team or the teams end tied if the scores are similar. Finally, we present a way to use PlayeRank in an online fashion using modified free analysis tools. Calculating this modified version of PlayeRank, we performed an online analysis of a real football match every five minutes of game. Here, we evaluate the usefulness of that information with experts and managers, and conclude that the obtained data indeed provides useful information that was not previously available to the manager during the match.

Large Vision-Language Models (LVLMs) have achieved remarkable progress on visual perception and linguistic interpretation. Despite their impressive capabilities across various tasks, LVLMs still suffer from the issue of hallucination, which involves generating content that is incorrect or unfaithful to the visual or textual inputs. Traditional benchmarks, such as MME and POPE, evaluate hallucination in LVLMs within the scope of Visual Question Answering (VQA) using answerable questions. However, some questions are unanswerable due to insufficient information in the images, and the performance of LVLMs on such unanswerable questions remains underexplored. To bridge this research gap, we propose TUBench, a benchmark specifically designed to evaluate the reliability of LVLMs using unanswerable questions. TUBench comprises an extensive collection of high-quality, unanswerable questions that are meticulously crafted using ten distinct strategies. To thoroughly evaluate LVLMs, the unanswerable questions in TUBench are based on images from four diverse domains as visual contexts: screenshots of code snippets, natural images, geometry diagrams, and screenshots of statistical tables. These unanswerable questions are tailored to test LVLMs' trustworthiness in code reasoning, commonsense reasoning, geometric reasoning, and mathematical reasoning related to tables, respectively. We conducted a comprehensive quantitative evaluation of 28 leading foundational models on TUBench, with Gemini-1.5-Pro, the top-performing model, achieving an average accuracy of 69.2%, and GPT-4o, the third-ranked model, reaching 66.7% average accuracy, in determining whether questions are answerable. TUBench is available at https://github.com/NLPCode/TUBench.

This study employs Bayesian methodologies to explore the influence of player or positional factors in predicting the probability of a shot resulting in a goal, measured by the expected goals (xG) metric. Utilising publicly available data from StatsBomb, Bayesian hierarchical logistic regressions are constructed, analysing approximately 10,000 shots from the English Premier League to ascertain whether positional or player-level effects impact xG. The findings reveal positional effects in a basic model that includes only distance to goal and shot angle as predictors, highlighting that strikers and attacking midfielders exhibit a higher likelihood of scoring. However, these effects diminish when more informative predictors are introduced. Nevertheless, even with additional predictors, player-level effects persist, indicating that certain players possess notable positive or negative xG adjustments, influencing their likelihood of scoring a given chance. The study extends its analysis to data from Spain's La Liga and Germany's Bundesliga, yielding comparable results. Additionally, the paper assesses the impact of prior distribution choices on outcomes, concluding that the priors employed in the models provide sound results but could be refined to enhance sampling efficiency for constructing more complex and extensive models feasibly.

Esteban Granero has some good news, a little light at the end of a long, dark tunnel in Spain, where the coronavirus crisis has left more than 21,000 people dead. "The situation is terrible," says the midfielder, a league title winner with Real Madrid, "but the curve is clearly downward now; we reached the peak on the fourth [of April] and now we're on the way down. Things shift daily but we think at the end of the month, early May, the number of cases will be very low and there will be room for optimism." Granero does not speak lightly. He has been watching the trends carefully.

Esteban Granero has some good news, a little light at the end of a long, dark tunnel in Spain, where the coronavirus crisis has left more than 21,000 people dead. "The situation is terrible," says the midfielder, a league title winner with Real Madrid, "but the curve is clearly downward now; we reached the peak on the fourth [of April] and now we're on the way down.

Over the last few decades, the player recruitment process in professional football has evolved into a multi-billion industry and has thus become of vital importance. To gain insights into the general level of their candidate reinforcements, many professional football clubs have access to extensive video footage and advanced statistics. However, the question whether a given player would fit the team's playing style often still remains unanswered. In this paper, we aim to bridge that gap by proposing a set of 21 player roles and introducing a method for automatically identifying the most applicable roles for each player from play-by-play event data collected during matches.

Football fans have long been bombarded with an array of statistics ranging from the number of successful passes completed to the distance covered by each player in English Premier League matches. But that approach is blind to the context of the game and the specific role of each player. What is needed is a new system that can reveal not only what distance players covered in a game, but why they covered it and at what intensity. These statistics would tell managers and coaches who on their team is following the game plan and who is playing a game of their own. It is this method that our new research has explored.