Actions Of Players Research Articles

АНАЛІЗ ОСОБЛИВОСТЕЙ ФОРМУВАННЯ ІНДИВІДУАЛЬНОГО СТИЛЮ ДІЯЛЬНОСТІ СПОРТСМЕНІВ У СПОРТИВНИХ ІГРАХ

The main task of the research is a general analysis of the peculiarities of the formation of the individual style of sportsmenєs activity in sports games. The research data on the problem of individualization of training of athletes are summarized. The main shortcomings and limitations of experimental studies related to the formation of an individual style of activity are analyzed. It was determined that the existing experimental and methodical works mainly solve the problems of sports training and the problems of the individual style of activity in sports games in isolation. It is proved that among the general features of game activity that are inherent in all sports games (operational nature and non-stationary conditions of activity, high mental tension, dynamism, dependence of the result on the collective actions of partners) there are also distinctive ones, primarily due to the rules of a particular game (size of playing fields, method of achieving the result, number of players). These differences determine the peculiarity of technical and tactical actions of players in different sports games and provide for the need to apply different approaches to the problems of forming an individual style of playing activity.

NarrativeGenie: Generating Narrative Beats and Dynamic Storytelling with Large Language Models

Interactive narrative in games utilize a combination of dynamic adaptability and predefined story elements to support player agency and enhance player engagement. However, crafting such narratives requires significant manual authoring and coding effort to translate scripts to playable game levels. Advances in pretrained large language models (LLMs) have introduced the opportunity to procedurally generate narratives. This paper presents NarrativeGenie, a framework to generate narrative beats as a cohesive, partially ordered sequence of events that shapes narrative progressions from brief natural language instructions. By leveraging LLMs for reasoning and generation, NarrativeGenie, translates a designer’s story overview into a partially ordered event graph to enable player-driven narrative beat sequencing. Our findings indicate that NarrativeGenie can provide an easy and effective way for designers to generate an interactive game episode with narrative events that align with the intended story arc while at the same time granting players agency in their game experience. We extend our framework to dynamically direct the narrative flow by adapting real-time narrative interactions based on the current game state and player actions. Results demonstrate that NarrativeGenie generates narratives that are coherent and aligned with the designer’s vision.

Trajectory Prediction Analysis of Basketball Players’ Movements Based on DSM Optimization Algorithm

In this paper, the action trajectory of basketball players is predicted and analyzed by the DSM optimization algorithm. Data on the movements of basketball players, including position, speed and acceleration are collected. The motion trajectory is predicted by the DSM optimization algorithm. At present, computer technology is more and more applied in sports training. Based on this, this paper studies the simulation of basketball players’ motion trajectory prediction. After describing the research background and significance, this paper constructs a human joint model and then proposes the motion process DSM algorithm to simulate the jump shot action of basketball players. In this paper, the algorithm and model are tested, and the experiment adopts the run-jump fatigue model to produce fatigue. The infrared high-speed motion capture system and three-dimensional force measuring table are used to collect the kinematics and dynamics data of the sudden stop jump shot before and after fatigue. The experimental results show that the trajectory prediction simulation proposed in this paper is feasible.

Can money buy peace? Efficiency of side‐payments to avoid costly conflicts

AbstractOn the basis of a simple two‐player bargaining game model, we investigate whether a side‐payment strategy can be used to avoid costly conflicts caused by informational barriers. Assuming that utility is transferable with zero transaction cost, we demonstrate that a side‐payment not only provides the incentives to play a peaceful strategy but also contributes to overcoming informational barriers as credible signals. In addition, we show that when actions are strategic complements, a dovish player pays a larger side‐payment while a hawkish pays a higher side‐payment if actions are strategic substitutes. When negotiating parties are homogeneous with actions being either strategic complements or substitutes for both players, it is more likely that informational barriers can be removed with a higher probability of peace equilibrium. When players are asymmetric with one player's actions being strategic complements while the other player's being strategic substitutes, informational barriers might not be removed with higher chances of costly conflicts.

TL-CStrans Net: a vision robot for table tennis player action recognition driven via CS-Transformer.

Currently, the application of robotics technology in sports training and competitions is rapidly increasing. Traditional methods mainly rely on image or video data, neglecting the effective utilization of textual information. To address this issue, we propose: TL-CStrans Net: A vision robot for table tennis player action recognition driven via CS-Transformer. This is a multimodal approach that combines CS-Transformer, CLIP, and transfer learning techniques to effectively integrate visual and textual information. Firstly, we employ the CS-Transformer model as the neural computing backbone. By utilizing the CS-Transformer, we can effectively process visual information extracted from table tennis game scenes, enabling accurate stroke recognition. Then, we introduce the CLIP model, which combines computer vision and natural language processing. CLIP allows us to jointly learn representations of images and text, thereby aligning the visual and textual modalities. Finally, to reduce training and computational requirements, we leverage pre-trained CS-Transformer and CLIP models through transfer learning, which have already acquired knowledge from relevant domains, and apply them to table tennis stroke recognition tasks. Experimental results demonstrate the outstanding performance of TL-CStrans Net in table tennis stroke recognition. Our research is of significant importance in promoting the application of multimodal robotics technology in the field of sports and bridging the gap between neural computing, computer vision, and neuroscience.

Characteristics of potential head injury situations at the FIFA World Cup Qatar 2022TM.

This exploratory video analysis study aimed to review characteristics of potential head injury situations during a men's professional international football tournament. For 64 matches of the FIFA World Cup Qatar 2022™ FIFA analysts used match footage to record all potential head injury situations. A potential head injury situation was defined as a player staying down for more than 5 s and/or requesting medical attention, and where the body impact location included the player's head. Characteristics were further recorded for match, player (i.e. player action including aerial duels), medical assessment, and outcome (e.g. substitution). Descriptive statistics are reported as well as Pearson's Chi-squared test (or Fisher's exact test) to explore potential head injury situations more likely to result in medical attention. In total, 149 potential head injury situations occurred in 56 matches (mean 2.33/match, range 0-6) involving 117 players. Eight matches resulted in no incidents. Aerial duels were the most frequent match characteristic leading to a potential head injury situation. Injury stoppage with on-pitch medical assessment occurred in 35 of the 149 potential head injury situations (23%), with pitch-side assessment also occurring in 15 situations (10%), resulting in four concussion substitutions. Players were more likely to require medical attention for potential head injuries sustained when the ball was loose (χ2 = 6.88; p = 0.038) when the injured player was jumping (FET p = 0.044) and for head-to-head contact (FET p = <0.001). Further exploration of aerial duels during match play which do and do not lead to potential and actual head injuries is recommended.

Sports Player Action Recognition based on Deep Learning

A sports auxiliary evaluation system suitable for China’s national conditions was established using big data and sports identification technology. First of all, this paper extends the data of normative behavior and constructs a normative library of scores and comparisons. The acquisition of 3D data is emphasized. The method based on Fourier descriptors is used to locate the motion accurately. In this way, better gait recognition results can be obtained. The Fourier characteristics before and after wavelet transform are compared with the actual object characteristics, and the results show that the proposed algorithm can extract the features with high precision. This scheme can obtain a more accurate identification effect. The system proposed in this paper provides a powerful means for judges to score.

ACA-Net: adaptive context-aware network for basketball action recognition.

The advancements in intelligent action recognition can be instrumental in developing autonomous robotic systems capable of analyzing complex human activities in real-time, contributing to the growing field of robotics that operates in dynamic environments. The precise recognition of basketball players' actions using artificial intelligence technology can provide valuable assistance and guidance to athletes, coaches, and analysts, and can help referees make fairer decisions during games. However, unlike action recognition in simpler scenarios, the background in basketball is similar and complex, the differences between various actions are subtle, and lighting conditions are inconsistent, making action recognition in basketball a challenging task. To address this problem, an Adaptive Context-Aware Network (ACA-Net) for basketball player action recognition is proposed in this paper. It contains a Long Short-term Adaptive (LSTA) module and a Triplet Spatial-Channel Interaction (TSCI) module to extract effective features at the temporal, spatial, and channel levels. The LSTA module adaptively learns global and local temporal features of the video. The TSCI module enhances the feature representation by learning the interaction features between space and channels. We conducted extensive experiments on the popular basketball action recognition datasets SpaceJam and Basketball-51. The results show that ACA-Net outperforms the current mainstream methods, achieving 89.26% and 92.05% in terms of classification accuracy on the two datasets, respectively. ACA-Net's adaptable architecture also holds potential for real-world applications in autonomous robotics, where accurate recognition of complex human actions in unstructured environments is crucial for tasks such as automated game analysis, player performance evaluation, and enhanced interactive broadcasting experiences.

Tennis action recognition and evaluation with inertial measurement unit and SVM

Action recognition in tennis plays a crucial role for athletes and coaches, aiding in understanding and evaluating the players' skill levels to formulate more effective training plans and tactical strategies. To enhance the recognition and grading of tennis player actions, this study introduces the use of inertial measurement units and flexible resistive sensors for data collection. An improved Support Vector Machine is employed for data classification to achieve efficient action recognition. The results demonstrated that the proposed classification algorithm achieved an average accuracy of 95.35 % in recognizing actions of elite athletes, with the highest accuracy (96.38 %) observed in forehand strokes. In the case of sub-elite athletes, the algorithm achieved an impressive average accuracy of 97.67 %. For amateur enthusiasts, the algorithm exhibited an average accuracy of 94.08 %. Furthermore, elite athletes exhibited larger peak values in the three-axis acceleration waveform during ball striking. Specifically, the absolute peak value of acceleration in the Y-axis for elite athletes reached 78 m/s², representing an increase of 39 m/s² and 8 m/s² compared to the other two levels of athletes, respectively. Additionally, on the X and Z axes, elite athletes' acceleration peak values reached 59 m/s² and 78 m/s², significantly higher than those of sub-elite athletes and amateur enthusiasts. Moreover, the acceleration curves of elite athletes demonstrated a higher overall regularity. These findings indicate that the proposed action recognition method has a significant impact on recognition and evaluation, providing valuable insights for action recognition and assessment across various domains and advancing the application of artificial intelligence technology in the field of sports.

Effects of the number of ball touches among elite youth players during small-sided soccer games.

This study investigates the effects of manipulating the number of ball touches (free play vs. two touches) on the physical and technical actions of elite male youth soccer players during 5v5 + 2 goalkeepers (GKs) small-sided games (SSGs). Players played in two different SSGs: 1) free play, where the number of touches per possession was not restricted; and 2) two touches, where players were limited to a maximum of two touches per ball possession. A total of 24 male elite youth soccer players (age: 14.79±0.71 years; body mass: 56.02±1.41 kg; body height: 164±2.12 cm) participated in the study. Players' physical metric and technical performance data were captured using a global positioning system and video camera, respectively. A paired-samples t-test or Wilcoxon Signed-Rank Test was employed to examine differences in players' technical performance variables depending on SSGs' ball-touch rules. The results show significantly more unsuccessful passes (t=-3.48; P=0.04; d=1.92) when players were limited to two touches than when there were no pass limits. The physical metrics indicate that total distance covered (Z=-2.90; P=0.001; d=0.07), meters per minute (Z=-3.44; P=0.001; d=0.11), low-speed running (Z=-2.25; P=0.02; d=0.04) and high acceleration (Z=-1.90; P=0.05; d=0.28) were significantly higher when touches were unlimited than when they were not. Soccer coaches should decide the number of touches per ball possession they allow depending on their tactical and/or physical objectives in training.

Optimizing Blockchain Protocols with Algorithmic Game Theory

Because blockchain technology is changing so quickly, different algorithms have been created to make sure that independent systems are safe, scalable, and efficient. However, as blockchain networks get bigger, protocol efficiency problems get harder to solve. This study looks at how blockchain technology and algorithmic game theory can be used together to solve these problems and suggests a new way to make blockchain systems work better. A part of mathematics called algorithmic game theory studies strategic exchanges where the results depend on the actions of many players. It can instruct us a part almost how to create blockchain frameworks work superior. Able to consider and move forward distinctive parts of convention execution, such as agreement forms, exchange preparing, and network security, by considering of blockchain clients as coherent specialists with their possess objectives. The primary portion of the paper talks approximately essential blockchain conventions, like Confirmation of Work (PoW) and Verification of Stake (PoS), and how they have issues with scaling and proficiency. At that point, we conversation around a few imperative thoughts from algorithmic amusement hypothesis, like Nash equilibrium, component plan, and sell off hypothesis. We appear how these thoughts can be used in blockchain systems. We see at how different game-theoretic strategies can alter how individuals act, make the most excellent utilize of assets, and boost the full execution of systems utilizing complex numerical models and re-enactments. A huge portion of the study is around how game-theoretic forms can be utilized to create assention strategies superior. For illustration, we see at how motivating force structures can be made so that the objectives of mineworkers or validators are adjusted with those of the organize. This brings down the chance of awful behaviour and makes the convention more solid.

Influence of Personal and Reactive Anxiety on Personality Characteristics, Cognitive Abilities and Performance of Basketball Actions

Abstract The purpose of this paper is to establish the possible influence of personal and reactive anxiety on personality characteristics, cognitive abilities and performance of basketball actions of basketball players. An empirical study is conducted with basketball players (which are also students at University of Economics – Varna - UEV) in 2016, playing basketball at the Sports Club at the UEV. To establish the level of effectiveness of the students’ game actions, their performances in 9 meetings (10 units of observation) are recorded. ANOVA is used to determine differences in basketball performance measures for individuals with relatively high levels of anxiety and for individuals with relatively low levels of anxiety. One of the indicators is “personal anxiety” (Q6). Another indicator is available. It is “reactive anxiety” (Q7). PSPP is used to analyze the dataset. The ANOVA test shows that there is a statistically significant difference (p&lt;0.05) in Q21 “Countering a shelter with successful” for the examined two groups of basketball players. The practical significance of the results refers to the effectiveness of the educational and sports process of university students. The novelty of the paper refers to the analysis of personal characteristics of basketball players (such as anxiety) on their performance in action.

Temperament structures and the effectiveness of individual play in football.

Regulatory Theory of Temperament distinguishes two traits involving the time course of responses and four relating to how energy is distributed and stored. This theory enables the presentation of entire structures of temperament traits and it is relevant for assessing an individual's ability to process stimulation and influence their performance during competitions. In our cross-sectional study, these structures are examined in relation to the offensive, defensive, and comprehensive effectiveness of an individual player's actions. The study included 91 football players aged between 20 and 31 years, who had approximately 10 years of training experience. The Formal Characteristics of Behavior - Temperament Inventory was utilized to assess temperamental traits, while a simulation game was employed to evaluate the effectiveness of each individual player's actions. Research demonstrated that football players possessing a temperament structure that is closely aligned harmonized structure with a high capacity to process stimulation showed higher levels of individual efficacy in attack, defence, and comprehensive effectiveness of performance compared to football players with a harmonized temperament structure but a low capacity to process stimulation. The obtained result emphasises the significance of specific temperament structures in terms of matching the individual's stimulation levels and the their ability to process it.

Broadening our understanding of injury mechanisms to include at-risk situations: an overview of potential injuries at the FIFA men’s World Cup Qatar 2022TM

ABSTRACT This study aims to examine and describe the characteristics of potential injury situations during a men’s professional international tournament quantified using the FIFA Football Language Medical Coding. A prospective study was conducted during the 64 matches of the FIFA World Cup Qatar 2022™, during which five analysts recorded potential injury situations from video analysis. ”Potential injuries” were recorded when players stayed down > 5 s and/or requested medical attention. Characteristics were further recorded for variables such as opponent’s action and body location. In total, 720 potential injury situations were recorded of which 139 required medical assessment. The actions which resulted in most potential injuries were running while receiving a pass (74; 10%), while passing the ball (59; 8%), and while progressing with the ball (48; 7%). Duels and ball progression led to a potential injury in 3.0% and 2.1% of all similar actions in total. Both aerial duels and ball progression led to an potential injury that required medical assessment on 0.4% of occasions. Most potential injuries involved the head (149; 21%), foot (120; 17%), or lower leg (110; 15%) with most medical assessments of the head (35; 25%), lower leg (17; 12%), and knee (15; 11%) with a median duration of 47 seconds (IQR 28–61). This study provides a detailed overview of match circumstances that may have a higher injury risk. Although some variables within the coding system need improvement to increase reliability, its use will allow a more detailed comparison of differences between high-risk player actions leading to injury and those that do not, which can improve future prevention strategies.

Learning from Ludemes: An Inventory of Common Player Actions within Tabletop Role-Playing Games (TTRPGs) to Inform Principled Design of Game-Based Learning Experiences

In this study, the authors investigate role-playing game design through the lens of instructional design practice to probe the ecosystem of tabletop role-playing games (TTRPGs) and their applied use for learning. A qualitative thematic analysis approach is used to systematically catalog and categorize all possible player actions, called ludemes, from the 5th Edition of Dungeons &amp; Dragons (D&amp;D). Through this analysis, the interplay of game mechanics, player actions, and rules of TTRPGs are explored, unveiling their potential to stimulate various psychological, environmental, and behavioral factors that positively influence learning processes. Specifically, this study’s analysis of a game’s ludemes (i.e., player actions) exposes the interrelationships, congruence, and combinations of actions with various learning processes. The identified categories of ludemes in the study pave the way toward a working vocabulary and framework for future research in the application of game-based learning and its potential to catalyze meaningful learning outcomes. From a global instructional standpoint, this study highlights the role of pedagogical affordances, such as collaborative storytelling and problem solving skills, theorizing how TTRPGs can promote cognitive, metacognitive, affective, social, and cultural learning. Toward these goals, the authors posit that role-playing, with its complex dynamics and compelling narratives, offers a robust and natural conduit for learning.

Kelly Criterion Extension: Advanced Gambling Strategy

This article introduces an innovative extension of the Kelly criterion, which has traditionally been used in gambling, sports wagering, and investment contexts. The Kelly criterion extension (KCE) refines the traditional capital growth function to better suit dynamic market conditions. The KCE improves the traditional approach to accommodate the complexities of financial markets, particularly in stock and commodity trading. This innovative method focuses on crafting strategies based on market conditions and player actions rather than direct asset investments, which enhances its practical application by minimizing risks associated with volatile investments. This paper is structured to first outline the foundational concepts of the Kelly criterion, followed by a detailed presentation of the KCE and its advantages in practical scenarios, including a case study on its application to blackjack strategy optimization. The mathematical framework and real-world applicability of the KCE are thoroughly discussed, demonstrating its potential to bridge the gap between theoretical finance and actual trading outcomes.

ANALYSIS OF TECHNICAL-TACTICAL ACTIONS OF HIGHLY QUALIFIED WOMEN FOOTBALL PLAYERS IN INTERNATIONAL MATCHES

This article investigates the scope of technical-tactical actions of highly qualified women football players in international matches. The conducted survey revealed an insufficiently high level of volume and effectiveness of technical-tactical actions of women football players of national team.

Study of Lodz Society’s Knowledge of the Circular Economy in the Textile and Clothing Industry

Abstract The textile and clothing industry is one of the most resource-intensive and polluting industries. The circular economy (CE) is one of the solutions to these problems. The textile industry has been identified as one of the most in need of transformation towards CE. The transition to a circular economy requires changes in the behavior and actions of all market players. Consumer interest in sustainability and the CE can stimulate companies to be more responsible and offer more circular solutions. This article analyses the knowledge and attitudes of consumers, adults living in Lodz (Poland), towards new circular models in the fashion industry (e.g. rent, repair). The development of the clothing rental service is still insufficient, but repair and the use of online portals to sell/buy second-hand clothing is becoming increasingly popular. However, it is still necessary to orient people towards environmentally friendly activities.

Plotting: a case study in lifted planning with constraints

We study a planning problem based on Plotting, a tile-matching puzzle video game published by Taito in 1989. The objective of this turn-based game is to remove a target number of coloured blocks from a grid by sequentially shooting blocks into the same grid. Plotting features complex transitions after every shot: various blocks are affected directly, while others can be indirectly affected by gravity. We consider modelling and solving Plotting from two perspectives. The puzzle is naturally cast as an AI Planning problem and we first discuss modelling the problem using the Planning Domain Definition Language (PDDL). We find that a model in which planning actions correspond to player actions is inefficient with a grounding-based state-of-the-art planner. However, with a more fine-grained action model, where each change of a block is a planning action, solving performance is dramatically improved. We also describe two lifted constraint models, able to capture the inherent complexities of Plotting and enabling the application of efficient solving approaches from SAT and CP. Our empirical results with these models demonstrates that they can compete with, and often exceed, the performance of the dedicated planning solvers, suggesting that the richer languages available to constraint modelling can be of benefit when considering planning problems with complex changes of state. CP and SAT solvers solved almost all of the largest and most challenging instances within 1 hour, whereas the best planning approach solved approximately 30%. Finally, the flexibility provided by the constraint models allows us to easily curate interesting levels for human players.

Evaluating communication capability across diverse groups for disaster medicine: A time-tracking method for functional exercises

Recent experiences in disaster medicine in Japan have shown the importance of preventing post-disaster health deterioration among citizens during the evacuation and recovery phases. The effective involvement of various medical, social welfare, and nursing organizations is necessary to mitigate these health problems. This study explored the effectiveness of a time-tracking evaluation method for functional exercises in analyzing the communication and joint task-processing capabilities of various medical and social welfare organizations in response to a citywide heavy-rain disaster. Each exercise inject was analyzed in advance to create a task-processing chain according to the emergency medical response plan. The actions of the exercise players were then compared with the expected tasks in the processing chain. The exercise created overload for some player groups. The completion rates and average execution times of the tasks were compared across task types and player groups, indicating the potential weaknesses of the current emergency operation plan. Responses of local medical associations to similar exercise injects were compared to analyze the range of diversity in responses and the merits of past exercise participation. Although the results of this time-tracking evaluation need to be carefully interpreted by combining the results regarding the quality of players' exercise actions, this method provides a new perspective for evaluating emergency operation plans through functional exercises.