Ball Trajectory Inference from Multi-Agent Sports Contexts Using Set Transformer and Hierarchical Bi-LSTM

As artificial intelligence spreads out to numerous fields, the application of AI to sports analytics is also in the spotlight. However, one of the major challenges is the difficulty of automated acquisition of continuous movement data during sports matches. In particular, it is a conundrum to reliably track a tiny ball on a wide soccer pitch with obstacles such as occlusion and imitations. Tackling the problem, this paper proposes an inference framework of ball trajectory from player trajectories as a cost-efficient alternative to ball tracking. We combine Set Transformers to get permutation-invariant and equivariant representations of the multi-agent contexts with a hierarchical architecture that intermediately predicts the player ball possession to support the final trajectory inference. Also, we introduce the reality loss term and postprocessing to secure the estimated trajectories to be physically realistic. The experimental results show that our model provides natural and accurate trajectories as well as admissible player ball possession at the same time. Lastly, we suggest several practical applications of our framework including missing trajectory imputation, semi-automated pass annotation, automated zoom-in for match broadcasting, and calculating possession-wise running performance metrics

SoccER: Computer graphics meets sports analytics for soccer event recognition

Automatic event detection from images or wearable sensors is a fundamental step towards the development of advanced sport analytics and broadcasting software. However, the collection and annotation of large scale sport datasets is hindered by technical obstacles, cost of data acquisition and annotation, and commercial interests. In this paper, we present the Soccer Event Recognition (SoccER) data generator, which builds upon an existing, high quality open source game engine to enable synthetic data generation. The software generates detailed spatio-temporal data from simulated soccer games, along with fine-grained, automatically generated event ground truth. The SoccER software suite includes also a complete event detection system entirely developed and tested on a synthetic dataset including 500 minutes of game, and more than 1 million events. We close the paper by discussing avenues for future research in sports event recognition enabled by the use of synthetic data

Creating a model for expected Goals in football using qualitative player information

The field of sports analytics has been growing a lot in recent years. Sports like baseball and basketball were among the first to embrace it, but football has also taken big steps in that direction. One of the causes is that data analysis allows for the development of new advanced metrics which can provide a competitive advantage. This project presents a new version of one of these advanced metrics applied to football, the Expected Goals. The metric estimates how likely it is for a shot to end up becoming a goal. We present two different approaches for building the predictors: one that uses player qualitative information and another player agnostic. We then reflect on the importance of the calibration of the probabilities yielded by the models, as well as their possible interpretations, and present some of the applications that can be used to evaluate team and player performance. We also show the impact each feature has on the models to make their outputs interpretable and to demonstrate that the addition of the player qualitative information is important for the performance of the model

The assessment of perceptual-cognitive and decision-making abilities for the prediction of talent in Australian rules football

Talent identification (TID) is a vital component within the recruitment process for all sporting bodies and organisations. Given the considerable influence it may have on the success of a team, substantial resources are invested in identifying young athletes with the most potential for the development of expertise. Successful performance in team sports requires an athlete to have a unique combination of physical, technical and tactical skills. Such a combination allows athletes to compensate for different areas of weaknesses in the dynamic nature of game play. However, traditional TID does not allow athletes to showcase this multi-factorial element, but instead utilises mono-dimensional approaches, such as testing only physical fitness. Thus, forecasting longitudinal performance based upon one element of effective play (e.g. physical), fails to provide sufficient information for selectors to make informed decisions and leads to biased identification. In addition, TID uses a subjective assessment for the tactical decision-making performance, whereby recruiters watch game footage to determine a player’s decision-making ability based on their own perspectives and experiences. This type of assessment is problematic as it leaves assessments open to conscious or below conscious biases, due to conflicting opinions of what constitutes good play. The purpose of the current Doctoral study was to address the current limitations in talent identification practices and explore accessible additions to the current battery of tests, with an emphasis on decision-making. This thesis examines the tactical decision-making skill requirements within Australian Rules (AR) football to identify underlying mechanisms of elite decision-making. To achieve this, we measured eye-movement behaviour and related verbal explanations for decisions. The research presented in this thesis is divided into three studies. The first study (Chapter 2) explores perceptual-cognitive and decision-making skills in elite senior AR football players. This is followed by a longitudinal study (Chapter 3) which examines perceptual-cognitive and decision-making skill for elite junior AR football players across an eighteen-month time period. These studies form the foundation for the proposed testing items in study three which is a proof of concept, outlining a protocol design that quantifies perceptual-cognitive and decision-making skill in a manner not used in current AR football TID testing programs. The research findings contribute an important body of research to the study of TID by providing a conceptually translatable means through which the development of an objective protocol design approach can be undertaken in the future, thus ensuring that objective measurements of all determinants of game play are assessed and in turn creating a more comprehensive TID procedure

A computer vision based web application for tracking soccer players

Soccer is a sport where everyone that is involved with it make all the efforts aiming for excellence. Not only the players need to show their skills on the pitch but also the coach, and the remaining staff, need to have their own tools so that they can perform at higher levels. Footdata is a project to build a new web application product for soccer (football), which integrates two fundamental components of this sport's world: the social and the professional. While the former is an enhanced social platform for soccer professionals and fans, the later can be considered as a Soccer Resource Planning, featuring a system for acquisition and processing information to meet all the soccer management needs. In this paper we focus only in a specific module of the professional component. We will describe the section of the web application that allows to analyse movements and tactics of the players using images directly taken from the pitch or from videos, we will show that it is possible to draw players and ball movements in a web application and detect if those movements occur during a game. © 2014 Springer International Publishing

Developing game awareness, perception and decision-making in elite youth footballers

In football, elite players appear to have more time and space, understand the pattern of the game, make better decisions with the ball and be one step ahead of their opponents. Some players are anecdotally believed to ‘just have it’. This study examined the process of perception-decision-execution during skill acquisition within football and whether training focussed on cognition and perception leads to players’ decision-making being improved. There appears to be a gap in both the research and application in the sporting context as to the role and trainability of decision-making in football and whether greater perception of the in-game environment contributes to better decision-making. The aim of the study was to examine the effects of multi-task and cognitive effort training during football practice and to determine the impact of these methods on perception and decision-making regarding a player’s first touch in the match environment. The study involved an experimental design using a control trial during the intervention. Players from two teams (N=31, age M=14.18, SD=0.55), competing in the NSW National Youth Premier League (NYPL) were divided into control and intervention groups and completed testing at three time points (pre-, post-intervention and retention). A training intervention was conducted replacing the 20 minute traditional passing practice component of the training. The intervention consisted of cognitive load exercises based on first touch ball manipulation and movements commonly found in football. The effectiveness of the training intervention was assessed via three methods; a video-based decision-making test using 20 video clips with players depicting their first touch, a questionnaire self-assessing decision-making and expert analysis of individuals’ game performance from footage of games. Results indicate that the altered training environment was equally effective to traditional passing practices in all three measures used in the study. There was a significant difference in the video-based testing (p < .01), for both the control and intervention groups between pre-test (M=7.196) and post-test (M=10.714) and between pre-test (M=7.196) and retention test (M=10.750) supporting previous studies that on field training positively influences decision-making in video-based tests. The questionnaire revealed players self-assessed their decision-making ability at a constant level across both the control and intervention groups. Game performance in the match environment indicated players made less poor decisions leading to losing possession, but did not improve decision-making to create more scoring chances. The impact of the altered training environment on players across the three measures are discussed along with the implications of the results for the development of decision-making in youth football. Recommendations are made for the scope and focus of future research into training and testing decision-making through cognitive load training

FootApp: An AI-powered system for football match annotation

In the last years, scientific and industrial research has experienced a growing interest in acquiring large annotated data sets to train artificial intelligence algorithms for tackling problems in different domains. In this context, we have observed that even the market for football data has substantially grown. The analysis of football matches relies on the annotation of both individual players’ and team actions, as well as the athletic performance of players. Consequently, annotating football events at a fine-grained level is a very expensive and error-prone task. Most existing semi-automatic tools for football match annotation rely on cameras and computer vision. However, those tools fall short in capturing team dynamics and in extracting data of players who are not visible in the camera frame. To address these issues, in this manuscript we present FootApp, an AI-based system for football match annotation. First, our system relies on an advanced and mixed user interface that exploits both vocal and touch interaction. Second, the motor performance of players is captured and processed by applying machine learning algorithms to data collected from inertial sensors worn by players. Artificial intelligence techniques are then used to check the consistency of generated labels, including those regarding the physical activity of players, to automatically recognize annotation errors. Notably, we implemented a full prototype of the proposed system, performing experiments to show its effectiveness in a real-world adoption scenario