Space evaluation in football games via field weighting based on tracking data

The dual problem of space: Relative player positioning determines attacking success in elite men's football

The concept of space has been successfully modelled in football using spatiotemporal player data and Voronoi diagrams. Current approaches, however, are narrow in scope by focusing on an inter-team allocation of space to measure space control. The present work extends this widespread perspective with an intra-team application of the Voronoi diagram and its dual Delaunay triangulation to measure space management. Both models are leveraged to derive novel performance metrics, which assess how teams use triangular positioning and how players tie up defenders during attacks. The outcome of N = 128,187 attacking sequences from 306 elite men's football matches is analysed using linear mixed-effects models to validate the proposed performance metrics. Results show that attacking success is characterized by player positioning which promotes forming of large triangles especially in ball proximity, whereas the overall number of triangles is of no relevance. Furthermore, players tie up more defenders and thus create free teammates more often during successful attacks. The results demonstrate that a new perspective on space is helpful to better quantify and understand the effect of space management and player positioning on attacking performance in football.

A fully automatic method for segmentation of soccer playing fields

This paper proposes a strategy to segment the playing field in soccer images, suitable for integration in many soccer image analysis applications. The combination of a green chromaticity-based analysis and an analysis of the chromatic distortion using full-color information, both at the pixel-level, allows segmenting the green areas of the images. Then, a fully automatic post-processing block at the region-level discards the green areas that do not belong to the playing field. The strategy has been evaluated with hundreds of annotated images from matches in several stadiums with different grass shades and light conditions. The results obtained have been of great quality in all the images, even in those with the most complex lighting conditions (e.g., high contrast between sunlit and shadowed areas). In addition, these results have improved those obtained with leading state-of-the-art playing field segmentation strategies.

Validation of a motion model for soccer players' sprint by means of tracking data

In soccer game analysis, the widespread availability of play-by-play and tracking data has made it possible to test mathematical models that have been discussed mainly theoretically. One of the essential models in soccer game analysis is a motion model that predicts the arrival point of a player in t s. Although many space evaluation and pass prediction methods rely on motion models, the validity of each has not been fully clarified. This study focuses on the motion model proposed by Fujimura and Sugihara (Fujimura-Sugihara model) under sprint conditions based on the equation of motion. A previous study indicated that the Fujimura-Sugihara model is ineffective for soccer games because it generates a circular arrival region. This study aims to examine the validity of the Fujimura-Sugihara model using soccer tracking data. Specifically, we quantitatively compare the arrival regions of players between the model and real data. We show that the boundary of the player's arrival region is circular rather than elliptical, which is consistent with the model. We also show that the initial speed dependence of the arrival region satisfies the solution of the model. Furthermore, we propose a method for estimating valid kinetic parameters in the model directly from tracking data and discuss the limitations of the model for soccer games based on the estimated parameters.

Open Dataset Recorded by Single Cameras for Multi-Player Tracking in Soccer Scenarios

Multi-player action recognition for automatic analysis in sports is the subject of increasing attention. Trajectory-tracking technology is key for accurate recognition, but little research has focused on this aspect, especially for non-professional matches. Here, we study multi-player tracking in the most popular and complex sport among non-professionals—soccer. In this non-professional soccer player tracking (NPSPT) challenge, single-view-based motion recording systems for continuous data collection were installed in several soccer fields, and a new benchmark dataset was collected. The dataset consists of 17 2-min long super-high-resolution videos with diverse game types consistently labeled across time, covering almost all possible situations for multi-player detection and tracking in real games. A comprehensive evaluation was conducted on the state-of-the-art multi-object-Tracking (MOT) systems, revealing insights into player tracking in real games. Our challenge introduces a new dimension for researchers in the player recognition field and will be beneficial to further studies.

Application of Machine Learning and Information Coverage Centralized Genetic Method in Safety Management of Football Training

In recent years, China's competitive sports have developed rapidly. Among them, football is a sport with high energy consumption, high intensity, strong antagonism, and high speed. As a result, injuries are common in football practice. It is impossible to do adequate football training in order to avoid such incidents. At the moment, football injury incidents are common, severely limiting the full growth of the sport in China. This work investigates the safety management of football training using machine learning and an information coverage-centralized genetic technique. To begin, this article describes in detail the machine learning and information coverage-centralized genetic algorithm, summarizes the classification of machine learning models, and introduces the verification and evaluation process of machine learning models and trusted information coverage models as an important theoretical basis for football training safety management. Then, the genetic algorithm based on information coverage concentration is used in football training to analyze the safety risk of football training and the analysis of training speed type. The results show that the human factor accounts for the highest proportion in football training safety accidents, accounting for 28.75%. In the analysis of football training speed, the average passing time of medium strength accounts for the highest proportion of 39.75%. In football training, in order to ensure the safety of training, the combination of medium strength and high strength can be adopted to avoid training injury.

Football player dominant region determined by a novel model based on instantaneous kinematics variables

Dominant regions are defined as regions of the pitch where a player can reach before any other and are commonly determined without considering the free-spaces in the pitch. We presented an approach to football players’ dominant regions analysis, based on movement models created from players’ positions, displacement, velocity, and acceleration vectors. 109 Brazilian male professional football players were analysed during official matches, computing over 15 million positional data obtained by video-based tracking system. Movement models were created based on players’ instantaneous vectorial kinematics variables, then probabilities models and dominant regions were determined. Accuracy in determining dominant regions by the proposed model was tested for different time-lag windows. We calculated the areas of dominant, free-spaces, and Voronoi regions. Mean correct predictions of dominant region were 96.56%, 88.64%, and 72.31% for one, two, and three seconds, respectively. Dominant regions areas were lower than the ones computed by Voronoi, with median values of 73 and 171 m², respectively. A median value of 5537 m² was presented for free-space regions, representing a large part of the pitch. The proposed movement model proved to be more realistic, representing the match dynamics and can be a useful method to evaluate the players’ tactical behaviours during matches.