The tactics of successful attacks in professional association football: large-scale spatiotemporal analysis of dynamic subgroups using position tracking data

Artificial intelligence applications in the football codes: A systematic review

Artificial Intelligence (AI) is increasingly being adopted across many domains such as transport, healthcare, defence and sport, with football codes no exception. Though there is a range of potential benefits of AI, concern has also been expressed regarding potential risks. An important first step in ensuring that AI applications in football are usable, beneficial, safe and ethical is to understand the current range of applications, the AI models adopted and their proposed functions. This systematic review aimed to identify different applications of AI across football codes to synthesise current knowledge and determine whether potential risks are being considered. The systematic review included 190 peer-reviewed articles. Nine areas of application were found ranging from athlete evaluation and event detection to match outcome prediction and injury detection and prediction. In total, 27 different AI models were identified, with artificial neural networks the most frequently applied. Five AI assessment metrics were identified including specificity, recall, precision, accuracy and F1-score. Four potential risks were identified, concerning data security, usability, data biases and inappropriate athlete load management. It is concluded that, though a wide range of AI applications currently exist, further work is required to develop AI for football and identify and manage potential risks.

A competing risk survival analysis of the impacts of team formation on goals in professional football

Introduction

This study investigated the influence of team formation on goal-scoring efficiency through analysing the time required for a goal to be scored in elite football matches.

Method

The analysis was conducted using a comprehensive open access dataset encompassing eight major football competitions, including prestigious events such as the World Cup and the UEFA Champions League. It notably focused on the competing risks framework and employed the Fine and Gray model to account for the interplay between two competing events: team A scoring and team B scoring.

Results

Through analysis of Team A's goal occurrences, we assessed the offensive capabilities of its formation and the defensive effectiveness of Team B's composition in relation to the time it took for Team A to score a goal. Findings revealed that teams employing the 4-3-3 and 4-2-3-1 formations outperformed other formations (3-4-3, 3-5-2, 4-4-2, 4-5-1, 5-3-2, 5-4-1) regarding goal-scoring efficiency.

Discussion

By shedding light on the impact of team formation on goal scoring, this research contributes to a deeper understanding of some of the successful strategic aspects of elite football.

How and Why People Synchronize: An Integrated Perspective

Academic AbstractInterpersonal synchrony, the alignment of behavior and/or physiology during interactions, is a pervasive phenomenon observed in diverse social contexts. Here we synthesize across contexts and behaviors to classify the different forms and functions of synchrony. We provide a concise framework for classifying the manifold forms of synchrony along six dimensions: periodicity, discreteness, spatial similarity, directionality, leader-follower dynamics, and observability. We also distill the various proposed functions of interpersonal synchrony into four interconnected functions: reducing complexity and improving understanding, accomplishing joint tasks, strengthening social connection, and influencing partners' behavior. These functions derive from first principles, emerge from each other, and are accomplished by some forms of synchrony more than others. Effective synchrony flexibly adapts to social goals and more synchrony is not always better. Our synthesis offers a shared framework and language for the field, allowing for better cross-context and cross-behavior comparisons, generating new hypotheses, and highlighting future research directions.

The role of offensive processes and age development for female soccer players' anticipation

Anticipation has been confirmed as a more valid measure for recognizing talented athletes than pattern recall alone. Anticipation of offensive processes in soccer, such as counter attacks and positional attacks, is essential for the sport. Additionally, the anticipation of elements such as the soccer ball, offensive and defensive players may also be affected by varied offensive processes. In this study, we combined anticipation with the pattern recall paradigm to measure the perceptual-cognitive skills of female soccer players across different age groups and offensive processes. Adult (U23) and adolescent (U15) female soccer players were recruited to complete the pattern anticipation task using coach-rated video segments. Our results show that adult female soccer players demonstrated greater accuracy in anticipating locations during positional attacks compared to adolescents, but no significant difference was observed during counter attacks. Furthermore, location anticipation accuracy is higher in all groups towards elements of the soccer ball and offensive players, but not defensive players, during counter attacks compared to positional attacks. These findings suggest that positional attack is the main advantage in perceptual-cognitive skills for adult female soccer players. Additionally, offensive processes and elements should be carefully considered when measuring perceptual-cognitive skills.

The Success Factors of Rest Defense in Soccer - A Mixed-Methods Approach of Expert Interviews, Tracking Data, and Machine Learning

While the tactical behavior of soccer players differs between specific phases of play (offense, defense, offensive transition, defensive transition), little is known about successful behavior of players during defensive transition (switching behavior from offense to defense). Therefore, this study aims to analyze the group tactic of rest defense (despite in ball possession, certain players safeguard quick counterattacks in case of ball loss) in defensive transition. A mixed-methods approach was used, involving both qualitative and quantitative analysis. Semi-structured expert interviews with seven professional soccer coaches were conducted to define rest defense. In the quantitative analysis, several KPIs were calculated, based on tracking and event data of 153 games of the 2020/21 German Bundesliga season, to predict the success of rest defense situations in a machine learning approach. The qualitative interviews indicated that rest defense can be defined as the positioning of the deepest defenders during ball possession to prevent an opposing counterattack after a ball loss. For instance, the rest defending players created a numerical superiority of 1.69 ± 1.00 and allowed a space control of the attacking team of 11.51 ± 9.82 [%] in the area of rest defense. The final machine learning model showed satisfactory prediction performance of the success of rest defense (Accuracy: 0.97, Precision: 0.73, f1-Score: 0.64, AUC: 0.60). Analysis of the individual KPIs revealed insights into successful behavior of players in rest defense, including controlling deep spaces and dangerous counterattackers. The study concludes regaining possession as fast as possible after a ball loss is the most important success factor in defensive transition.

Strengths, weaknesses, opportunities, and threats associated with the application of artificial intelligence in connection with sport research, coaching, and optimization of athletic performance: a brief SWOT analysis

Here, we performed a non-systematic analysis of the strength, weaknesses, opportunities, and threats (SWOT) associated with the application of artificial intelligence to sports research, coaching and optimization of athletic performance. The strength of AI with regards to applied sports research, coaching and athletic performance involve the automation of time-consuming tasks, processing and analysis of large amounts of data, and recognition of complex patterns and relationships. However, it is also essential to be aware of the weaknesses associated with the integration of AI into this field. For instance, it is imperative that the data employed to train the AI system be both diverse and complete, in addition to as unbiased as possible with respect to factors such as the gender, level of performance, and experience of an athlete. Other challenges include e.g., limited adaptability to novel situations and the cost and other resources required. Opportunities include the possibility to monitor athletes both long-term and in real-time, the potential discovery of novel indicators of performance, and prediction of risk for future injury. Leveraging these opportunities can transform athletic development and the practice of sports science in general. Threats include over-dependence on technology, less involvement of human expertise, risks with respect to data privacy, breaching of the integrity and manipulation of data, and resistance to adopting such new technology. Understanding and addressing these SWOT factors is essential for maximizing the benefits of AI while mitigating its risks, thereby paving the way for its successful integration into sport science research, coaching, and optimization of athletic performance.

Using machine learning pipeline to predict entry into the attack zone in football

Sports sciences are increasingly data-intensive nowadays since computational tools can extract information from large amounts of data and derive insights from athlete performances during the competition. This paper addresses a performance prediction problem in soccer, a popular collective sport modality played by two teams competing against each other in the same field. In a soccer game, teams score points by placing the ball into the opponent's goal and the winner is the team with the highest count of goals. Retaining possession of the ball is one key to success, but it is not enough since a team needs to score to achieve victory, which requires an offensive toward the opponent's goal. The focus of this work is to determine if analyzing the first five seconds after the control of the ball is taken by one of the teams provides enough information to determine whether the ball will reach the final quarter of the soccer field, therefore creating a goal-scoring chance. By doing so, we can further investigate which conditions increase strategic leverage. Our approach comprises modeling players' interactions as graph structures and extracting metrics from these structures. These metrics, when combined, form time series that we encode in two-dimensional representations of visual rhythms, allowing feature extraction through deep convolutional networks, coupled with a classifier to predict the outcome (whether the final quarter of the field is reached). The results indicate that offensive play near the adversary penalty area can be predicted by looking at the first five seconds. Finally, the explainability of our models reveals the main metrics along with its contributions for the final inference result, which corroborates other studies found in the literature for soccer match analysis.

Peripheral muscle function during repeated changes of direction in professional soccer players

PURPOSES

To evaluate peripheral muscle function of the knee extensors during repeated changes of direction in professional soccer players by examining differences between competitive levels, periods of the season and playing positions, and to investigate the relationships between peripheral muscle function and physical activities during matches.

METHODS

Knee extensor peripheral muscle function (twitch peak torque, PT) of 593 male soccer players from 13 European professional clubs competing at 3 different levels was evaluated 4 times during the season. The main outcomes were PT_max (maximal PT, muscle contractility), MP_max (maximal metabolic power exercise intensity) and PT_dec (PT decline, muscle fatigability) obtained during intermittent runs of increasing intensity with multiple changes of direction interspersed with electrically evoked contractions. Relative total and sprint distances covered during a whole match and during short intervals were quantified from a sub-sample.

RESULTS

PT_max and MP_max were higher for first than for second division (p < 0.047; d = 0.15-0.23) and Under-19 players (p < 0.007; d = 0.17-0.25). MP_max was lower (p < 0.016; d = 0.23-0.32) and PT_dec was higher (p < 0.004; d = 0.26-0.39) in the pre-season compared to all the other time points. MP_max was higher for fullbacks than attackers and defenders (p < 0.041; d = 0.20-0.22). PT_dec was higher for defenders than fullbacks, midfielders and wings (p < 0.029; d = 0.21-0.28). PT_max was associated with whole-match relative total distance (p = 0.004; d = 0.26). PT_dec was associated with whole-match relative total distance and relative short-interval sprint distance (p < 0.050; d = 0.18-0.22).

CONCLUSION

The ability to sustain repeated change of direction efforts at high intensities while preserving peripheral muscle function should be considered an important determinant of soccer physical performance.

The "Hockey" Assist Makes the Difference-Validation of a Defensive Disruptiveness Model to Evaluate Passing Sequences in Elite Soccer

With the growing availability of position data in sports, spatiotemporal analysis in soccer is a topic of rising interest. The aim of this study is to validate a performance indicator, namely D-Def, measuring passing effectiveness. D-Def calculates the change of the teams' centroid, centroids of formation lines (e.g., defensive line), teams' surface area, and teams' spread in the following three seconds after a pass and therefore results in a measure of disruption of the opponents' defense following a pass. While this measure was introduced earlier, in this study we aim to prove the usefulness to evaluate attacking sequences. In this study, 258 games of Dutch Eredivisie season 2018/19 were included, resulting in 13,094 attacks. D-Def, pass length, pass velocity, and pass angle of the last four passes of each attack were calculated and compared between successful and unsuccessful attacks. D-Def showed higher values for passes of successful compared to unsuccessful attacks (0.001 < p ≤ 0.029, 0.06 ≤ d ≤ 0.23). This difference showed the highest effects sizes in the penultimate pass (d = 0.23) and the maximal D-Def value of an attack (d = 0.23). Passing length (0.001 < p ≤ 0.236, 0.08 ≤ d ≤ 0.17) and passing velocity (0.001 < p ≤ 0.690, -0.09 ≤ d ≤ 0.12) showed inconsistent results in discriminating between successful and unsuccessful attacks. The results indicate that D-Def is a useful indicator for the measurement of pass effectiveness in attacking sequences, highlighting that successful attacks are connected to disruptive passing. Within successful attacks, at least one high disruptive action (pass with D-Def > 28) needs to be present. In addition, the penultimate pass ("hockey assist") of an attack seems crucial in characterizing successful attacks.