Individual ball possession in soccer

DEXA Body Composition Asymmetry Analysis and Association to Injury Risk and Low Back Pain in University Soccer Players

Soccer is a laterally dominant sport owing to the repetitive nature of unilateral kicking. The relationship between functional and body composition asymmetries related to limb dominance in soccer players has yet to be established. When present, asymmetries can increase the risk of injury and low back pain. Our study investigated whether lateral dominance is associated with limb asymmetries in a comprehensive body composition assessment among varsity soccer players. Twenty-seven varsity soccer players (age 20.4 ± 1.7 years old; BMI 22.6 ± 4.6 kg/m2) participated in this study. Body composition was assessed through dual-energy X-ray absorptiometry scans. Results showed low lower limb asymmetry indices in both males (3.82%) and females (3.36%) compared to normal ranges. However, upper limb lean mass exhibited high asymmetry, surpassing thresholds in males (7.3%) and females (4.39%). Significant differences were found in total bone mass among males and total lean body mass among females. Male players exhibited higher asymmetry indices in both arm and trunk mass compared to females. Despite these asymmetries, no significant correlations were found between asymmetry indices and occurrences of lower limb injury or low back pain. The study suggests that while evaluating body composition for injury prevention in soccer shows potential, lateral dominance may be influenced by factors extending beyond sport-specific adaptations.

Synchronization of passes in event and spatiotemporal soccer data

The majority of soccer analysis studies investigates specific scenarios through the implementation of computational techniques, which involve the examination of either spatiotemporal position data (movement of players and the ball on the pitch) or event data (relating to significant situations during a match). Yet, only a few applications perform a joint analysis of both data sources despite the various involved advantages emerging from such an approach. One possible reason for this is a non-systematic error in the event data, causing a temporal misalignment of the two data sources. To address this problem, we propose a solution that combines the SwiftEvent online algorithm (Gensler and Sick in Pattern Anal Appl 21:543-562, 2018) with a subsequent refinement step that corrects pass timestamps by exploiting the statistical properties of passes in the position data. We evaluate our proposed algorithm on ground-truth pass labels of four top-flight soccer matches from the 2014/15 season. Results show that the percentage of passes within half a second to ground truth increases from 14 to 70%, while our algorithm also detects localization errors (noise) in the position data. A comparison with other models shows that our algorithm is superior to baseline models and comparable to a deep learning pass detection method (while requiring significantly less data). Hence, our proposed lightweight framework offers a viable solution that enables groups facing limited access to (recent) data sources to effectively synchronize passes in the event and position data.

The relationship between deliberate practice, play, and futsal in childhood and adolescence and the development of different decision-making skills in professional female soccer players

Soccer players' ability to make efficient and quick decisions has gained more importance due to the increase in game speed in the last few years. Therefore, this study aimed to examine the relationship between the engagement in previous developmental activities in soccer and futsal with the quality and speed of decision-making skills in different phases of sport development of elite female soccer players. The sample comprised 77 elite Brazilian professional female soccer players. Players' decision-making skills were assessed based on an objective video-based test - TacticUP®. We used a retrospective questionnaire to collect information about previous participation in different developmental activities. The results showed that engagement in deliberate practice in soccer and futsal, especially during childhood and early adolescence, is related to a better quality of offensive decision-making skills, although showing small to medium effect sizes. We highlight that deliberate practice in futsal is associated only with offensive decision-making skills with the ball and near the ball. In turn, engaging in deliberate play in soccer, mainly in childhood and early adolescence, is related to quicker offensive and defensive decision-making skills, showing mostly medium effect sizes. To the best of our knowledge, it was the first study to measure the relationship of decision-making speed with developmental activities in soccer. It is concluded that deliberate practice in soccer and deliberate play in soccer are associated with different dimensions of decision-making skills.

How Football Players' Age Affect Passing Patterns of Play According to Field Location

This study aimed to characterize the passing patterns that support collective tactical behaviour in football players of different ages (U15, U17, and U19) in different field zones. Two hundred and twenty-eight male players, divided into U15, U17, and U19, participated in the study. Cluster analysis was used to group the passes into three sizes (short, medium, and long). The chi-square test was used to analyse the effect of player age on game-passing patterns in each field zone. The results revealed that long and medium passes were used more in areas close to the goals and short passes in the middle area of the field, concerning all ages (p < 0.001). Furthermore, the analysis of the relative distance between the ball carrier and the receiver indicated that older players (U17 and U19) used more distant players to pass the ball in medium and long passes. These results can help coaches design small-sided games according to the players’ ages and adjust to the field’s space and the numerical relationship, thus creating a greater transfer from training to competition.

Machine learning application in soccer: a systematic review

Due to the chaotic nature of soccer, the predictive statistical models have become in a current challenge to decision-making based on scientific evidence. The aim of the present study was to systematically identify original studies that applied machine learning (ML) to soccer data, highlighting current possibilities in ML and future applications. A systematic review of PubMed, SPORTDiscus, and FECYT (Web of Sciences, CCC, DIIDW, KJD, MEDLINE, RSCI, and SCIELO) was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. From the 145 studies initially identified, 32 were fully reviewed, and their outcome measures were extracted and analyzed. In summary, all articles were clustered into three groups: injury (n = 7); performance (n = 21), which was classified in match/league outcomes forecasting, physical/physiological forecasting, and technical/tactical forecasting; and the last group was about talent forecasting (n = 5). The development of technology, and subsequently the large amount of data available, has become ML in an important strategy to help team staff members in decision-making predicting dose-response relationship reducing the chaotic nature of this team sport. However, since ML models depend upon the amount of dataset, further studies should analyze the amount of data input needed make to a relevant predictive attempt which makes accurate predicting available.

A review of machine learning applications in soccer with an emphasis on injury risk

This narrative review paper aimed to discuss the literature on machine learning applications in soccer with an emphasis on injury risk assessment. A secondary aim was to provide practical tips for the health and performance staff in soccer clubs on how machine learning can provide a competitive advantage. Performance analysis is the area with the majority of research so far. Other domains of soccer science and medicine with machine learning use are injury risk assessment, players' workload and wellness monitoring, movement analysis, players' career trajectory, club performance, and match attendance. Regarding injuries, which is a hot topic, machine learning does not seem to have a high predictive ability at the moment (models specificity ranged from 74.2%-97.7%. sensitivity from 15.2%-55.6% with area under the curve of 0.66-0.83). It seems, though, that machine learning can help to identify the early signs of elevated risk for a musculoskeletal injury. Future research should account for musculoskeletal injuries' dynamic nature for machine learning to provide more meaningful results for practitioners in soccer.

Effects of Age and Playing Tactics on the Individual Tactical Behavior in U10 and U12 Elite Spanish Soccer Players

The aim of this paper was to explore the effects of age and playing tactics on the individual tactical behavior and performance in young soccer players. A total of 1247 individual possessions during 16 knockout matches from LaLiga Promises U12 tournament (n = 621) and LaLiga U10 Iscar Cup (n = 626) were analyzed by observational methodology. Multilevel and multivariate logistic regression models were created to explain the interdependent effects of age category and playing tactics on the individual tactical behavior and performance. Youth players performed most of their actions against defensive pressure (72.5%), during offensive support (91.3%) and receiving the ball facing forward (62.6%). The most frequent action was to receive and pass the ball (69.6%) and the level of offensive success was 56.9%. The multilevel mixed models revealed that U10 players presented higher odds of running with the ball vs. passing the ball (OR = 1.823; 95% CI: 1.333−2.493; p < 0.001) and lower odds of achieving offensive success (OR = 0.698; 95% CI: 0.525−0.928; p < 0.05) than U12 players. Regarding playing tactics, technical-tactical dimensions such as the players’ body shape when receiving the ball, offensive support, defensive pressure, collective type of attack and type of technical action presented a significant and combined effect on the offensive success regardless of age category, playing position and match status. This study found tactical differences between U10 and U12 age categories and revealed important interactive effects of multiple tactical dimensions on the individual offensive behavior and performance in youth soccer players.

Predicting the in-game status in soccer with machine learning using spatiotemporal player tracking data

An important structuring feature of a soccer match is the in-game status, whether a match is interrupted or in play. This is necessary to calculate performance indicators relative to the effective playing time or to find standard situations, ball actions, and other tactical structures in spatiotemporal data. Our study explores the extent to which the in-game status can be determined using time-continuous player positions. Therefore, to determine the in-game status we tested four established machine learning methods: logistic regression, decision trees, random forests, and AdaBoost. The models were trained and evaluated using spatiotemporal data and manually annotated in-game status of 102 matches in the German Bundesliga. Results show up to 92% accuracy in predicting the in-game status in previously unknown matches on frame level. The best performing method, AdaBoost, shows 81% precision for detecting stoppages (longer than 2 s). The absolute time shift error at the start was ≤ 2 s for 77% and 81% at the end for all correctly predicted stoppages. The mean error of the in-game total distance covered per player per match using the AdaBoost in-game status prediction was − 102 ± 273 m, which is 1.3% of the mean value of this performance indicator (7939 m). Conclusively, the prediction quality of our model is high enough to provide merit for performance diagnostics when teams have access to player positions (e.g., from GPS/LPM systems) but no human-annotated in-game status and/or ball position data, such as in amateur or youth soccer.

Design and Reliability of an Observational Framework to Evaluate the Individual Offensive Behavior in Youth Soccer—The INDISOC Tool

Despite the great development of match analysis in professional soccer during the last decade, very few studies have assessed the individual technical and tactical behaviors of youth soccer players. The purpose of this paper was to design and assess the reliability of an observational instrument to evaluate the INDIvidual offensive behavior in competitive 7 and 11-a-side SOCcer (INDISOC). A total of eight experts in soccer training and analysis were included in the design of the tool by means of meetings and exploratory observations. This process involved design and re-design steps of the INDISOC tool to its final format which includes twelve dimensions related to the spatial, technical, and tactical constraints of individual behavior in soccer. The unit of analysis was the individual ball possession (IBP), described as the time that starts when a player can perform an action with the ball, and which ends when the IBP for another player begins. In the INDISOC tool, the IBP is analyzed taking into account three temporal moments: (1) receiving the ball, (2) processing the ball, and (3) culminating the individual action. Inter-observer and intra-observer analyses were performed and the kappa (K) coefficients were calculated to test the instrument reliability. The K values showed optimal inter (7-a-side: 0.73–0.95; 11-a-side: 0.76–0.98) and intra-observer (7-a-side: 0.84–1;11-a-side: 0.79–1) reliability levels. These results support the notion that the INDISOC observational tool could be a suitable instrument for analyzing the individual offensive behavior in competitive youth (7-a-side), junior and senior (11-a-side) soccer.

A systematic review about the performance indicators related to ball possession

The purpose of this review was to investigate the influence of the performance indicators related to ball possession on the match outcome and team performance. Following the PRISMA 2020 (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines, this systematic review searched for literature on Web of Science, Scopus and Pub Med database, the publication date of the retrieved literature is set from January 2000 to December 2020. The keywords 'football' or 'soccer' were each paired with the following terms: 'ball possession', 'ball recovery', 'ball regain', 'transition', 'playing style', 'possession zone', 'duration' and 'running performance'. The search returned 2,436 articles. After screening the records against set criteria, 75 analysis were made, and their technical and physical indicators were identified. Through analysing ball-possession related variables, the review concluded that the match outcome is not related to ball possession percentage. The ball possession percentage is not dominant to predict the match success. The status of ball possession percentage can affect the team's performance in passing, organizational and running distance with the ball possession. There are league differences in ball possession strategies and duration. The frequency and offensive efficiency of direct ball recovery types are higher than indirect types. Ball possessions regained in the defensive third were higher than the final third. However, there remain some limitations such as the difference in the definition of concepts and sample participants, only a few studies consider the influence of situational variables and lack of in-depth analysis on ball possession strategy. Therefore, further study should adopt a more comprehensive approach, establishing a new connection between possession strategy and more technical and tactical indicators.

Examining the effect of reduced action capabilities on defensive anticipation in a 1-vs-1 task

This study used small-sided games (SSGs) to induce fatigue and therefore, reduce the action capabilities of Varsity soccer players (n= 20). The aim was to examine the effect of compromised action capabilities on defensive movement response in a 1-vs-1 scenario. Action capabilities were assessed via countermovement jumps (CMJ), 5-m acceleration, 20-m sprint and a Change of Direction (COD) test. Defensive movement response was measured via a Soccer-Specific Anticipation Test (SSAT). Following SSGs, significant reductions were observed in jump (p=.04, d=.31), acceleration (p<.001, d=.98), and sprint (p<.001, d=.66) performance. Significantly, players tended to move earlier in the SSAT following SSGs (p=.049, d=.66). Furthermore, to examine the distinct effect of reductions in each action capability, players were categorised according to whether SSGs had a worthwhile change in CMJ, acceleration, sprint or COD performance. For each of the four measures, movements tended to be initiated earlier following SSGs, although pre-/post-SSGs differences were not significant (p =.08-.51), moderate to large effect sizes were shown (d=.56-.84). These findings intimate that compromised action capabilities influence the timing of the movement response of defensive players in 1-vs-1 situations.

How the COVID-19 Pandemic has Changed the Game of Soccer

This study explores the influence of corona-specific training and playing conditions - especially empty stadiums - on match performance, contact behavior, and home advantage in the Bundesliga (BL) and Bundesliga 2 (BL2). We analyzed the 2017/18, 2018/19, and 2019/20 seasons and compared matches in rounds 26-34 before shutdown with "ghost" matches after restart. Results show increased running activity for high intensity distance: (+ 6.1%) and total distance covered (+ 4.3%). In BL2 in particular there were also changes in tactical aspects of the game (time in last third: -6.3%, pressure on pass receiver: -8.6%, success of attacking duels: -7.9%, share of long passes completed: + 15.6%, outplayed opponents per pass: -14.7%). Contact time to other players (< 2 m distance) was 15:35 mins per match. After restart, contact was reduced, especially when the ball was not in the last third (-11.2%). Away wins increased by +44.2% in BL and the home-away difference in yellow cards changed in favor of the away team (+31.2%) in BL2. We conclude that empty stadiums have reduced home advantage and decreased referee bias when awarding yellow cards. Player behavior might have been affected by tactical demands and/or conscious or unconscious self-protection.

The Response of Hip Joint Cartilage to Exercise in Children: An MRI Study Using T2-Mapping

OBJECTIVE

The aim of this study was to determine the effects of activity and cam morphology on cartilage composition during adolescence and investigate the development of cartilage composition with age.

DESIGN

Cross-sectional observational cohort study of individuals from football club academies and an age-matched control population, aged 9 to 18 years. Assessments included questionnaires and T2-mapping of hips. Primary imaging outcome measures were T2 relaxation time of acetabular and femoral cartilage, average alpha angle, and lateral epiphyseal extension.

RESULTS

The cohort consisted of 109 elite male footballers, 49 male controls, and 51 female controls. Elite male footballers had an acetabular cartilage T2 value 4.85 ms greater than male controls (P < 0.001). A significant positive correlation existed between Physical Activity Questionnaire Score and acetabular cartilage T2 value (coefficient 1.07, P < 0.001) and femoral cartilage T2 value (coefficient 0.66, P = 0.032). Individuals with a closed physis had an acetabular cartilage T2 value 7.86 ms less than individuals with an open physis. Acetabular cartilage T2 values decreased with age in elite footballers. No correlation existed between alpha angle and anterosuperior acetabular cartilage T2 value and no difference in T2 value existed between individuals with and without cam morphology.

CONCLUSIONS

This study demonstrates that high activity levels may significantly affect acetabular cartilage composition during adolescence, but cam morphology may not detrimentally affect cartilage composition until after adolescence.

The dominant leg is more likely to get injured in soccer players: systematic review and meta-analysis

In soccer (football), dominant limb kicking produces higher ball velocity and is used with greater frequency than the non-dominant limb. It is unclear whether limb dominance has an effect on injury incidence. The purpose of this systematic review with meta-analysis is to examine the relationship between limb dominance and soccer injuries. Studies were identified from four online databases according to PRISMA guidelines to identify studies of soccer players that reported lower extremity injuries by limb dominance. Relevant studies were assessed for inclusion and retained. Data from retained studies underwent meta-analyses to determine relative risk of dominant versus non-dominant limb injuries using random-effects models. Seventy-four studies were included, with 36 of them eligible for meta-analysis. For prospective lower extremity injury studies, soccer players demonstrated a 1.6 times greater risk of injury to the dominant limb (95% CI [1.3-1.8]). Grouped by injury location, hamstring (RR 1.3 [95% CI 1.1-1.4]) and hip/groin (RR 1.9 [95% CI 1.3-2.7]) injuries were more likely to occur to the dominant limb. Greater risk of injury was present in the dominant limb across playing levels (amateurs RR 2.6 [95% CI 2.1-3.2]; youths RR 1.5 [95% CI 1.26-1.67]; professionals RR 1.3 [95% CI 1.14-1.46]). Both males (RR 1.5 [95% CI 1.33-1.68)] and females (RR 1.5 [95% CI 1.14-1.89]) were more likely to sustain injuries to the dominant limb. Future studies investigating soccer injury should adjust for this confounding factor by using consistent methods for assigning limb dominance and tracking use of the dominant versus non-dominant limb.

Data-driven detection of counterpressing in professional football

Detecting counterpressing is an important task for any professional match-analyst in football (soccer), but is being done exclusively manually by observing video footage. The purpose of this paper is not only to automatically identify this strategy, but also to derive metrics that support coaches with the analysis of transition situations. Additionally, we want to infer objective influence factors for its success and assess the validity of peer-created rules of thumb established in by practitioners. Based on a combination of positional and event data we detect counterpressing situations as a supervised machine learning task. Together, with professional match-analysis experts we discussed and consolidated a consistent definition, extracted 134 features and manually labeled more than 20, 000 defensive transition situations from 97 professional football matches. The extreme gradient boosting model—with an area under the curve of $$87.4\%$$ 87.4 % on the labeled test data—enabled us to judge how quickly teams can win the ball back with counterpressing strategies, how many shots they create or allow immediately afterwards and to determine what the most important success drivers are. We applied this automatic detection on all matches from six full seasons of the German Bundesliga and quantified the defensive and offensive consequences when applying counterpressing for each team. Automating the task saves analysts a tremendous amount of time, standardizes the otherwise subjective task, and allows to identify trends within larger data-sets. We present an effective way of how the detection and the lessons learned from this investigation are integrated effectively into common match-analysis processes.

Identification of Explanatory Variables in Possession of the Ball in High-Performance Women's Football

Women's football is a phenomenon that is experiencing an unstoppable increase in recent years. The objective of this study was to analyze how ball possessions are performed in high-performance women's football. For this, the 52 matches played by all the national teams participating in the Women's World Cup 2015 were analyzed. A total of 3740 ball possessions were examined. Using the "move outcome" variable as a reference criterion, a statistically significant relationship was found between possessions that ended in success and possessions that have failed. Specifically, the successful possessions were those that were made in the offensive zone, with a clear intention to progress towards the rival goal, with a low number of passes, and made in the second half of the matches. The results of the logistic regression showed that the proposed model is statistically significant, with an acceptable explanatory capacity. Finally, the results of the decision tree evidence the success of those possessions aiming at a quick completion, with very few passes and the offensive zone as a priority area for the possession.

Match Analysis of Soccer Refereeing Using Spatiotemporal Data: A Case Study

This case study explored how spatiotemporal data can develop key metrics to evaluate and understand elite soccer referees’ performance during one elite soccer match. The dynamic position of players from both teams, the ball and three elite referees allowed to capture the following performance metrics: (i) assistant referees: alignment with the second last defender; (ii) referee: referee diagonal movement—a position density was computed and a principal component analysis was carried to identify the directions of greatest variability; and (iii) referee: assessing the distance from the referee to the ball. All computations were processed when the ball was in-play and separated by 1st and 2nd halves. The first metric showed an alignment lower than 1 m between the assistant referee and the second last defender. The second metric showed that in the 1st half, the referee position ellipsis area was 548 m², which increased during the 2nd half (671 m²). The third metric showed an increase in the distance from the referee to the ball and >80% of the distance between 5–30 m during the 2nd half. The findings may be used as a starting point to elaborate normative behavior models from the referee’s movement performance in soccer.

Can Tracking Data Help in Assessing Interpersonal Contact Exposure in Team Sports during the COVID-19 Pandemic?

During the COVID-19 pandemic, the temporary closure of physical activity and sports facilities, and the generalized cancellation or postponement of sports events have a massive impact on social and economic development. In this study, we explored the feasibility of using tracking data from a football match to assess interpersonal contact between individuals by calculating two measures of respiratory exposure. The dynamic tracking positioning of all players and referees during one international football match was analyzed. For each individual, two measures of respiratory exposure were calculated, based on the 2 m interpersonal distance recommendations for contact tracing for COVID-19 control. Overall, individuals spent a median of 0.12 mm:ss (IQR = 0.45 mm:ss) exposed to interpersonal contact of fewer than 2 m from others. The highest value of exposure was observed between two players of opposing teams (6.35 mm:ss). The results suggest that tracking data can be used to assess respiratory exposure to interpersonal contact in team sports, such as football. The measures of exposure calculated can be used to the prompt identification of high-risk contacts of COVID-19 cases during a match or a training session, but also the risk stratification of different sports and physical activities.

Passing Networks and Tactical Action in Football: A Systematic Review

The aim of this study is to examine the most significant literature on network analyses and factors associated with tactical action in football. A systematic review was conducted on Web of Science, taking into account the PRISMA guidelines using the keyword “network”, associated with “football” or “soccer”. The search yielded 162 articles, 24 of which met the inclusion criteria. Significant results: (a) 50% of the studies ratify the importance of network structures, quantifying and comparing properties to determine the applicability of the results instead of analyzing them separately; (b) 12.5% analyze the process of offensive sequences and communication between teammates by means of goals scored; (c) the studies mainly identify a balance in the processes of passing networks; (d) the variables allowed for the interpretation of analyses of grouping metrics, centralization, density and heterogeneity in connections between players of the same team. Finally, a systematic analysis provides a functional understanding of knowledge that will help improve the performance of players and choose the most appropriate response within the circumstances of the game.

Game-based versus multilateral approach: effects of a 12-week program on motor skill acquisition and physical fitness development in soccer school children

BACKGROUND

The purpose of this study was to investigate the effects of a 12-week-game-based training versus a traditional multilateral approach on motor skills acquisition and physical fitness, in a group of U9 children playing soccer.

METHODS

Forty children aged 9 or younger (U9) recruited from a local soccer school were assigned in a 1:1 ratio to a game-based training program (GB) or a multilateral training (MA) approach. The training programs lasted 12 weeks, and players were tested at baseline and at the end of the program (12-week follow-up). The outcomes were: standing long jump test, shuttle dribble test, 10×5 shuttle run test and Mini-Cooper test.

RESULTS

Within-group comparisons showed statistically-significant improvements in both of the groups: standing long lump (P<0.0001), shuttle dribble test (P<0.0001), shuttle run test (P<0.0001) and Mini-Cooper test (P<0.0001). Furthermore, the MA group showed better performance in the shuttle run test after 12 weeks of training compared to the GB group (P=0.0002; +8%).

CONCLUSIONS

A multilateral approach promotes physical development in U9 soccer players without affecting learning of-soccer skills. Therefore, a multilateral approach should be included in soccer training programs to ensure an optimal development in young soccer players.

Football-specific validity of TRACAB's optical video tracking systems

The present study aimed to validate and compare the football-specific measurement accuracy of two optical tracking systems engineered by TRACAB. The “Gen4” system consists of two multi-camera units (a stereo pair) in two locations either side of the halfway line, whereas the distributed “Gen5” system combines two stereo pairs on each side of the field as well as two monocular systems behind the goal areas. Data were collected from 20 male football players in two different exercises (a football sport-specific running course and small-sided games) in a professional football stadium. For evaluating the accuracy of the systems, measures were compared against simultaneously recorded measures of a reference system (VICON motion capture system). Statistical analysis uses RMSE for kinematic variables (position, speed and acceleration) and the difference in percentages for performance indicators (e.g. distance covered, peak speed) per run compared to the reference system. Frames in which players were obviously not tracked were excluded. Gen5 had marginally better accuracy (0.08 m RMSE) for position measurements than Gen4 (0.09 m RMSE) compared to the reference. Accuracy difference in instantaneous speed (Gen4: 0.09 m⋅s^-1 RMSE; Gen5: 0.08 m⋅s^-1 RMSE) and acceleration (Gen4: 0.26 m⋅s^-2 RMSE; Gen5: 0.21 m⋅s^-2 RMSE) measurements were significant, but also trivial in terms of the effect size. For total distance travelled, both Gen4 (0.42 ± 0.60%) and Gen5 (0.27 ± 0.35%) showed only trivial deviations compared to the reference. Gen4 showed moderate differences in the low-speed distance travelled category (-19.41 ± 13.24%) and small differences in the high-speed distance travelled category (8.94 ± 9.49%). Differences in peak speed, acceleration and deceleration were trivial (<0.5%) for both Gen4 and Gen5. These findings suggest that Gen5’s distributed camera architecture has minor benefits over Gen4’s single-view camera architecture in terms of accuracy. We assume that the main benefit of the Gen5 towards Gen4 lies in increased robustness of the tracking when it comes to optical overlapping of players. Since differences towards the reference system were very low, both TRACAB’s tracking systems can be considered as valid technologies for football-specific performance analyses in the settings tested as long as players are tracked correctly.

A public data set of spatio-temporal match events in soccer competitions

Soccer analytics is attracting increasing interest in academia and industry, thanks to the availability of sensing technologies that provide high-fidelity data streams for every match. Unfortunately, these detailed data are owned by specialized companies and hence are rarely publicly available for scientific research. To fill this gap, this paper describes the largest open collection of soccer-logs ever released, containing all the spatio-temporal events (passes, shots, fouls, etc.) that occured during each match for an entire season of seven prominent soccer competitions. Each match event contains information about its position, time, outcome, player and characteristics. The nature of team sports like soccer, halfway between the abstraction of a game and the reality of complex social systems, combined with the unique size and composition of this dataset, provide an ideal ground for tackling a wide range of data science problems, including the measurement and evaluation of performance, both at individual and at collective level, and the determinants of success and failure.

Extracting spatial-temporal features that describe a team match demands when considering the effects of the quality of opposition in elite football

Spatiotemporal patterns of play can be extracted from competitive environments to design representative training tasks and underlying processes that sustain performance outcomes. To support this statement, the aims of this study were: (i) describe the collective behavioural patterns that relies upon the use of player positioning in interaction with teammates, opponents and ball positioning; (ii) and define the underlying structure among the variables through application of a factorial analysis. The sample comprised a total of 1,413 ball possession sequences, obtained from twelve elite football matches from one team (the team ended the season in the top-5 position). The dynamic position of the players (from both competing teams), as well as the ball, were captured and transformed to two-dimensional coordinates. Data included the ball possession sequences from six matches played against top opponents (TOP, the three teams classified in the first 3 places at the end of the season) and six matches against bottom opponents (BOTTOM, the three teams classified in the last 3 at the end of the season). The variables calculated for each ball possession were the following: ball position; team space in possession; game space (comprising the outfield players of both teams); position and space at the end of ball possession. Statistical comparisons were carried with magnitude-based decisions and null-hypothesis analysis and factor analysis to define the underlying structure among variables according to the considered contexts. Results showed that playing against TOP opponents, there was ~38 meters game length per ~43 meters game width with 12% of coefficient of variation (%). Ball possessions lasted for ~28 seconds and tended to end at ~83m of pitch length. Against BOTTOM opponents, a decrease in the game length with an increase in game width and in the deepest location was observed in comparison with playing against TOP opponents. The duration of ball possession increased considerable (~37 seconds), and the ball speed entropy was higher, suggesting lower levels of regularity in comparison with TOP opponents. The BOTTOM teams revealed a small EPS. The Principal Component Analysis showed a strong association of the ball speed, entropy of the ball speed and the coefficient of variation (%) of the ball speed. The EPS of the team in possession was well correlated with the game space, especially the game width facing TOP opponents. Against BOTTOM opponents, there was a strong association of ball possession duration, game width, distance covered by the ball, and length/width ratio of the ball movement. The overall approach carried out in this study may serve as the starting point to elaborate normative models of positioning behaviours measures to support the coaches’ operating decisions.

Current Approaches to the Use of Artificial Intelligence for Injury Risk Assessment and Performance Prediction in Team Sports: a Systematic Review

Background

The application of artificial intelligence (AI) opens an interesting perspective for predicting injury risk and performance in team sports. A better understanding of the techniques of AI employed and of the sports that are using AI is clearly warranted. The purpose of this study is to identify which AI approaches have been applied to investigate sport performance and injury risk and to find out which AI techniques each sport has been using.

Methods

Systematic searches through the PubMed, Scopus, and Web of Science online databases were conducted for articles reporting AI techniques or methods applied to team sports athletes.

Results

Fifty-eight studies were included in the review with 11 AI techniques or methods being applied in 12 team sports. Pooled sample consisted of 6456 participants (97% male, 25 ± 8 years old; 3% female, 21 ± 10 years old) with 76% of them being professional athletes. The AI techniques or methods most frequently used were artificial neural networks, decision tree classifier, support vector machine, and Markov process with good performance metrics for all of them. Soccer, basketball, handball, and volleyball were the team sports with more applications of AI.

Conclusions

The results of this review suggest a prevalent application of AI methods in team sports based on the number of published studies. The current state of development in the area proposes a promising future with regard to AI use in team sports. Further evaluation research based on prospective methods is warranted to establish the predictive performance of specific AI techniques and methods.

Electronic supplementary material

The online version of this article (10.1186/s40798-019-0202-3) contains supplementary material, which is available to authorized users.

Not Every Pass Can Be an Assist: A Data-Driven Model to Measure Pass Effectiveness in Professional Soccer Matches

In professional soccer, nowadays almost every team employs tracking technology to monitor performance during trainings and matches. Over the recent years, there has been a rapid increase in both the quality and quantity of data collected in soccer resulting in large amounts of data collected by teams every single day. The sheer amount of available data provides opportunities as well as challenges to both science and practice. Traditional experimental and statistical methods used in sport science do not seem fully capable to exploit the possibilities of the large amounts of data in modern soccer. As a result, tracking data are mainly used to monitor player loading and physical performance. However, an interesting opportunity exists at the intersection of data science and sport science. By means of tracking data, we could gain valuable insights in the how and why of tactical performance during a soccer match. One of the most interesting and most frequently occurring elements of tactical performance is the pass. Every team has around 500 passing interactions during a single game. Yet, we mainly judge the quality and effectiveness of a pass by means of observational analysis, and whether the pass reaches a teammate. In this article, we present a new approach to quantify pass effectiveness by means of tracking data. We introduce two new measures that quantify the effectiveness of a pass by means of how well a pass disrupts the opposing defense. We demonstrate that our measures are sensitive and valid in the differentiation between effective and less effective passes, as well as between the effective and less effective players. Furthermore, we use this method to study the characteristics of the most effective passes in our data set. The presented approach is the first quantitative model to measure pass effectiveness based on tracking data that are not linked directly to goal-scoring opportunities. As a result, this is the first model that does not overvalue forward passes. Therefore, our model can be used to study the complex dynamics of build-up and space creation in soccer.

Selection procedures in sports: Improving predictions of athletes' future performance

The selection of athletes has been a central topic in sports sciences for decades. Yet, little consideration has been given to the theoretical underpinnings and predictive validity of the procedures. In this paper, we evaluate current selection procedures in sports given what we know from the selection psychology literature. We contrast the popular clinical method (predictions based on overall impressions of experts) with the actuarial approach (predictions based on pre-defined decision rules), and we discuss why the latter approach often leads to superior performance predictions. Furthermore, we discuss the "signs" and the "samples" approaches. Taking the prevailing signs approach, athletes' technical-, tactical-, physical-, and psychological skills are often assessed separately in controlled settings. However, for predicting later sport performance, taking samples of athletes' behaviours in their sports environment may result in more valid assessments. We discuss the possible advantages and implications of making selection procedures in sports more actuarial and sample-based.