The application of social network analysis to team sports

This article reviews how current social network analysis might be used to investigate individual and group behavior in sporting teams. Social network analysis methods permit researchers to explore social relations between team members and their individual-level qualities simultaneously. As such, social network analysis can be seen as augmenting existing approaches for the examination of intra-group relations among teams and provide detail of team members' informal connections to others within the team. Social network analysis is useful in addressing the issue of interdependencies in the data inherent in team structures. Social network terms are introduced and explained by way of an example team, software and resources are discussed, and a statistical approach to social network analysis is introduced.

Accelerometer validity to measure and classify movement in team sports

 In team sports accelerometers are used to monitor the physical demands of athletic performance. Daniel's research showed that accelerometer accuracy can be improved through filtering. He also showed that the accelerometer can be used to automatically classify the type of movement performed. Further improving the understanding of team sports.

Emotional contagion in team sports and its impact on individual performance – an experimental study

In sport psychology research about emotional contagion in sport teams has been scarce (Reicherts & Horn, 2008). Emotional contagion is a process leading to a specific emotional state in an individual caused by the perception of another individual’s emotional expression (Hatfield, Cacioppo & Rapson, 1994). Apitzsch (2009) described emotional contagion as one reason for collapsing sport teams. The present study examined the occurrence of emotional contagion in dyads during a basketball task and the impact of a socially induced emotional state on performance. An experiment with between-subjects design was conducted. Participants (N=81, ♀=38, M=21.33 years, SD=1.45) were randomly assigned to one of two experimental conditions, by joining a confederate to compose a same gender, ad hoc team. The team was instructed to perform a basketball task as quickly as possible. The between-factor of the experimental design was the confederate’s emotional expression (positive or negative valence). The within-factor was participants’ emotional state, measured pre- and post-experimentally using PANAS (Krohne, Egloff, Kohlmann & Tausch, 1996). The basketball task was video-taped and the number of frames participants needed to complete the task was used to determine the individual performance. The confederate’s emotional expression was appraised in a significantly different manner across both experimental conditions by participants and video raters (MC). Mixed between-within subjects ANOVAs were conducted to examine the impact of the two conditions on participants’ scores on the PANAS subscales across two time periods (pre- and post-experimental). No significant interaction effects but substantial main effects for time were found on both PANAS subscales. Both groups showed an increase in positive and a reduction in negative PANAS scores across these two time periods. Nevertheless, video raters assessment of the emotional states expressed by participants was significantly different between the positive (M=3.23, SD=0.45) and negative condition (M=2.39, SD=0.53; t=7.64, p<.001, eta squared=.43). An independent-samples t-test indicated no difference in performance between conditions. Furthermore, no significant correlation between the extent of positive or negative emotional contagion and the number of frames was observed. The basketball task lead to an improvement of the emotional state of participants, independently of the condition. Even though participants PANAS scores indicated a tendency to emotional contagion, it was not statistically significant. This could be explained by the low task duration of approximately three minutes. Moreover, the performance of participants was unaffected by the experimental condition or the extent of positive or negative emotional contagion. Apitzsch, E. (2009). A case study of a collapsing handball team. In S. Jern & J. Näslund (Eds.), Dynamics within and outside the lab. Proceedings from The 6th Nordic Conference on Group and Social Psychology, May 2008, Lund, pp. 35-52. Hatfield, E., Cacioppo, J. T. & Rapson, R. L. (1994). Emotional contagion. Cambridge: University Press. Krohne, H. W., Egloff, B., Kohlmann, C.-W. & Tausch, A. (1996). Untersuchungen mit einer deutschen Version der „Positive und Negative Affect Schedule“ (PANAS). Diagnostica, 42 (2), 139-156. Reicherts, M. & Horn, A. B. (2008). Emotionen im Sport. In W. Schlicht & B. Strauss (Eds.), Enzyklopädie der Psychologie. Grundlagen der Sportpsychologie (Bd. 1) (S. 563-633). Göttingen: Hogrefe.

The implicit achievement motive predicts match performance and the explicit motive choices for goal distances in team sports

Motivational research over the past decade has provided ample evidence for the existence of two distinct motivational systems. Implicit motives are affect-based needs and have been found to predict spontaneous behavioral trends over time. Explicit motives in contrast represent cognitively based self-attributes and are preferably linked to choices. The present research examines the differentiating and predictive value of the implicit vs. explicit achievement motives for team sports performances. German students (N = 42) completed a measure of the implicit (Operant Motive Test) and the explicit achievement motive (Achievement Motive Scale-Sport). Choosing a goal distance is significantly predicted by the explicit achievement motive measure. By contrast, repeated performances in a team tournament are significantly predicted by the indirect measure. Results are in line with findings showing that implicit and explicit motive measures are associated with different classes of behavior.

Coach Behaviors and Athlete Satisfaction in Team and Individual Sports

The coach can have a profound impact on athlete satisfaction, regardless of the level of sport involvement. Previous research has identified differences between coaching behavior preferences in team and individual sport athletes. The present study examined the moderating effect that an athlete's sport type (i.e., individual or team) may have on the relationships among seven coaching behaviors (mental preparation, technical skills, goal setting, physical training, competition strategies, personal rapport, and negative personal rapport) for predicting coaching satisfaction. Moderated multiple regression analyses indicated that each of the seven coaching behaviors were significant main effect predictors of coaching satisfaction. However, sport type (i.e., team or individual sports) was found to moderate six of the seven relationships: mental preparation, technical skills, goal setting, competition strategies, personal rapport, and negative personal rapport in predicting satisfaction with the coach. These findings indicate that high coaching satisfaction for athletes in team sports is influenced to a greater extent by the demonstration of these behaviors than it is for individual sport athletes.

Coach Behaviors and Athlete Satisfaction in Team and Individual Sports

The coach can have a profound impact on athlete satisfaction, regardless of the level of sport involvement. Previous research has identified differences between coaching behavior preferences in team and individual sport athletes. The present study examined the moderating effect that an athlete's sport type (i.e., individual or team) may have on the relationships among seven coaching behaviors (mental preparation, technical skills, goal setting, physical training, competition strategies, personal rapport, and negative personal rapport) for predicting coaching satisfaction. Moderated multiple regression analyses indicated that each of the seven coaching behaviors were significant main effect predictors of coaching satisfaction. However, sport type (i.e., team or individual sports) was found to moderate six of the seven relationships: mental preparation, technical skills, goal setting, competition strategies, personal rapport, and negative personal rapport in predicting satisfaction with the coach. These findings indicate that high coaching satisfaction for athletes in team sports is influenced to a greater extent by the demonstration of these behaviors than it is for individual sport athletes.

Power law distributions in pattern dynamics of attacker-defender dyads in the team sport of Rugby Union : phenomena in a region of self-organized criticality?

In the region of self-organized criticality (SOC) interdependency between multi-agent system components exists and slight changes in near-neighbor interactions can break the balance of equally poised options leading to transitions in system order. In this region, frequency of events of differing magnitudes exhibits a power law distribution. The aim of this paper was to investigate whether a power law distribution characterized attacker-defender interactions in team sports. For this purpose we observed attacker and defender in a dyadic sub-phase of rugby union near the try line. Videogrammetry was used to capture players’ motion over time as player locations were digitized. Power laws were calculated for the rate of change of players’ relative position. Data revealed that three emergent patterns from dyadic system interactions (i.e., try; unsuccessful tackle; effective tackle) displayed a power law distribution. Results suggested that pattern forming dynamics dyads in rugby union exhibited SOC. It was concluded that rugby union dyads evolve in SOC regions suggesting that players’ decisions and actions are governed by local interactions rules.

Characterizing multi-agent team behavior from partial team tracings : evidence from the English Premier League

Real-world AI systems have been recently deployed which can automatically analyze the plan and tactics of tennis players. As the game-state is updated regularly at short intervals (i.e. point-level), a library of successful and unsuccessful plans of a player can be learnt over time. Given the relative strengths and weaknesses of a player’s plans, a set of proven plans or tactics from the library that characterize a player can be identified. For low-scoring, continuous team sports like soccer, such analysis for multi-agent teams does not exist as the game is not segmented into “discretized” plays (i.e. plans), making it difficult to obtain a library that characterizes a team’s behavior. Additionally, as player tracking data is costly and difficult to obtain, we only have partial team tracings in the form of ball actions which makes this problem even more difficult. In this paper, we propose a method to overcome these issues by representing team behavior via play-segments, which are spatio-temporal descriptions of ball movement over fixed windows of time. Using these representations we can characterize team behavior from entropy maps, which give a measure of predictability of team behaviors across the field. We show the efficacy and applicability of our method on the 2010-2011 English Premier League soccer data.

Recognising team activities from noisy data

Recently, vision-based systems have been deployed in professional sports to track the ball and players to enhance analysis of matches. Due to their unobtrusive nature, vision-based approaches are preferred to wearable sensors (e.g. GPS or RFID sensors) as it does not require players or balls to be instrumented prior to matches. Unfortunately, in continuous team sports where players need to be tracked continuously over long-periods of time (e.g. 35 minutes in field-hockey or 45 minutes in soccer), current vision-based tracking approaches are not reliable enough to provide fully automatic solutions. As such, human intervention is required to fix-up missed or false detections. However, in instances where a human can not intervene due to the sheer amount of data being generated - this data can not be used due to the missing/noisy data. In this paper, we investigate two representations based on raw player detections (and not tracking) which are immune to missed and false detections. Specifically, we show that both team occupancy maps and centroids can be used to detect team activities, while the occupancy maps can be used to retrieve specific team activities. An evaluation on over 8 hours of field hockey data captured at a recent international tournament demonstrates the validity of the proposed approach.

A modified TRIMP to quantify the in-season training load of team sport players

Thatcher, Rhys, et al., 'A modified TRIMP to quantify the in-season training load of team sport players', Journal of Sport Sciences, (2007) 25(6) pp.629-634 RAE2008

Home field effect and team performance: Evidence from English Premiership football

Thomas, Dennis, Carmichael, Fiona, 'Home field effect and team performance: Evidence from English Premiership football', Journal of Sports Economics (2005) 6(3) pp.264-281 RAE2008