PROJECTS
We are always looking to develop and grow our skills, which means we constantly involved with some kind of fun exciting project. Take a look at some of the innovative things we have been up to.
We are always interested in new projects so let us know if you have an exciting projects you may think we would be interested in.
Communication and its Impact on Performance: An Analysis of a Professional Sailing Teams Real Time Communication.
Objectives: The study examined the nature of the communicative strategies sailors use
to collaborate in a dynamic environment, and how these strategies relate to and consequently influence performance.
Design: A combination of descriptive statistics and Conversation Analysis was used to examine elite sailors’ communications during the start of a race.
Method: Competition starts were both audio and video recorded. Recordings were coded for frequency of an individual’s communication, as well as the frequency of differing types of communication. The data was analyzed for evidence of recurring patterns for both verbal and non-verbal forms of communication using a chi square analysis (alpha = 0.05).
Results: Total frequency of communication had no association to starting performance; likewise the frequencies of type of communication had no association with starting performance. Individual communication frequency did demonstrate an association with starting performance. Qualitative conversation analysis showed action statements to take the form of short direct messages, communication to be very consistent in structure, timing and phraseology, and responses to be either formed of short acknowledgements, repetition of the instruction, or just direction action.
Conclusions: Overall communication frequency had no association to starting performance, where as the frequency of communication types, and individual’s communication frequency did have an affect on performance. Conversation analysis indicated that consistency, phraseology and structure within communication help form communication leading to higher performance and that the frequency of communication types by individual is a product of preconditioned communication.
How Does The Relationship Between Emotional Intelligence And Competitive Performance Anxiety Impact Upon Competitive Performance?
Thestudy explores the suggestion that emotional intelligence may be an important construct in the sport domain, although widely accepted that emotions play a large part in the development and performance of athletes and teams, little research explores these relationships. In order to begin to gain an understanding of these relationships, analysis of participant’s competitive performance anxiety, emotional intelligence and competitive task performance were reviewed. Fifty participants were used (23 males; M = 26.2 years, SD = 9.38, 27 females; M = 24.8 years, SD = 11.82). Schutte et al. (1998) Self Report Emotional intelligence Test (SREIT) was used to measure emotional intelligence, and Martins et al. (1990) Sport Competitive Anxiety Test (SCAT) was used to measure competitive performance anxiety. . A simple timed cup-stacking task was used as performance measure. The data was analysed using a Pearson Correlation to determine a significant negative correlation (p = 0.048 two tailed) between competitive performance anxiety and emotional intelligence. And a Factorial Mixed ANOVA was used to access the significance of the differences between each of the levels and the dependent variable, no significant relationship between competitive performance anxiety (t = -2.053, df = 50, p = 0.159 ns), emotional intelligence (t = -0.550, df = 50, p = 0.462 ns) and competitive task performance was reported. Similarly competitive performance anxiety combined with emotional intelligence also showed no significant relationship with competitive task performance (t = -0.23, df = 50, p = 0.881).
The Antecedents And Functions Of Self-Talk And Their Effects On Communication In High Performance Sailing
The study explored the antecedents and functions of self-talk in relation to the effectiveness of communication in high performance sailing. Semi-structured interviews were conducted with six British Olympic Development Squad double- handed sailors. The data was analysed using causal networks (Hanton et al., 2007). Seven primary antecedents emerged from the data, which influenced (n = 7) communication effectiveness. These comprised; environmental conditions, performance in race, teammates communication, stage in race, boat handling, other boats and emotional state. Self-talk was shown to serve a number of positive functions, specifically; maintenance of perceptual field, increased understanding and meaning of information, increased memory, thought sharing, changed type of communication, increased perceptions of control, instructional, motivational, communication purpose and recognition of emotional state. Negative functions comprised decreased focus, decreased communication and decreased task relevant information. Communication was found to both influence and be influenced by self-talk, which in-turn influenced communication effectiveness. The causal networks provided a detailed description of the pathways through which self-talk influenced communication. The findings are discussed in relation to existing empirical research.
A Game Theory Approach To Analysis Starting Tactics In Sailing
The start offers competitors the opportunity to gain an advantage over one an other, giving sailors a greateropportunity to sail the shortest course possible, as freely as possible and therefore being the first boat tofinish the course and win the race. Different starting strategies have evolved for this reason and are herecategorised into six main strategies. This paper looks to establish a new approach to picking such a startingstrategy. This has been done by analysing each strategy with the use of game theory and agent-basedsimulations. If we know a crew’s chosen starting strategy, an initial utility function can be created. Withthe help of the initial utility function, a utility function for every time step has been generated by iteratingbackwards. Simulations have then been created for the start, with the use of the generated utility function space. By then letting a boat from the average worst performing strategy update to a better strategy, we will get a new strategy distribution. By iterate enough times, one hopefully ends up in a evolutionarystable state, where no boat will benefit to change their strategy.
