Do squash players accurately report use of appropriate protective eyewear?

Self-report surveys are a common method of collecting data on protective equipment use in sport. The aim of this study was to assess the validity of self-reported use of appropriate protective eyewear by squash players. Surveys of squash players' appropriate protective eyewear behaviours were conducted over two consecutive years (2002 and 2003) at randomly-selected squash venues in Melbourne, Australia. Over the two years, 1219 adult players were surveyed (response rate of 92%). Trained observers also recorded the actual on-court appropriate protective eyewear behaviours of all players during the survey sessions. Eyewear use rates calculated from both data sources were compared. The self-reported appropriate protective eyewear use rate (9.4%; 95% CI 7.8, 11.0) was significantly higher (1.6 times more) than the observed rate (5.9%; 95%CI 4.6, 7.2). This suggests that players may over-report their use of appropriate protective equipment, though some may have incorrectly classified their eyewear as being appropriate or suitably protective. Studies that rely only on self-report data on protective equipment use need to take into account that this could lead to biased estimates.

Anthropometry profiles of elite rugby players: quantifying changes in lean mass

Objective: To demonstrate the utility of a practical measure of lean mass for monitoring changes in the body composition of athletes. Methods: Between 1999 and 2003 body mass and sum of seven skinfolds were recorded for 40 forwards and 32 backs from one Super 12 rugby union franchise. Players were assessed on 13 (7) occasions ( mean (SD)) over 1.9 (1.3) years. Mixed modelling of log transformed variables provided a lean mass index (LMI) of the form mass/skinfolds(x), for monitoring changes in mass controlled for changes in skinfold thickness. Mean effects of phase of season and time in programme were modelled as percentage changes. Effects were standardised for interpretation of magnitudes. Results: The exponent x was 0.13 for forwards and 0.14 for backs ( 90% confidence limits +/- 0.03). The forwards had a small decrease in skinfolds ( 5.3%, 90% confidence limits +/- 2.2%) between preseason and competition phases, and a small increase ( 7.8%, 90% confidence limits +/- 3.1%) during the club season. A small decrease in LMI (similar to 1.5%) occurred after one year in the programme for forwards and backs, whereas increases in skinfolds for forwards became substantial (4.3%, 90% confidence limits +/- 2.2%) after three years. Individual variation in body composition was small within a season (within subject SD: body mass, 1.6%; skinfolds, 6.8%; LMI, 1.1%) and somewhat greater for body mass (2.1%) and LMI (1.7%) between seasons. Conclusions: Despite a lack of substantial mean changes, there was substantial individual variation in lean mass within and between seasons. An index of lean mass based

Sprint patterns in rugby union players during competition

The purpose of this study was to characterize sprint patterns of rugby union players during competition. Velocity profiles (60 m) of 28 rugby players were initially established in testing from standing, walking, jogging, and striding starts. During competition, the individual sprinting patterns of 17 rugby players were determined from video by using the individual velocity profiles. Forwards commenced sprints from a standing start most frequently (41%), whereas backs sprinted from standing (29%), walking (29%),jogging (29%), and occasionally striding (13%) starts. Forwards and backs achieved speeds in excess of 90% maximal velocity (Vmax) on 5 +/- 4 and 9 +/- 4 occasions (similar to 50% of the sprints performed), respectively, during competition. The higher frequency of sprinting for the backs compared with the forwards highlights the importance of speed training for this positional group. The similar relative distribution of velocities achieved during competition for forwards and backs suggests both positional groups should train acceleration and Vmax qualities. The backs should have a higher total volume of sprint training. Sprinting efforts should be performed from a variety of starting speeds to mimic the movement patterns of competition.

A global vision system for a robot soccer team

This paper describes the real time global vision system for the robot soccer team the RoboRoos. It has a highly optimised pipeline that includes thresholding, segmenting, colour normalising, object recognition and perspective and lens correction. It has a fast ‘paint’ colour calibration system that can calibrate in any face of the YUV or HSI cube. It also autonomously selects both an appropriate camera gain and colour gains robot regions across the field to achieve colour uniformity. Camera geometry calibration is performed automatically from selection of keypoints on the field. The system acheives a position accuracy of better than 15mm over a 4m × 5.5m field, and orientation accuracy to within 1°. It processes 614 × 480 pixels at 60Hz on a 2.0GHz Pentium 4 microprocessor.

Visualising collaboration via email: Finding the key players

Email is an important form of asynchronous communication. Visualizing analyses of email communication patterns during a collaborative activity help us better understand the nature of collaboration, and identify the key players. By analysing the contents of email communication and adding reflective comments on its perceived importance from the participants of a collaboration new information can be gleaned not immediately obvious in its original flat form. This paper outlines a proof-of-concept prototype collaborative email visualisation schema. Data from a collaboration case study is analysed and subsequently employed to construct a display of the relative impact of both key players and the types of email used.

Classifying an opponent's behaviour in robot soccer

This paper illustrates the prediction of opponent behaviour in a competitive, highly dynamic, multi-agent and partially observableenvironment, namely RoboCup small size league robot soccer. The performance is illustrated in the context of the highly successful robot soccer team, the RoboRoos. The project is broken into three tasks; classification of behaviours, modelling and prediction of behaviours and integration of the predictions into the existing planning system. A probabilistic approach is taken to dealing with the uncertainty in the observations and with representing the uncertainty in the prediction of the behaviours. Results are shown for a classification system using a Naïve Bayesian Network that determines the opponent’s current behaviour. These results are compared to an expert designed fuzzy behaviour classification system. The paper illustrates how the modelling system will use the information from behaviour classification to produce probability distributions that model the manner with which the opponents perform their behaviours. These probability distributions are show to match well with the existing multi-agent planning system (MAPS) that forms the core of the RoboRoos system.