The visual behaviour is a determining factor in sailing due to the influence of the environmental conditions. The aim of this research was to determine the visual behaviour pattern in sailors with different practice time in one star race, applying a probabilistic model based on Markov chains. The sample of this study consisted of 20 sailors, distributed in two groups, top ranking (n = 10) and bottom ranking (n = 10), all of them competed in the Optimist Class. An automated system of measurement, which integrates the VSail-Trainer sail simulator and the Eye Tracking System(TM) was used. The variables under consideration were the sequence of fixations and the fixation recurrence time performed on each location by the sailors. The event consisted of one of simulated regatta start, with stable conditions of wind, competitor and sea. Results show that top ranking sailors perform a low recurrence time on relevant locations and higher on irrelevant locations while bottom ranking sailors make a low recurrence time in most of the locations. The visual pattern performed by bottom ranking sailors is focused around two visual pivots, which does not happen in the top ranking sailor's pattern. In conclusion, the Markov chains analysis has allowed knowing the visual behaviour pattern of the top and bottom ranking sailors and its comparison.

In a sport conditioned by natural elements such as sailing, visual perception is a key factor for the performance. Research has shown that the visual behavior of athletes at different skill levels varies, which may cause differences in the performance achieved. The aim of this research was to examine the visual behavior of sailors from different ranking positions at the start of a race in a simulated situation. Twenty junior sailors (N = 10 top and N = 10 bottom ranking) participated in this study. The visual behavior was recorded at the start of a sailing simulation. The top-ranking sailors performed more visual fixations on the locations that have more highly relevant information, such as "telltales" and "rivals," than do bottom-ranking sailors (p < .005). The top-ranking sailors are closer to the start line at the time of the start signal. The analysis of the visual search strategy shows that top-ranking sailors employed a more active visual search strategy. More experienced athletes can make better use of the information obtained from the important locations.

This research aims to test the suitability of a protocol for automated measurement to describe visual and motor behaviour in the process of learning to sail. The objective is to provide coaches with the necessary scientific and technological support to analyse the variables of success in race starting. The study was performed with a highly ranked sailor in the Optimist class ranking. The instruments used to carry out the investigation were the sailing simulator VSail-Trainer ® and the eye tracking system ASL ® . Two simulated race starts were performed with a protocol of five minutes. The results show the automated protocol is suitable for measuring the ability of boat handling and visual performance in simulated conditions. Visual behaviour shows that the sailor visually fixates on locations that provide relevant information for the race start such as clock, other competitors, wind direction and the start buoys. K E Y

It is difficult to assess the real effectiveness of the sponsorship of esports for a brand. An initial approach would be to calculate the visibility of the brands’ ads during a broadcast, which is usually characterized in terms of exposition time. Classically, this time was computed manually by visual inspection, but computer vision algorithms have recently automated this process, providing some sort of cost effectiveness parameter. This study goes a step further by proposing a new and complementary research methodology to assess the effectiveness of ads in esports, based on implicit research techniques such as electroencephalogram, galvanic skin response, eye-tracking, and analysis of the gaze behavior of the viewers, along with their emotional and cognitive states. Although there is no scarcity of studies on market investigation and advertising employing these research methodologies, these have not been applied to esports research yet. This study reports the implementation of this methodology in a case study with 48 participants during a given esports match. It is also demonstrated how these new metrics, which capture the non-conscious states of viewers, can be used to assess the performance of ads (in this case, brand logos). Additionally, it is shown how ad exposition time (widely accepted metric to assess ad effectiveness) presents an error of 60.18% with respect to real visualization, and how the methodology presented herein can be used to find the best placements for ad/brand exposure during esports broadcast. Es difícil evaluar la eficacia real del patrocinio de los esports para una marca. Una primera aproximación sería calcular la visibilidad de los anuncios de las marcas durante una retransmisión, que se suele caracterizar en términos de tiempo de exposición. Clásicamente, este tiempo se calculaba manualmente mediante inspección visual, pero recientemente los algoritmos de visión por ordenador han automatizado este proceso, proporcionando algún tipo de parámetro de rentabilidad. Este estudio da un paso más al proponer una metodología de investigación nueva y complementaria para evaluar la eficacia de los anuncios en los deportes electrónicos, basada en técnicas de investigación implícitas como el electroencefalograma, la respuesta galvánica de la piel, el seguimiento ocular y el análisis del comportamiento de la mirada de los espectadores, junto con sus estados emocionales y cognitivos. Aunque no escasean los estudios sobre investigación de mercado y publicidad que emplean estas metodologías de investigación, aún no se han aplicado a la investigación de los deportes electrónicos. Este estudio informa de la aplicación de esta metodología en un estudio de caso con 48 participantes durante un partido de esports. También se demuestra cómo estas nuevas métricas, que captan los estados no conscientes de los espectadores, pueden utilizarse para evaluar el rendimiento de los anuncios (en este caso, los logotipos de las marcas). Además, se muestra cómo el tiempo de exposición de los anuncios (métrica ampliamente aceptada para evaluar la eficacia de los anuncios) presenta un error del 60,18% con respecto a la visualización real, y cómo la metodología aquí presentada puede utilizarse para encontrar las mejores ubicaciones para la exposición de anuncios/marcas durante la emisión de deportes electrónicos.