Ray Fisher, UM Baseball, baseball card as New York Yankee, ca. 1911(Hassan cigarette card series)

(original loaned by Brian Williams for scanning)

Little league baseball tryouts

General note: Title and date provided by Bettye Lane.

Little league baseball tryouts

General note: Title and date provided by Bettye Lane.

Little league baseball tryouts

General note: Title and date provided by Bettye Lane.

Little league baseball tryouts (Kim Minicica, hitting with Naomi Johnson catching)

General note: Title and date provided by Bettye Lane.

Wearing Eye Tracking Technology During Batting Practice: Assessing the Experiences of Professional Baseball Athletes

Utilizing wearable technology in sport allows for the collection of motor behavior data during task engagement. This data can be assessed in real-time or retrospectively. Although enriching the scope of performance data, the consequences of wearable technology on the athlete-user, specifically the cognitive effects, has not been fully investigated, hence the purpose of this study. This qualitative study examines the cognitions of 57 professional baseball players who wore eye tracking technology whilst engaged in batting practice. Their verbal self-reports were framed by temporal context: before-during-after task. Three themes emerged during the pre-task segment: social appearance anxiety, claimed self-handicapping, and curiosity. During the task of batting, verbal behavior contained motivational and instructional overt self-talk while claimed self-handicapping was sustained. The final, post-performance segment was marked by the re-emergence of curiosity from the pre-task period as well as self-evaluation/appraisal. Given the participants were professional athletes, their performance has greater career implications than amateur competitors. Nonetheless, the verbal behavior elicited while wearing eye tracking technology indicates an awareness of the equipment by the user. This study found cognitive effects from wearable technology; more research is required to under-stand the scope and nature of those effects on cognitive and motor behaviors.

Slicing big data - Twitter, gambling and time sensitive information

Big Data presents many challenges related to volume, whether one is interested in studying past datasets or, even more problematically, attempting to work with live streams of data. The most obvious challenge, in a ‘noisy’ environment such as contemporary social media, is to collect the pertinent information; be that information for a specific study, tweets which can inform emergency services or other responders to an ongoing crisis, or give an advantage to those involved in prediction markets. Often, such a process is iterative, with keywords and hashtags changing with the passage of time, and both collection and analytic methodologies need to be continually adapted to respond to this changing information. While many of the data sets collected and analyzed are preformed, that is they are built around a particular keyword, hashtag, or set of authors, they still contain a large volume of information, much of which is unnecessary for the current purpose and/or potentially useful for future projects. Accordingly, this panel considers methods for separating and combining data to optimize big data research and report findings to stakeholders. The first paper considers possible coding mechanisms for incoming tweets during a crisis, taking a large stream of incoming tweets and selecting which of those need to be immediately placed in front of responders, for manual filtering and possible action. The paper suggests two solutions for this, content analysis and user profiling. In the former case, aspects of the tweet are assigned a score to assess its likely relationship to the topic at hand, and the urgency of the information, whilst the latter attempts to identify those users who are either serving as amplifiers of information or are known as an authoritative source. Through these techniques, the information contained in a large dataset could be filtered down to match the expected capacity of emergency responders, and knowledge as to the core keywords or hashtags relating to the current event is constantly refined for future data collection. The second paper is also concerned with identifying significant tweets, but in this case tweets relevant to particular prediction market; tennis betting. As increasing numbers of professional sports men and women create Twitter accounts to communicate with their fans, information is being shared regarding injuries, form and emotions which have the potential to impact on future results. As has already been demonstrated with leading US sports, such information is extremely valuable. Tennis, as with American Football (NFL) and Baseball (MLB) has paid subscription services which manually filter incoming news sources, including tweets, for information valuable to gamblers, gambling operators, and fantasy sports players. However, whilst such services are still niche operations, much of the value of information is lost by the time it reaches one of these services. The paper thus considers how information could be filtered from twitter user lists and hash tag or keyword monitoring, assessing the value of the source, information, and the prediction markets to which it may relate. The third paper examines methods for collecting Twitter data and following changes in an ongoing, dynamic social movement, such as the Occupy Wall Street movement. It involves the development of technical infrastructure to collect and make the tweets available for exploration and analysis. A strategy to respond to changes in the social movement is also required or the resulting tweets will only reflect the discussions and strategies the movement used at the time the keyword list is created — in a way, keyword creation is part strategy and part art. In this paper we describe strategies for the creation of a social media archive, specifically tweets related to the Occupy Wall Street movement, and methods for continuing to adapt data collection strategies as the movement’s presence in Twitter changes over time. We also discuss the opportunities and methods to extract data smaller slices of data from an archive of social media data to support a multitude of research projects in multiple fields of study. The common theme amongst these papers is that of constructing a data set, filtering it for a specific purpose, and then using the resulting information to aid in future data collection. The intention is that through the papers presented, and subsequent discussion, the panel will inform the wider research community not only on the objectives and limitations of data collection, live analytics, and filtering, but also on current and in-development methodologies that could be adopted by those working with such datasets, and how such approaches could be customized depending on the project stakeholders.

Physical activities and sedentary pursuits in African American and Caucasian girls

The purposes of this study were to describe and compare the specific physical activity choices and sedentary pursuits of African American and Caucasian American girls. Participants were 1,124 African American and 1,068 Caucasian American eighth grade students from 31 middle schools. The 3-Day Physical Activity Recall (3DPAR) was used to measure participation in physical activities and sedentary pursuits. The most frequently reported physical activities were walking, basketball, jogging or running, bicycling, and social dancing. Differences between groups were found in 11 physical activities and 3 sedentary pursuits. Participation rates were higher in African American girls (p<.001)for social dancing, basketball, watching television, and church attendance but lower in calisthenics, ballet and other dance, jogging or running, rollerblading, soccer, softball or baseball, using an exercise machine, swimming, and homework. Cultural differences of groups should be considered when planning interventions to promote physical activity.