External Workloads Vary by Position and Game Result in US-based Professional Soccer Players.

Professional soccer is a physically demanding sport that requires players to be highly trained. Advances using GPS allow the tracking of external workloads for individual players in practice and competition, however, there is a lack of evidence on how these measures impact match results. Therefore, we analyzed external workloads by player position and determined if they vary depending on the result of competitive matches. External workloads were analyzed in professional soccer players (n = 25) across 28 competitive games. One-way ANOVA determined if workloads varied by position (striker - ST, wide midfielder - WM, central midfielder - CM, wide defender - WD, central defender - CD) or across games won (n = 8), lost (n = 13) or tied (n = 7). Repeated-measures ANOVA assessed differences in workloads specific to each position in each of the result categories. Statistical significance was set at p < 0.05. Across all games, more high-speed and very-high speed running was done by ST and WD compared to CD (p < 0.001) and CM (p < 0.001 - 0.02). Whole-team data showed no differences in any external workload variable with respect to match result (p > 0.05), however, in games won ST did more very high-speed running than in losing games (p = 0.03) and defending players did more high and very high-speed running in games tied vs. those won or lost (p < 0.05). Whole-team external workloads do not vary depending on the match result; however, high speed running may be a differentiating factor at the positional level. Coaches should consider position-specific analysis when examining player workloads.

Improving Visualization Design for Effective Multi-Objective Decision Making.

Decision-makers across many professions are often required to make multi-objective decisions over increasingly larger volumes of data with several competing criteria. Data visualization is a powerful tool for exploring these complex 'solution spaces', but there is limited research on its ability to support multi-objective decisions. In this article, we explore the effects of chart complexity and data volume on decision quality in multi-objective scenarios with complex trade-offs. We look at the impact of four common multidimensional chart types (scatter plot matrices, parallel coordinates plots, heat maps, radar charts), the number of options and dimensions and participant chart usage experience on decision time and accuracy when selecting the 'optimal option'. As objectively evaluating the quality of multi-objective decisions and the trade-offs involved is challenging, we employ rank- and score-based accuracy metrics. While heat maps demonstrate a time advantage, our findings show no strong performance benefit for one chart type over another for accuracy. We find mixed evidence for the impact of chart complexity on performance, with our results suggesting the existence of a 'ceiling' in the number of dimensions considered by participants. This points to a potential limit to data complexity that is useful for decision making. Lastly, participants who use charts frequently performed better, suggesting that users can potentially be trained to effectively use complex visualizations in their decision-making.

Rural risk environments, opioid-related overdose, and infectious diseases: A multidimensional, spatial perspective.

BACKGROUND: Much remains unknown in rural risk environments, despite a growing crisis in these areas. We adapt a risk environment framework to characterize rural southern Illinois and describe the relations of risk environments, opioid-related overdose, HIV, Hepatitis C, and sexually transmitted infection rates between 2015 and 2017. METHODS: Over two dozen risk environment variables are summarized across zip-code (n = 128) or county levels (n = 16) based on availability and theoretical relevance. We calculate data attribute associations and characterize spatial and temporal dimensions of longitudinal health outcomes and the rural risk environment. We then use a "regional typology analysis" to generate data-driven risk regions and compare health outcomes. RESULTS: Pervasive risk hotspots were identified in more populated locales with higher rates of overdose and HCV incidence, whereas emerging risk areas were isolated to more rural locales that had experienced an increase in analgesic opiate overdoses and generally lacked harm-reduction resources. At-risk areas were characterized with underlying socioeconomic vulnerability but in differing ways, reflecting a nuanced and shifting structural risk landscape. CONCLUSIONS: Rural risk environment vulnerabilities and associated opioid-related health outcomes are multifaceted and spatially heterogeneous. More research is needed to better understand how refining geographies to more precisely define risk can support intervention efforts and further enrich investigations of the opioid epidemic.