Data Analytics

This chapter sets out to illustrate the dictum that there is (almost) nothing new under the sun. More specifically, its goal is to make the unfamiliar familiar within the field of data analytics. The need for such a treatment can be gauged from the plethora of terms currently vying for attention in the contemporary data analysis landscape, which can be puzzling even for seasoned researchers. These terms include: data mining, data science, data analytics, machine learning, deep learning, neural networks, and artificial intelligence. Hybrid terms such as ‘big data analytics’ are also emerging. As for the current front-runner term, data analytics, the evidence provided by the number of search engine hits reveals multiple competing versions subdivided by application domains, ranging from business analytics and crime analytics, to performance analytics, visual analytics, and many more. There is also an emerging software sub-industry providing tools for data analytics, many of which are named after the company which originally developed them

INVESTIGATING FRUIT AND VEGETABLE VARIETY IN A NATIONAL FOOD CO-OP: A BRIGHTER BITES EVALUATION

American children eat fewer fruits and vegetables (F&V) and less variety of F&V than recommended for health. Food cooperatives and other programs have become a popular way to increase F&V intake, but little is known about the variety of F&V distributed by these programs or its relationship with program attendance or child F&V intake. Brighter Bites is a national, school-based food co-op distributing rescued, donated, fresh F&V to families in low-income schools. We evaluated, for the first time, the variety of F&V Brighter Bites distributed to families in the 2018-2019 school year and the relationships between that variety and both child F&V intake and family program attendance. We categorized the F&V distributed in the 2018-2019 school year using the Brighter Bites internal variety matrix and described them in detail using frequencies and percentages. We generated a variety score for each family in a subpopulation (n=3,790) of survey respondents based on the specific F&V distributed the weeks they attended. A generalized ordinal estimation model was specified to evaluate the relationship between family variety score and parent-reported child F&V intake before and after participating in Brighter Bites. We generated a variety score for schools (n=90) based on the specific F&V distributed at each school across 16 weeks of programming, then specified a multilevel negative binomial model to assess the relationship between school variety score and family program attendance. Additional post hoc analyses were completed. Across six cities, Brighter Bites distributed 109 types of F&V in the 2018-2019 school year. Families most frequently received starchy and root vegetables (white potatoes and carrots) and citrus fruits (limes and oranges), but they received dark leafy green vegetables and berries infrequently. Our statistical models were not significant overall, but in post hoc analyses of school F&V variety score and family program attendance we found differences between cities which may have obscured a relationship in our original model. Researchers are still in the early stages of evaluating and understanding relationships between the variety of F&V programs distribute and desired program and behavioral outcomes. Counting only the variety of F&V distributed by a program is inadequate to describe its influences on individual behaviors. Additional, more sensitive measures and variables, informed by a behavioral theory such as Social Cognitive Theory, should be used in future analyses to model better the intrapersonal, interpersonal, and environmental factors which influence desired outcomes

Health Insurance for Displaced Workers

CRS_January_2003_Health_Insurance_for_Displaced_Workers.pdf: 566 downloads, before Oct. 1, 2020

Assessing group-based changes in high-performance sport. Part 1: null hypothesis significance testing and the utility of p values

The role of a strength and conditioning coach (SCC) has evolved over the last 10 years to accommodate the large influx of data now available. As such, today’s SCC must extend their skill set to include data analysis, understanding the validity and utility of p values, effect sizes, confidence intervals, and terms such as the smallest worthwhile change, and minimal difference. The aim of part one of this two-part review is to define and discuss the utility of null hypothesis significance testing (NHST), p values, and error rates. In part two, we introduce effect sizes, measures of variability, and confidence intervals, culminating in recommendations as to which may be the most viable options within the context of performance-based sport, and thus potential methods to report group-based changes

Using principal component analysis to develop performance indicators in professional rugby league

Previous research on performance indicators in rugby league has suggested that dimension reduction techniques should be utilised when analysing sporting data sets with a large number of variables. Forty-five rugby league team performance indicators, from all 27 rounds of the 2012, 2013 and 2014 European Super League seasons, collected by Opta, were reduced to 10 orthogonal principal components with standardised team scores produced for each component. Forced-entry logistic (match outcome) and linear (point’s difference) regression models were used alongside exhaustive chi-square automatic interaction detection decision trees to determine how well each principle component predicted success. The 10 principal components explained 81.8% of the variance in point’s difference and classified match outcome correctly ~90% of the time. Results suggested that if a team increased “amount of possession” and “making quick ground” component scores, they were more likely to win (β = 15.6, OR = 10.1 and β = 7.8, OR = 13.3) respectively. Decision trees revealed that “making quick ground” was an important predictor of match outcome followed by “quick play” and “amount of possession”. The use of PCA provided a useful guide on how teams can increase their chances of success by improving performances on a collection of variables, instead of analysing variables in isolation

Assessing group-based changes in high-performance sport. Part 2: effect sizes and embracing uncertainty through confidence intervals

Today’s strength and conditioning coach must extend their skill set to include data analysis, understating the validity and utility of p values, effect sizes, confidence intervals, and terms such as the smallest worthwhile change, and minimal difference. The aim of part two of this two-part review is to now build on our discussion of null hypothesis significance testing (covered in part one), and introduce effect sizes, measures of variability, and confidence intervals, culminating in recommendations as to which may be the most viable options within the context of performance-based sport, and thus potential methods to report group-based changes. This paper has a series of worked examples to aid the reader