R Analytics Tool to Study the Performance of the D1 Women’s Basketball Team at Sacred Heart University

The R Analytics tool leverages the power of the R programming language, a versatile and widely used software environment for statistical computing, to provide a comprehensive and sophisticated analysis of the team\u27s performance. Advanced statistical techniques are employed to generate insights into the team\u27s performance and identify areas for improvement. The tool also integrates data from multiple sources, including game statistics, player performance data and other relevant information, to create a comprehensive picture of the team\u27s performance. The goal of the tool is to support data-driven decision making and help coaches and staff at Sacred Heart University gain a deeper understanding of the team\u27s strengths and weaknesses. By using this tool, they can make informed decisions about player development, strategy and tactics, and optimize the team\u27s performance. In addition, the tool can also be used to monitor player performance over time and detect trends, allowing the team to make adjustments as needed to maintain a competitive edge

A Dynamic Online Dashboard for Tracking the Performance of Division 1 Basketball Athletic Performance

Using Data Analytics is a vital part of sport performance enhancement. We collect data from the Division 1 \u27Women\u27s basketball athletes and coaches at our university, for use in analysis and prediction. Several data sources are used daily and weekly: WHOOP straps, weekly surveys, polar straps, jump analysis, and training session information. In this paper, we present an online dashboard to visually present the data to the athletes and coaches. R shiny was used to develop the platform, with the data stored on the cloud for instant updates of the dashboard as the data becomes available. The performance of athletes can be compared to the group averages, while coaches have access to all athletes and can compare them to each other and the team averages for all parameters. A simple color-coded design was utilized to convey the coaches which of the measured parameters is in an acceptable range and which is deficient. The dashboard was reviewed by the athletes, coaches, and exercise scientists and was useful for their needs

In the Race Towards Infinity, Who Wins: Exponential or Polynomial?

Part of understanding the global dynamics of mathematical models is to investigate the end behaviors (i.e. limits at infinity) of these models. As shown in almost every precalculus course, values of both exponential functions of the form ekx , k \u3e 0, and polynomials with positive leading coefficient grow as their input values gets “arbitrarily large”. Motivated by these facts, we investigate how the growth of exponential functions compares to the growth of polynomials. In particular, we show that every function of the form ekx, for k \u3e 0, eventually dominates every polynomial

Obesity, inflammatory and thrombotic markers, and major clinical outcomes in critically ill patients with COVID‐19 in the US

Objective This study aimed to determine whether obesity is independently associated with major adverse clinical outcomes and inflammatory and thrombotic markers in critically ill patients with COVID‐19. Methods The primary outcome was in‐hospital mortality in adults with COVID‐19 admitted to intensive care units across the US. Secondary outcomes were acute respiratory distress syndrome (ARDS), acute kidney injury requiring renal replacement therapy (AKI‐RRT), thrombotic events, and seven blood markers of inflammation and thrombosis. Unadjusted and multivariable‐adjusted models were used. Results Among the 4,908 study patients, mean (SD) age was 60.9 (14.7) years, 3,095 (62.8%) were male, and 2,552 (52.0%) had obesity. In multivariable models, BMI was not associated with mortality. Higher BMI beginning at 25 kg/m2 was associated with a greater risk of ARDS and AKI‐RRT but not thrombosis. There was no clinically significant association between BMI and inflammatory or thrombotic markers. Conclusions In critically ill patients with COVID‐19, higher BMI was not associated with death or thrombotic events but was associated with a greater risk of ARDS and AKI‐RRT. The lack of an association between BMI and circulating biomarkers calls into question the paradigm that obesity contributes to poor outcomes in critically ill patients with COVID‐19 by upregulating systemic inflammatory and prothrombotic pathways