README

Important Links:

• Github Repo: https://github.com/sahil-681/Gymnastics-Data-Challenge

• R Shiny App: https://sahil-singh-6.shinyapps.io/gymnastics

• Research Poster: https://sahil-681.github.io/portfolio/assets/img/poster.pdf

Introduction:

The goal of this analysis and Shiny app is to navigate the multifaceted challenges of selecting the most optimum team to pick for the 2024 Paris Olympics Gymnastics Event. Our tool is built on the comprehensive dataset provided, and takes the help of a plethora of R scripts written by us to analyze and utilize past competition statistics and current performance trends in the end goal of building the interactive application which can be used by the end user easily.

This file outlines the purpose of each script present in our submission.

Key components of the repository:

• app.R: Integrates various components of the application, including user interface elements and sourcing other scripts, to create the main application for gymnastics team selection.

• fit.model.R: Computes mean scores and standard deviations for athletes across different apparatuses, as a preliminary step for model fitting or data analysis.

• get_default_assignments.R: Defines a function to assign default athlete selections for teams based on top performances, segregated by gender and apparatus.

• get_reasonable_set.R: Implements a function to select a reasonable set of competitors for each country and gender, considering performance data and a specified number of top performers.

• get.best.teams.R: Processes data to identify and recommend the best gymnastics teams, taking into account factors like team performance and athlete compatibility. This script runs all of the simulations

• get.data.R: Loads gymnastics data from CSV files for the years 2017-2021 and 2022-2023.

• main.R: A master script that orchestrates the entire gymnastics case study. It sources and executes other scripts like get.data.R, prep.data.R, fit.model.R, and get.best.teams.R to generate all outputs.

• prep.data.R: Contains functions for data preprocessing, including date processing and data cleaning, to prepare gymnastics datasets for analysis and modeling.

• qual_names_aa.R: Reads and combines athlete data from various sources to create a consolidated list of athletes eligible for all-around events, including individual and team qualifiers.

• qual_names_e.R: Prepares a dataset of athletes eligible for event qualifications, including detailed scores across different apparatuses, used for further analysis in event-specific qualifications.

• qual_names_t.R: Processes and filters athlete data to determine top performers for team qualifications, focusing on aggregate scores across different apparatuses.

• query.existing.database.R: Implements functions to modify data weights based on medal standings and to exclude specific individuals from datasets for analysis purposes.

• run_sims.R: Defines a function to tally medals based on simulation results, integrating data from various sources to produce a comprehensive medal count.

• simulate_medals.R: Simulates medal outcomes for gymnastics events using specified teams and athlete data, integrating default assignments and simulation logic.

• styles.css: A Cascading Style Sheets (CSS) file that defines the styling rules for the application’s user interface. It includes font size adjustments for various screen sizes to ensure responsive and visually appealing design across different devices.