Creating two educational introductory data science modules in R, one focusing on joining datasets with a Formula One database and the other on data visualization techniques using NFL Combine data, to provide learners with hands-on experience and practical skills in data manipulation and visualization.

**Project 1:**

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**Project 2:**

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Study analyzing machine learning methods for predicting player Wins Above Replacement (WAR) in Major League Baseball (MLB). The study highlights the superiority of the Regularization method, specifically Lasso regression, over Random Forests and Neural Networks, offering practical insights for precise player performance forecasts and informed decision-making in MLB teams.

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Comparing Machine Learning Methods for Predicting Wins Above Replacement (WAR) in Major League Baseball

Comprehensive analysis of how coaches and analysts can utilize soccer tracking data analysis to improve a team's defensive performance. The paper provides practical insights into using hierarchical clustering to define defensive formations and make informed decisions on tactical strategies.

**Abstract**

"Organizing For Success: A Tactical Analysis of Defensive Structure in Soccer" provides a
comprehensive analysis of the importance of defensive tactical analysis in soccer and how it can be
applied to improve team performance. The paper provides practical insights into how coaches and
analysts can gain a more comprehensive and objective view of a team's defensive performance by
utilizing data on soccer passing events and player locations.

One of the key methods discussed in the paper is the use of hierarchical clustering to define
soccer defensive formations. This process involves the use of player location data from passing events to
group similar data points into clusters based on their proximity to each other. The process starts by
grouping the data into three clusters representing the formation's three lines (defensive line, midfield
line, forward line), and then further refining the cluster to assign specific positions to players in a
defensive, defensive midfield, attacking midfield and forward line.

This information can be used by coaches and analysts to better understand their team's
strengths and weaknesses and make informed decisions on how to optimize their tactical decisions. For
example, data can be used to identify areas for improvement in their team's defensive structure and
develop strategies to better protect their goal. Additionally, coaches and analysts can use the
information to analyze the effectiveness of their defensive strategies and make changes as needed to
improve their team's performance on the field.

Overall, the paper provides valuable insights into the importance of defensive tactical analysis in
soccer and how coaches and analysts can utilize data analysis to improve their team's performance. By
incorporating these techniques, a better understanding of a team's defensive performance can be found
and informed decisions can be made to help the team succeed on the field.

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