npenaper
Norge Pena Perez
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In-class activities 12 & 13
Intro to Moneyball Analytics in the NBA
Intro to LR (First Steps) with R
We analyzed MLB data to predict salary as a function of OBP and RBIs.
First Steps with R Part 2
Summary of baseball and soccer players stats
Sports Analytics: Activity 1
First Steps with R
Activity_11
Intro MLR with R (Interaction terms)
Intro R-Spring 2026
First steps with R
In-class activities 12 & 13
Intro to NBA Moneyball analytics with R
Sports Analytics: Activity 10
We computed the correlation between the number of wins and the teams' ranking.
Activity_6:Summer2025_Intro to Linear Regression for Sports Analytics
Module2, Activity 6
Module2_Activity_5
Intro to Sports Analytics with R
First Steps in R
Module 2: Activity 4
Intro to Data Visualization with R
In-class activity 3
Module 3 Activities
Intro to Sports Analytics using Moneyball
Intro to Sports Analytics with R part 2 (Activity 5)
Basic calculations using RStudio
Exploring and Understanding Data with R from ML with R
From the book Machine Learning with R by Brett Lantz
RData Structures from Machine Learning with R by Brett Lantz
Introduction to Data Mining and Machine Learning with R
Predicting the WS Winner
This is an intro to Logistic Regression to predict the winner of the world series based on 12 different predictors.
Getting Started with R, Part 2: Computing Basic Baseball Statistics
This is an introduction to the R language focused on the computation of basic statistics used in Baseball.
First Steps with R
This document introduces users to the world of R.
Salary vs Hitting Stats: 2022 Active 1st Base MLB Players
This is a draft on first base players salary as a function of different hitting stats.
In-class activity 9: Dataframes Part 1
This is an introduction to data frames. Everything has been adapted from the book The Art of R Programming.
In-class activity 8: Lists
Adapted from the book The Art of R Programming
Project 2-Draft (dataset group3.csv)
This is a rough draft of project 2 using the dataset group3.csv
