Build an NBA Fantasy Projection Model in Python with Pandas

Build an NBA Fantasy Projection Model in Python with Pandas, available at $49.99, has an average rating of 4.2, with 63 lectures, based on 87 reviews, and has 2096 subscribers.

You will learn about How to build a fantasy basketball projection model in a Jupyter Notebook Pandas library basics for data analysis & data manipulation Fundamentals of the Python programming language How to win your fantasy league using value based drafting This course is ideal for individuals who are Beginner Python Programmers looking to learn the Pandas library or NBA Fantasy Basketball Players It is particularly useful for Beginner Python Programmers looking to learn the Pandas library or NBA Fantasy Basketball Players.

Enroll now: Build an NBA Fantasy Projection Model in Python with Pandas

Summary

Title: Build an NBA Fantasy Projection Model in Python with Pandas

Price: $49.99

Average Rating: 4.2

Number of Lectures: 63

Number of Published Lectures: 63

Number of Curriculum Items: 63

Number of Published Curriculum Objects: 63

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

How to build a fantasy basketball projection model in a Jupyter Notebook

Pandas library basics for data analysis & data manipulation

Fundamentals of the Python programming language

How to win your fantasy league using value based drafting

Who Should Attend

Beginner Python Programmers looking to learn the Pandas library

NBA Fantasy Basketball Players

Target Audiences

Beginner Python Programmers looking to learn the Pandas library

NBA Fantasy Basketball Players

What is this Course?

Let me start off by saying that my first love has always been the NBA and my second love is coding. As such, I think this class will be a lot of fun for passionate NBA fans who also happen to be aspiring coders. This is the premier Udemy class out there that uses strictly NBA stats as data to help wrap your head around concepts in the python programming language.

While I have found it helpful to read textbooks and watch online tutorials to get a better understanding of the basics for any subject, nothing beats project-based learning. Actually getting your hands dirty and running into real problems that require specific solutions has been my ideal way to learn something new.

With that being said, the hardest question typically is, what project should I focus on? From my personal experience, I’ve found it beneficial to focus on something you are passionate about. To find that something, just think of what you frequently pay attention to in your spare time, when no one is paying youto pay attention to it. For me, that something is the NBA. I’m a proud subscriber to League Pass. It didn’t take long for me to realize that using NBA stats was going to be the best way for me to learn how to code.

“For one, sports has served as an entry point to data analysis for many. Sports is interesting and has great data relative to other fields, so it can teach skills and methods of thought that are then more broadly applicable. Personally, I learned how to program, a skill that has been enormously valuable to me, specifically to analyze basketball stats. And I’m far from the only story like this.” -Ben Falk, Cleaning The Glass

The Project

Using the NBA to learn how to code sounds like a good start, but it it still missing a key piece to turn it into an actual project. That key piece is a goal. Tiago Forte defines a project as, “a series of tasks linked to a goal, with a deadline.”

So what is our goal? Well, for those of you that have played fantasy basketball before, you may have learned how important the draft is. Your team’s success is often times linked directly to your success in the draft. And your success in the draft is often linked to how effectively you can project player stats for that upcoming year. If you know Lebron James is going to score more fantasy points than Anthony Davis then you will want Lebron James on your fantasy team.

After blindly turning to the internet for many consecutive years to use projection models that weren’t made by the oafs at ESPN or Yahoo, it dawned on me that said models had to come from someone’s brain. My thinking from there was, “what’s stopping me from building my own projection model?”

Alas! We have our class project! We are going to build an NBA Fantasy Projection model so you can win your NBA Fantasy League! And how will we do that? By learning to code!

What Will You Learn?

This is another reminder that everything I’ve done to date has been a combination of self-teaching and learning from a friend who also happens to be a talented engineer.

For our purposes, we are going to focus on Python. I’ve been hooked on it ever since I took the class Automate the Boring Stuff with Python. It’s undoubtedly a popular programming language so I think it will be beneficial for many years to come.

This class is not meant to be an introduction to programming or python, so my assumption is that you understand some basics. This class is geared more towards helping you apply Python programming to an actual project to help you better retain information while having fun within the process.

Since this class is primarily focused on data (in the form of NBA stats), we will need to manipulate the data in various ways. To help with this, we’ll use the Pandas library. Pandas is extremely powerful and can be used in more ways than just building NBA fantasy projection models so I think you will find it extremely helpful to learn more about.

In his book, Jake VanderPlas describes Pandas as, “a newer package built on top of NumPy, and provides an efficient implementation of a DataFrame. dataFrames are essentially multidimensional arrays with attached row and column labels, and often with heterogeneous types and/or missing data. As well as offering a convenient storage interface for labeled data, Pandas implements a number of powerful data operations familiar to users of both database frameworks and spreadsheet programs.” Said another way, Pandas is SQL and Excel on steroids!

By the end of this course you will be ready to win your NBA fantasy league by building the best fantasy projection model using Python and more specifically Pandas. All of this will be done using a Jupyter Notebook so you can share your work and improve on it over the years.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Intro

Lecture 2: Installation

Lecture 3: Virtual Environments

Lecture 4: Modules & Packages

Lecture 5: Resources

Chapter 2: Jupyter Notebook

Lecture 1: Setting Up Jupyter Notebook

Lecture 2: Cell Types & Kernels

Lecture 3: Running Code

Lecture 4: Resources

Chapter 3: Python Fundamentals

Lecture 1: Python Overview

Lecture 2: Importing Modules

Lecture 3: Printing

Lecture 4: Variables & Raw Input

Lecture 5: Lists

Lecture 6: Dictionaries

Lecture 7: For Loops

Lecture 8: If Else Statements

Lecture 9: Functions

Lecture 10: Arrays

Lecture 11: Resources

Chapter 4: Pandas Building Blocks

Lecture 1: Pandas Intro

Lecture 2: Dataframes & Series

Lecture 3: Creating a Dataframe

Lecture 4: Reading a CSV File

Lecture 5: Attributes & Methods

Lecture 6: Selecting Columns

Lecture 7: Adding & Deleting Columns

Lecture 8: Renaming Columns

Lecture 9: Selecting Rows

Lecture 10: Adding & Dropping Rows

Lecture 11: Inplace Parameter

Lecture 12: Sorting Dataframes

Lecture 13: Filtering Dataframes

Lecture 14: Groupby

Lecture 15: Concatenate & Append

Lecture 16: Merging & Joining

Lecture 17: Iterating Over Dataframes

Lecture 18: Applying Functions

Lecture 19: Arrays

Lecture 20: Resources

Chapter 5: Building Our Model

Lecture 1: Projection Model Overview

Lecture 2: Cleaning NBA Data

Lecture 3: Normalizing Season Data

Lecture 4: Player Distance Function

Lecture 5: Find Player Function

Lecture 6: Calculating Player Similarity

Lecture 7: Comparing Multiple Players in a For Loop

Lecture 8: Weighting Stat Columns

Lecture 9: Weighted Avg. Using Multiple Players Next Season

Lecture 10: Player Comparison Function

Lecture 11: Projecting 2018-19 Season Stats

Lecture 12: Resources

Chapter 6: Measuring Our Model

Lecture 1: Using RSME to Evaluate Our Model

Lecture 2: Comparing to Competitors Pt. 1

Lecture 3: Comparing to Competitors Pt. 2

Lecture 4: Adjusting Variables

Lecture 5: Resources

Chapter 7: Winning Your Fantasy League

Lecture 1: Converting to Fantasy Points

Lecture 2: Value Based Drafting

Lecture 3: Getting Our Baseline Numbers

Lecture 4: Draft Preparation

Lecture 5: Wrapping Up & Model Blind Spots

Lecture 6: Resources

Instructors

John Mannelly
Online Teacher & Student

Rating Distribution

1 stars: 2 votes

2 stars: 2 votes

3 stars: 11 votes

4 stars: 25 votes

5 stars: 47 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!