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Brendan Kent

23 Jun, 8 tweets, 3 min read

New to sports analytics?

Over the past year, I've put together a series of blog posts and lists of resources to get you started.

Here they all are, in one place 👇

Sports Analytics 101 is series of blog posts that introduces the core concepts behind sports analytics in non-technical terms.
brendankent.com/sports-analyti…

Sports analytics naturally requires a set of technical skills. This post outlines which technical skills you ought to learn, and the order you should prioritize learning them in.
brendankent.com/2020/12/16/lan…

Members of the sports analytics community have put together (mostly free) resources on learning to code for sports analytics. You'll find a number of them here.
brendankent.com/2020/09/15/cod…

This list sports analytics books includes my personal favorites and recommendations from others in the field.
brendankent.com/2021/06/15/spo…

Finding data is critical to any sports analytics project. This list features free data sources for a variety of sports.
brendankent.com/2021/03/09/fre…

Watching videos from past sports analytics conferences is an easy way to immerse yourself in the field and stay up-to-date on trends. Many conferences post these videos in online archives, which you'll find linked in this list.
brendankent.com/2020/09/17/whe…

If you're in school and interested in starting a sports analytics club on campus, this guide will walk you through the steps to get it off the ground.
brendankent.com/2020/09/28/how…

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More from @brendankent

Brendan Kent

@brendankent

10 May

Things about analytics every newcomer should know

(Feel free to add your own)

⬇⬇⬇

Raw data will almost always be messy and cleaning will be a big part of any project.

If you expect that, you'll be less frustrated with the amount of time you spend cleaning.

Relatedly: Data Engineers are important and if you don't have one, you'll probably wish you did.

Read 11 tweets

Brendan Kent

@brendankent

23 Mar

Why is sports analytics valuable?

Because humans have mental & situational constraints.

A thread 👇

1/ Sports analytics has been able to provide value beyond traditional qualitative judgments because humans have limits that can be generally classified as:

- Mental constraints
- Situational constraints

2/ Let's start with the mental constraints.

Perhaps the most obvious mental constraint is that our memory is limited.

We don't have the capacity to remember and process everything that happened on every play.

Read 20 tweets

Brendan Kent

@brendankent

11 Feb

Descriptive vs. Predictive

What's the difference between a descriptive metric & a predictive metric? Why does it matter?

A thread 👇

1/ Generally speaking:

- Descriptive metrics are intended to describe what has happened in the past

- Predictive metrics are intended to provide insight into what might happen in the future

2/ Let's look at an example of each type.

A 🏀 player's Free Throw % is a descriptive metric because it quantifies the efficiency with which a player shot free throws in the past.

Read 14 tweets

Brendan Kent

@brendankent

28 Jan

New to sports analytics?

These are the programming languages and tools to learn & the order to prioritize learning them in.

A thread 👇

Priority 1️⃣: Get comfortable with Excel

Most high-level modeling is not done in Excel, however, it’s still important to know your way around a spreadsheet.

If you don’t have access to Excel, Google Sheets (which is free) will do the trick.

Priority 2️⃣: Learn either R or Python

R & Python are the core languages of sports analytics, & most roles will require that you know at least 1 of them.

Don’t worry about learning both at first. They’re similar languages and if you know one, it'll be easy to learn the other.

Read 10 tweets

Brendan Kent

@brendankent

12 Jan

Win Probability, Explained 📈

What is win probability? How does it work? Is it ever "correct"?

If you ever find yourself asking these questions, this thread is for you 🧵

1/ Win probability (abbreviated "WP") is the likelihood that a team will win a particular game, expressed as a percentage.

50% WP: If the game is played 1000 times, the team will win ~500

10% WP: If the game is played 1000 times, the team will win ~100

2/ Importantly, WP is not a statement about whether a team WILL or WON'T win a game. A team having a 30% WP means they're MORE LIKELY TO LOSE THAN WIN but not that THEY'RE GOING TO LOSE.

Read 20 tweets

Brendan Kent

@brendankent

16 Oct 20

Thread: There are a variety of online statistics, computer science, and data science courses that can be audited for free.

Here are a few I'd recommend to those interested in developing a technical skill set for sports analytics ⬇

"Intro to Statistics" from Stanford

Gotta build the foundation!

udacity.com/course/intro-t…

"Introduction to Computer Science & Programming Using Python" from MIT

These days, virtually every job in sports analytics requires some programming experience.

edx.org/course/introdu…

Read 7 tweets

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