Share this page!

Enter URL or ID to Unroll

You can paste full URL like: https://x.com/threadreaderapp/status/1644127596119195649
or just the ID like: 1644127596119195649

How to get URL link on X (Twitter) App

  1. On the Twitter thread, click on or icon on the bottom
  2. Click again on or Share Via icon
  3. Click on Copy Link to Tweet
  4. Paste it above and click "Unroll Thread"!
  5. More info at Twitter Help
Brendan Kent Profile picture
Brendan Kent
@brendankent
data science & business strategy • currently @meta• previously @lyft @DraftKings @TimbersFC @Harvard
Oct 18, 2022 • 47 tweets • 22 min read
Looking for free sports data to kick off your sports analytics project?

Here's my updated list of 40+ free sports data sources, including pre-assembled data sets and R/Python libraries 👇 🏈 Pro Football Reference (@pfref)
pro-football-reference.com
Oct 17, 2022 • 19 tweets • 4 min read
You've probably seen charts like this showing each team's Win Probability over the course of a game.

But how is this calculated? And is it ever "correct"?

A non-technical explanation of one of the foundational sports analytics metrics 👇 Image 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
Oct 15, 2022 • 20 tweets • 4 min read
Analytics is increasingly responsible for coaching and personnel decisions in sports.

Why is it such a valuable tool?

Because humans have mental and situational constraints 👇 Image 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
Oct 14, 2022 • 18 tweets • 5 min read
What is Expected Goals ("xG"), and how is it calculated?

A non-technical explanation of the increasingly mainstream ⚽️ metric 👇 xG is perhaps the most ubiquitous advanced ⚽ metric, but can be applied to structurally similar sports like 🏒 as well.

In this thread, I'll focus on its application to ⚽, but much of what can be said about xG in ⚽ applies to xG in other sports.
Oct 13, 2022 • 16 tweets • 6 min read
Want to get into business, product, or sports analytics?

Here's how to develop your technical skillset for free: There are several key technical skills for analytics, and the importance of each depends a bit on the type of analytics you're interested in (e.g. product analytics vs. sports analytics).
Oct 12, 2022 • 5 tweets • 1 min read
🧵 Underrated analytics skills: 1. Communication

Excellent and/or highly technical work can be lost to poor communication.

The ability to explain work to non-technical stakeholders increases impact significantly.
Apr 26, 2022 • 8 tweets • 3 min read
New to sports analytics?

Last year I 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…
Jun 23, 2021 • 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…
May 10, 2021 • 11 tweets • 1 min read
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.
Mar 23, 2021 • 20 tweets • 3 min read
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
Feb 11, 2021 • 14 tweets • 3 min read
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
Jan 28, 2021 • 10 tweets • 2 min read
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.
Jan 12, 2021 • 20 tweets • 4 min read
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
Oct 16, 2020 • 7 tweets • 2 min read
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…
Oct 14, 2020 • 6 tweets • 2 min read
#BigDataBowl 2021 is here 🏈 There aren't many better ways to get exposure to teams (that are hiring) than to perform well in this. Also, sports analytics is fun.