data scientist working on @ray_ban x @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 👇
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 👇
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