profile

Learn Data Science from Data School 📊

Tuesday Tip #9: Calculate scoring runs in basketball 🏀

Published about 1 year ago • 2 min read

Hi Reader!

Before we get to today's tip, I have three big launch announcements:

1. My new course, Become a Regex Superhero, launches this Friday! Watch out for more info and a huge launch discount.

2. I recently launched location-based pricing. I'm offering a discount of up to 85% to residents of 160+ countries.

3. My pal Ben Collins just launched a new course, Beginner Apps Script, which lets you extend the functionality of Google Sheets (and other Google Apps). Ben is the best Sheets teacher I know and is offering 50% off during the launch! (FYI, this is an affiliate link, which means that I may earn a commission if you sign up using my link.)


👉 Tip #9: Calculate basketball scoring runs

Are you watching March Madness? If so, hit reply and let me know how your bracket is doing 😂

For those who don't know, March Madness is a US college basketball tournament. One term that you'll hear a lot during games is scoring runs.

For example, a team that's on a "12-point scoring run" has scored 12 points without the other team scoring any points.

So I was wondering: How could we calculate scoring runs using pandas? 🐼

Let's find out!


Example scoring data

Let's pretend this was our scoring data. There's one row for each time a team scored points:

In this case, the largest scoring run was when A scored 9 points in a row.


Identify each scoring run

Now we need to figure out when each scoring run starts!

First, we use the shift() method to shift all of the teams down a row, and store those in a column called previous_team:

Then, we check if team is not equal to previous_team, and store the boolean result in a column called start_of_run:

Do you see how that works?

By checking whether a given team value is equal to the value in the previous row, we now know when each scoring run starts!

Finally, we use the cumsum() method to assign a run_id to each scoring run:

Wait, what just happened?

Any time you do math on a boolean column, True gets treated as 1 and False gets treated as 0. Thus by taking the cumulative sum of the start_of_run column, the run_id increments every time it reaches a True value. (Neat, right?)

Shout out to Josh Devlin's excellent blog post, Calculating Streaks in Pandas, for teaching me this exact approach!


List all scoring runs

Now that each run has been assigned an id, we use a groupby() to show the number of points scored by each team during each run:

That's it! Here's the code from today's tip, in case you want to play around with it.

How else could we analyze this data using pandas? 🤔

Have an idea? Hit reply and let me know! 💡


If you enjoyed this week's tip, please forward it to a friend! Takes only a few seconds, and it really helps me out 🙏

See you next Tuesday!

- Kevin

P.S. Six people predicted the Final Four correctly​

Did someone awesome forward you this email? Sign up here to receive data science tips every week!

Learn Data Science from Data School 📊

Kevin Markham

Join 25,000+ aspiring Data Scientists and receive Python & Data Science tips every Tuesday!

Read more from Learn Data Science from Data School 📊

Hi Reader, I'm really proud of this week's tip because it covers a topic (data leakage) that took me years to fully understand. 🧠 It's one of those times when I feel like I'm truly contributing to the collective wisdom by distilling complex ideas into an approachable format. 💡 You can read the tip below 👇 or on my blog. 🔗 Link of the week Building an AI Coach to Help Tame My Monkey Mind (Eugene Yan) In this short post, Eugene describes his experiences calling an LLM on the phone for coaching:...

about 2 months ago • 4 min read

Hi Reader, Last week, I recorded the FINAL 28 LESSONS 🎉 for my upcoming course, Master Machine Learning with scikit-learn. That's why you didn't hear from me last week! 😅 I edited one of those 28 videos and posted it on YouTube. That video is today's tip, which I'll tell you about below! 👉 Tip #45: How to read the scikit-learn documentation In order to become truly proficient with scikit-learn, you need to be able to read the documentation. In this video lesson, I’ll walk you through the five...

about 2 months ago • 1 min read

Hi Reader, happy Tuesday! My recent tips have been rather lengthy, so I'm going to mix it up with some shorter tips (like today's). Let me know what you think! 💬 🔗 Link of the week A stealth attack came close to compromising the world's computers (The Economist) If you haven't heard about the recent "xz Utils backdoor", it's an absolutely fascinating/terrifying story! In short, a hacker (or team of hackers) spent years gaining the trust of an open-source project by making helpful...

2 months ago • 1 min read
Share this post