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👉 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! 💡
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See you next Tuesday!
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