Data Cleansing: Hockey Edition

Stacia Varga has a post covering some of the yeoman’s work of data cleansing:

For now, Power BI continues to my tool of choice for my project. My goals for today’s post are two-fold: 1) finish my work to address missing venues in the games table and 2) to investigate the remaining anomalies in the games and scores tables as I noted in my last post.

To recap, I noted the following data values that warranted further investigation :

  • Total Goals minimum of 0 seems odd – because hockey games do not end in ties. I would expect a minimum of 0 so I need to determine why this number is appearing.

  • Total Goals maximum of 29 seems high – it implies that either one team really smoked the opposing team or that both teams scored highly. I’d like to see what those games look like and validate the accuracy.

  • Record Losses minimum of 0 seems odd also – that means at least one team has never had a losing season?

  • Similarly, Record Wins minimum of 0 means one team has never won?

  • Record OT minimum of 0 – I’m not sure how to interpret. I need to look.

  • Score minimum of 0 seems to imply the same thing as Total Goals minimum of 0, which I have already noted seems odd.

This is the kind of stuff that we talk about as taking 80-95% of a data science team’s time.  It’s all about finding “weird” looking values, investigating those values, and determining whether the input data really was correct or if there was an issue.

Related Posts

Embedding Images In Power BI

Zach Conroe shows how you can embed an image in Power BI: The good news is that there are workarounds to this challenge. We are going to reconstruct the above use case and demonstrate how to pull in images from a local database, and then use custom columns in Power Query to reformat the source […]

Read More

Getting A Specific Rank In DAX

Marco Russo shows us how to get the Nth element in a list using DAX: The complexity of the calculation is in the Nth-Product Name Single and Nth-Product Sales Amount Single measures. These two measures are identical. The only difference is the RETURN statement in the last line, which chooses the return value between the […]

Read More

Categories

April 2018
MTWTFSS
« Mar May »
 1
2345678
9101112131415
16171819202122
23242526272829
30