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

gamestable and 2) to investigate the remaining anomalies in thegamesandscorestables 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.

Kevin Feasel

2018-04-03

Power BI