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Our third day of Dashboard Week required us to connect to this Kaggle Horse Racing data to make a dashboard to show a story we found.

I took a cursory look at the data and immediately became interested in the mother and father columns and decided to focus my analysis on whether racing statistics are passed down from mare to foal. It was only possible to relate horses to their mothers reliably as there was a father field and a maternal grandfather field which could be used to uniquely identify each horse by using its name and its father’s name.

### In Tableau Prep

I decided to switch things up and do the initial data cleaning in Tableau Prep, including cleaning, renaming, removing, splitting and making some new columns. While it’s nicer to look at, I think I could have done it all more quickly in Alteryx. I only ended up using the horses and races data so I did not output the cleaned forward data.

### In Tableau

I pulled the data into Tableau and began by splitting the mares up into bins according to what % of races they won in their careers, and then created a histogram to see the distribution of horses in each bin. I then created a few exploratory scatterplots to see how the horses in each mare group varied regarding the % of races they won and placed in, and the correlation between average horse ratings and the average ratings of their mothers. I decided to conclude the dashboard on a chart observing the % of races where horses were favourite over time and split them into two groups: horses with mares who won over 50% of the races they were in vs. horses with mares who won 50% of their races and under. This split into two larger groups was so that there was enough data in each group per year to measure reliable trends. Finally, I added a parameter so that the user can change the mare grouping method so that rather than using the % of races each mare won over its career, they are grouped according to the % of races they placed in. I hope you enjoy the result!

Dashboard Week Day 3 – Horse Racing

##### Author: Hunter Iceton

Hunter Iceton is an enthusiastic and positive individual. He graduated from Sydney Uni in 2017 with a Bachelor of Commerce (Liberal Studies) majoring in Finance, Marketing and Quantitative Business Analytics. For the next few years, Hunter spent his time creating and releasing music, while tutoring primary and high school students in Mathematics and Business Studies. Hunter is now excited to be joining The Data School, looking forward to approaching analytics with a creative perspective. In his spare time, Hunter enjoys continuing to create music, reading philosophy and cooking plant-based dishes. Otherwise, he can usually be found at a restaurant, a bar or an art gallery.