Jumping into Alteryx
If you Missed Part 1, It Can Be Found here.
Now we have all our information, we can start build our workflow and scraping our data.
(To note: I am building this workflow in Alteryx Designer 2025.1 So there may be some very small differences, but it should generally be the same.)
For this example, your workflow will end up looking like this
Now onto the tools
We’ll be going tool by tool I’ll be explaining the configuration you need for each tool and the basic function, but I won’t be going into all the functions and configurations of each tool.
Text Input Tool
This Tool Allows You to manually enter data straight into the it. It’s used for this case, as we only need to enter one value so it’s the most effective choice.
- The first tool you will need is a Text Input Tool which is where you will enter your URL. Simply change the field name to ‘URL’ and enter the ‘https://footballrates.com/lasttenseasons’ into the first row. The configuration will look something like this-
Download Tool
This Tool Can be configured to connect to the web. In this case we will only being using GET configuration (Default). But it can also be used to post or update data.
2. configure the Download Tool to look like this (Remember to right click on your download tool once it’s configured and select ‘cache and run workflow so you don’t accidentally hit the rate limit.) –
ReGex Tool
This tool uses regular expressions to output data from a string field where the values share a common pattern. Like the data in HTML code.
3. You will need to Parse your Fields like we mentioned earlier. This is using the ReGex Tool. Which is when you use Regular expressions which is a notation to parce out a common pattern. this is just 3 tools in a row.
ReGex Tool 1 Parse the table (REGEX <table.*?>(.*?)</table> )
You’ll see that the table you parsed out is ‘Not OK’. This is fine as the value is truncated which is why you see this.
ReGex Tool 2 Tokenize to rows (REGEX <tr>(.*?)</tr> )
Your parsed data will look something like this. Don’t worry about the blank values, This is just because the values will have leading whitespace.(You Should have 106 rows)
ReGex Tool 3 Tokenize to columns (REGEX <t.*?>(.*?)</t.*?> ) (For this example we split to 13 columns and used ‘c’ as our output root name.
Now we can see a full table structure beginning to form.
Dynamic Rename Tool
This tool allows us to rename multiple fields by using values in the first row. It can also be configured with different rename modes but today we just need this one.
4. Now that we have our table, we can start cleaning it up with a dynamic rename. We want to use our first row from our table we just parsed to rename the fields dynamically.
The values from row 1 in your selected columns will have moved up from row 1 into the field names
Regex Tool
This ReGex Tool will just be used for some basic cleaning of one of our fields. You could also use a formula tool if you’re more familiar with that.
5. Next, we’re cleaning up one of our columns (Team). It has some Bold HTML notation on the team names. we will use another Regex Tool with the replace output method. (ReGex (<.*?>) )
The team names values will be cleaned, and your table should look that same as how it was structed on the website.
Select Tool
In this case, we just need to change the data types and remove any columns which we don’t want to output.
6. Almost done, all you need to do is select the columns you want and change data types before outputting the data.
Your Table Should look like this. Clean, structed and ready to output for analysis.
Output Tool
Now all that’s left is to output the data into your preferred type (I just did an excel file) and you’re done.
