More APIs for Fantasy data.

After a bit of digging I found some useful APIs to get data, so I created some more Python scripts to capture data and now I’m in a position where I’m looking at that data and considering redesigning the entire data model. The reason for this is some of the data I was creating will be more accurate with the new APIs.

The new data allows me to summarise the players data by week. I have made a few more dashboards to show the data so I can see what I can do. Here they are.

This gives me a summary based on the last 6 weeks.  I can see things like minutes played and goals. Here I have selected Midfield.

If I select Hazard from the treemap I get his stats. This will enable me to see how his points are broken down.

And the big question is normally Kane or Aguero for my forward.

Based on the last 6 weeks Sergio wins 🙂 but what happens if we look at the last 3 weeks, well, Firmino comes into the mix

The good thing is we are getting to a point where the data is enabling us to make a decision. With footy, emotions play a major part so having decisions based on data rather than emotions may make a different.

What happens if we want to include price. We can add Price and Price range to the dataset. This will give us the ability to look at players within a certain budget. Having the ability to select the weeks you want to look at will also show how they are progressing or not!

Adding in a price range we can select our budget. I want to look at the Midfield players in the £9 million to £11 million range, as expected the big hitters like Salah and KDB are there. In the last three weeks they have amassed 9 goals and 7 assists.

 

If I select KDB I can see that he has scored once and has 4 assists. The bottom right hand chart shows me he has had a price increase as well

But if my budget was less, in the £5 million to £7 million range it would look like this

The top two being Prowse and Ramsey, so lets look at them. 1 goal 2 assists for Prowse

3 goals for Ramsey in a single game, and his game time is less.

So in this situation I would probably pick Prowse, and he is cheaper.

In summary leveraging Power BI to visualise the data will hopefully give me more of a competitive edge going forward 🙂 Remember spend time on getting the data right then the Power BI part is easier

Next I need to use Python to provide some stats prior to displaying in Power BI.