Transfers and remaining games

Over the last few months I have been creating Fantasy Football data flows using Azure which allows me to automate the data flows. This has enabled me to start looking at what data is important. I’ve been concentrating on the Transfers just lately. So here is a Power BI dashboard which shows almost real time data (Data captured every 15 minutes)

In this dashboard I have the ability to look at various pieces of data:

  • Transfer data. This allows me to see how big a player is expected to be in the coming game week.
  • Goals and assists for those players. I can select the game weeks so I can select what I believe is the right amount of weeks to go back over, 3 to 6 appears to be the ‘good zone’.
  • Remaining games details.

 

The actual Transfer section starts off at the team level then you drill down to players.

I love the capabilities of Power BI to drill down like this, it really gives you some options.

 

 

The whole dashboard becomes the selection options. If I actually select Teams or Positions only the data which meets that criteria will be shown, if I select something on the charts the rest stays there but looks opaque. Here I have selected Harry Kane as he wasn’t high up the charts for scoring/Assists.

The final part allows me to see the fixtures. Now this could be an interesting selection to highlight. Over the years I have observed that the first 6 and last 6 games of the season throws up the biggest surprises. Take this season, West Brom have beaten Man united and drew with Liverpool in the last few weeks, and they are in the relegation zone.  Spurs have West Brom so I suspect that the players who are bringing in Harry Kane expect a big return, but could there be surprise.

I still have a lot of data to blend to get me to where I want to be, but I’m building up some good history, this at least gives me a good place to start.

Having the historical data allows me to change calculations and then apply the new settings to see what happens.

Roll on next season 🙂

 

 

 

Latest dashboards

Been playing again with Power BI, the latest dashboards shows various stats to assist with my player picking:

Points & Average points by teams and positions  over a given time period.

Points & Average points by players over a given time period.

Points & Average points by players outside the top 6 teams  over a given time period.

Goals scored & Assists by teams and positions  over a given time period.

Goals scored & Assists by players  outside the top 6 teams  over a given time period.

If I drill down on the assists to show players you can see Benteke had 4 assists prior to last weeks games, who’d have thought that 🙂

 

Goalkeepers stats.

The purpose of these dashboards is to show what is happening and to give you the ability to ‘drill down’ from teams to players. Need to start looking at the next game week and think about game week 31 as that is a reduced game week in terms of games.

 

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.

 

Home Team dashboard

I’ve spent more time playing on dashboards, it appears the more time I spend the more options I want to include :). I decided to go with a Home Team dashboard.

You pick a League and a season and it gives you the home info.

If you select a Team you get the details

If we look at the games in details we can see that home draws are going to cost Liverpool.

You can look at the data across multiple season as well, here I selected 3 seasons and it gave me this data.

I’ve built quite a few dashboards now, I need to build the away dashboard next then I’m onto the predictive analysis stuff. I will then start to use R/Python to really push the data. The current Predictions stuff is not accurate enough yet so I’m busy building in some other variables.

Its amazing what you can add in and luckily with some of the variables I can retest all the previous results to see what effect it has. Some of the variables I cant use as I only have certain data points but going forward that will grow.

During my time on this side project it has made me realise the importance of collecting data, but not just collecting it but actually using it.

 

 

More analysis on players using Power BI – upto and including week 9

So in my quest to see if I can use stats to improve my Fantasy league team I have come up with a few more dashboards.

Team and Players

The first dashboard shows me the overall points for the teams, then the weekly points for the teams and then the players. Ignore the up/down as that is based on limited data, it will become more important as the weeks go by.

By selecting the team it will filter all the charts so you can see what each player got.

As well as the dashboards based on overall points etc. I wanted to see if I could create a ‘weighting’ which could be applied and then you can see if a player is worth buying, hence the Bargain column. It will be interesting to see the stats after I have uploaded this weeks data.

 

 

I will be updating the data and posting the latest stats later.