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.Β  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 πŸ™‚

 

 

 

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.Β  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 πŸ™‚ 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 Β£9 million to Β£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 Β£5 million to Β£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 πŸ™‚ 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.

 

Working on the Prediction dashbord

A new dashboard has been added, this time it based on real football and it includes Predictions πŸ™‚

This one allows you to select a home team and an away team and you can see stats based on that fixture. In the example I selected ‘Manchester City’ and ‘Newcastle’. Looking at the stats its going to be a home win.

 

It has also been predicted as a home win 67.74%. The predictions are still being tested as I have changed the algorithm a lot this season.

The stats in the tiles are based on historical fixtures between the 2 teams. Currently I’m using all data but I will modify this once I see how relevant all the data is.

If we change the teams to Arsenal/Palace we can see at one point this would have been a guaranteed Home win. But recent form shows a different story. The Prediction is 60.78% for a home win but I think it might be a draw.

if we use the players dashboard we can see it might be closer.

After this weeks games I will be doing some more analysis before I update all the data.

Watch out for another post coming out very soon.

Power BI and Fantasy footy data

The joys of data and hopefully some useful insights πŸ™‚ Here is my latest dashboard on the Fantasy Footy Data.

3 week diff

This charts shows the data when you look at it over a three week period. The purpose of this data is see in form players, but by looking at three weeks it shows more analysis than looking at a single week. It shows the data in the following way:

  • Data by teams
  • Data by players
  • Data by position

It can be filtered by team or cost band.

 

Goals & Assists

These charts are showing goals and assists by players. It is the same format as the last chart. Interesting you can see that only two teams have not ahd a defender score and Chelsea have 11 goals from defenders.

Quadrant

This chart is showing goals and assists per minute played. The size of the bubble is total points. Bottom left quadrant is the one to be in.

This can be filtered by position.

If we zoom in we can see the stats for Mo Salah.

Drilldown

The drill down capabilities shows us the records which makes up the charts, if we right click on Chelsea and DEF we can see who has scored.

 

Drill down records

The records are showing us that Alonso has scored 6.

 

The purpose of these charts is to help me select which players do I bring into my team.

I will be posting once a week from now as we in a position when the data is flowing and we are seeing some good insights.

 

Power BI and fantasy league data

Back to playing with Power BI. I’m using the data from my Fantasy league team and seeing what insights I can get.Β This is the first post of many on this subject. Future posts will have more details in them.

 

The idea is to see the data from a details and KPI perspective.

KPIs are based on what I would my team to score, so Captain points are 15, other players just below 5. I came up with these numbers based on.

  • Team needs to get 60 points
  • Captain needs 15 points
  • 45 points shared between the other 10 players.

In the first set of charts the waterfall chart has drill down capabilities.

Top level, which shows points by week.

First level down shows points by players

Bottom level shows points by bench and first 11. ‘Y’ is bench.

 

I’m hoping this data will help me get higher in the leagues.

Even more Power BI charts.

Another week and another Power BI chart, I’m looking forward to when we get all this good stuff in Azure.

This chart is looking at average points either at home or away for a given month and the selected teams, its all based on historical data since 2010.

average-points-by-team-and-month

There is so much more that this chart will do but I thought I would share it with you.

 

Power BI – Predictions

Still playing with Power BI, producing some charts for the Predictions.

This chart shows all predictions, for the test I have used 2 weeks worth of predictions.

week-22-preditions-pt1

You can select either all, not the latest or the latest predictions.

week-22-preditions-pt2

 

Then by selecting a game and hovering over the bubble you can see the stats, Arsenal home win against Watford, currently 0-2 down.

week-22-preditions-pt3

 

 

 

Power BI – Form of teams

Still playing with Power BI and looking at the data from the Predictor database.

This chart shows home and away form, the selectors on the left are for selecting League and teams

form1

If you click the charts the data will change. I have selected the Liverpool loss at home and the away charts shows you it was Swansea.

form2

 

If I select the Liverpool away losses on the away form chart, it highlights the teams that won on the home chart.

form3

 

If I use the slicers and once again select Liverpool, the home chart shows Liverpool only and the away chart shows the teams and the results.

form4

 

The use of Power BI makes the charts very interactive. As I’m looking at form charts I’m listening to Liverpool who are currently getting beat 1-2 at home to Wolves. January has been a bad month for Liverpool.

Demo charts & Dashboards using Power BI

Here are some charts/Dashboards that I have been playing with in Power BI, they are based on attack against defence, they are in the early stages of development so they will change and become more meaningful.

All teams from 20 leagues

bubble2

Specific only

bubble1

Premiership only

bubble3

 

Simple dashboard

bubble4

Simple dashboard with filters

bubble5

Power BI charts

Time has flown and I’m still not ready to roll out the new website, it is close, just need some time to get it uploaded and then some UAT.In the meantime I have been playing with Power BI, specifically the Globe 3D chart. The idea is to get all the stadium addresses in then plot the data on the globe.

globe-3d

Its good to zoom in and see close ups.

globe-3d-zoom

Then I have the normal charts, this one shows home goals and away goals and also most home goals in a league. I have only included certain leagues as I’m in the testing phase.

footy-data-dec-07

This chart shows the shots to goals ratio, just for the Premiership.

footy-data-shots-to-goals-dec-07

Anyway back to it.