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.
Back to playing with Power BI. I’m using the data from my Fantasy league team and seeing what insights I can get. 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.
So far I have been focused on showing you the charts that I created, here I am going to build a small project from the start. The goal is to produce visualization that shows me FTHG against FTAG so I can see at a glance which teams will do well.
Starting off – Data
Getting the data right is the basis for a trouble free Power BI experience, as I’m using SQL Server it makes the job easier. You can data wrangle in Power BI but I prefer to do as much as possible before. My data model is made up of a single Fact table and various Dimension tables. The Fact is the results etc. and the Dimensions are the Teams, Season, Leagues etc. For the purpose of this I have not used Fact or Dim as either a Schema or in the naming convention, normally I would 🙂
Note: The Team table I have turned into 2 views as I want to select Home and Away teams. There are other ways of doing this but this is a simple way.
Within Power BI I import the Tables and Views I require, and check the relationships between them. All the lines are solid so I’m happy.
The Dashboard is simply 2 charts and a selector. The selector is for the Leagues. The charts are a stacked bar chart and a quadrant chart. The stacked bar is based on Goal Difference. I created a new measure for this.
The Quadrant is based on FTHG against FTAG.
Now, you can select either of the charts and it will filter the other. Here I selected a specific value on the Stacked bar, Aston Villa -21 and it filtered the Quadrant chart.
If you hover over the Quadrant bubble for Aston Villa you can see FTHG 14 and FTAG 35 which gives you -21
This is a very simple project with just one dashboard, but because of the data even though it is simple we could produce quite a lot of charts.
I have been going through the data and seeing what I could come up with. I have created some new charts based on various pieces of data.
Here is a chart showing the last 6 season worth of data, highlighting average scored and conceded goals. Nothing really shows that we didn’t know.
This chart is based on the promoted sides and how they did in the first six games. This is more interesting as I always said don’t bet on the first 6 games as this is when the promoted teams do better, or perceived to do better as the other teams are getting used to them.
This chart goes into more details and shows how the promoted teams did in the following season. We can see things like;
- Which teams from 3 to 6 actually got promoted.
- Where the promoted teams finished in the following season to being promoted. I also grouped this by where they finished in the promotion season.
- How many promoted teams stayed up.
- Some stats on points.
These are just the first set of charts, I am starting to go through the data to see what information it tells me.
Its been an age since my last post. Saying that I have been busy with work and things and the things have included re-development of the website and backend.
I have also been playing with Power BI a lot more. Here are some of my latest dashboards.
This chart is showing Home and Away goal difference by the current season.
This chart is showing Home and Away goal difference by various season.
This chart is showing home and away wins by teams by season, it also shows the percentage by season and it compares with the previous season to see if there has been more home wins or away wins.
It becomes more interesting when you look over more than 2 seasons.
I will be getting more active on here so keep an eye out for more posts.
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.
There is so much more that this chart will do but I thought I would share it with you.
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.
You can select either all, not the latest or the latest predictions.
Then by selecting a game and hovering over the bubble you can see the stats, Arsenal home win against Watford, currently 0-2 down.
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
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.
If I select the Liverpool away losses on the away form chart, it highlights the teams that won on the home chart.
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.
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.
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
Simple dashboard with filters
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.
Its good to zoom in and see close ups.
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.
This chart shows the shots to goals ratio, just for the Premiership.
Anyway back to it.