Line plot was used for visualization.
In order to draw the end points of Pass, data was added with coordinates slightly shifted from endY.
This process was also necessary to prevent Pass from being connected to each other.
I think there are better ways.
Hi All- here is my solution for this week. Overall, i see some people had similar idea in mind as what i had. I built in a Filter-by-Team functionality and then added time intervals in order to add a bit of granularity… all of the data at once was not really informative. I did use a refresh button (instead of using the re-execution setting of the multiple selection widgets) to allow for changing multiple filter settings at once before the refresh occured… much smoother this way. When both teams are added to the visualization, i colored the markers by home jersey color. The markers are placed at the starting point of the pass attempt. The color indicates successful (green) or failed (red) passing attempt.
This is my take on S2 Challenge 07. I thought at the beginning as a good idea to try it with Py, but it took me more time than expected. Besides I have the feeling that R allows more creativity and more flexible too in combining charts Good lessons learnt and fun anyhow.
Just for the records. I’m still working in the workflow with some upgrades. Pass arrows layer has been added to the dashboard. As an example, here in the picture there’s a sample plot of Manchester City’s long distance passes [>40 pitch-units ] (be aware that units are provided in X and Y percentage, so units need to be transformed over pitch shape).
I’ve just realised fron @HeatherPikairos’ post, that I was using the same FCPython resource for the contour maps. So I could add easily the pass arrows’ map on top.
Well, in addition to the visualisation of Manchester City’s successful passes, I also showed the failed passes, as well as Manchester United’s passes. This is because I believe that the success or failure of one team’s pass is closely related to that of the other. Only the passes are shown here.
Matplotlib is a relatively slow solution.
2D Density Plot is a great visual solution in this scenario.
The sunburst chart is also great.
Obviously there are many factors that influence the success of the pass. In addition to the pass, there are many other types of data, but it is a pity that there is not so much time to analyse them.
alright, here’s my solution. Looking at the submissions so far, it feels a little bit “lame” because I have now soccer field visualized etc
Learned again a lot about re-executing when making changes in this little dashboard and also used some Auto-Binner functionality to bin some “match times” and the Euclidean Distance (aka movements in the 2D space rather than just from A to B)
Never thought I actually will like mathematics again hahaha
I just want to share an updated version of my workflow (in case anyone enjoyed the video!) because I had an issue generating a full video in AVI format and I have now received a solution from @gab1one on the forum:
The NativeQT format in -Image Writer- successfully generates the full video
I have also added a little bit extra at the end of the workflow to compress the video and delete the original version in the data folder:
As always on Tuesdays, here’s our solution to last week’s challenge!
Since this was an easy challenge, we kept the solution very simple and didactic, relying on a heat map that is quick to build with the 2D Density Plot (Plotly) node. As you can see, most of Manchester City’s successful passes are initiated from the center of the field.
Once again your incredible solutions AMAZE us! This year it seems like you folks are really going the extra mile and really exploring how KNIME can be used. We are so happy! This really means a lot to us.