What makes a good soccer team? (according to network theory)

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A huge thank you to the team behind the public dataset. Their paper can be found here: https://www.nature.com/articles/s4159...
The data itself is here:
Pappalardo, Luca; Massucco, Emanuele (2019). Soccer match event dataset. figshare. Collection. https://doi.org/10.6084/m9.figshare.c...
https://figshare.com/collections/Socc...
Also -- yes, I did accidentally misspell Betweenness every single time. No, I didn't notice until like... just now... -_-

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NOTES:

Code: I want to put my code onto github for this but I had a last-minute trip to Germany come up. I will try to post it when I come back. I'll make a community post when I do.

Tips for the dataset:
One thing I didn't find well explained in the paper is tags which are auxillary descriptors for events. Some examples:
1) A 'shot' can be successful, in which case it will get a tag '101', or not (no tag)
2) A pass can be labeled successful (1801) or not (1802), or it might get intercepted, in which case it will receive the tag 1401.
3) Tags can also be stacked. For example a shot with tags 101 and 401 means a successful goal with the left foot.
All the tags can be found here:
https://dataglossary.wyscout.com/

Normalizing Efficiency: Two things: First, I forgot to mention that when calculating the efficiency you should invert the link strengths. The interpretation of efficiency is 'distance' and the assumption that stronger links means the players are 'closer' in some sense. Second, It's not enough to divide by N to control for the number of players in the network efficiency. There is no one way to do this but I followed Latora, V. et al, (2001). "Efficient Behavior of Small-World Networks" -- you want to divide the efficiency by the "idealized" network which is essentially a fully connected network (aka, a clique or complete graph), and each link should be assigned the average strength of the links of the original network. This is also detailed on the wikipedia page for "Efficiency (network science)".

CREDITS:
Subtitles by Carlos Hanon Fernández (Español, Latinoamericano)

MUSIC CREDITS:
Killer Vacation - Chris Doerksen https://open.spotify.com/artist/1xnlf...

Native - HOME
https://open.spotify.com/artist/2exeb...

Above All - HOME
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Intermission - Stux.io and Vaporwavez https://open.spotify.com/artist/5OmqY...

Wehrmut - Godmode (YouTube audio library)

Loading Screen - Dyalla (YouTube audio library)

Mas Cafe - Casa Rosa (YouTube audio library)

Spring Field - Godmode (YouTube audio library)

Event Horizon - World Complete
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https://worldcomplete.bandcamp.com/

RPG Store - Chris Doerksen
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Stairway - Patrick Patrikios (YouTube audio library)