Hello everyone, I've been part of this community for a while and I wanted to do something to showoff some interesting informations I find particulary relevant.

Since I am doing a small university project, I wanted to analyze a small dataset that would show a network of the various TMM 2v2 teams that play during a small slice of time, to see if there is anything special that stands out.

For this analysis I've picked up the TMM 2v2 tables from https://kazbek.github.io/FAF-Analytics/tmm2v2, specifically for the period of 30 days from 08/05/2022 to 07/06/2022 (Date is in D/M/Y format)

The data that was picked was:

• the player name, which was subsequently trasformed in an ID to anonymize the data
• the TMM 2v2 teams, which makes up the connection between the nodes
• the player TMM 2v2 rating
• the player TMM 2v2 most played faction

For the availability of this data I want to thank the FAF team for allowing me usage of this data and @Kazbek, for the amazing tool that he created.

So, let's start with the first graph

From this first view, the thing that strikes out the most is the structure of this graph, which is called a core-periphery structure, where near the center there is a thightly connected graph and around it a bunch of other small networks.

This already gives and idea of the types of groups present in TMM 2v2, some players only play TMM with another person or they just play it once, while others play with multiple friends or just enter matchmaker alone.

But, let's also see the faction and the rating distribuition

This is the faction graph, where each node is colored with their respective faction, so:

• blue -> UEF
• red -> Cybran
• green -> Aeon
• yellow -> Seraphim

At a quick glance, there isn't an overall most played faction, and most of the distribuition seems equal between all the factions, maybe with both aeon and uef having more picks than cybran and seraphim

Another note is that in the periphery there are teams that play mostly the same factions, being just a small amount of the overall selection.

This is the rating graph, where each node is colored with the respective rating bracket, so:

• rating < 200 -> red
• 200 <= rating < 700 -> yellow
• 700 <= rating < 1200 -> green
• 1200 <= rating < 1700 -> blue
• 1700 <= rating -> grey

As we can see, much of the graph is not uniform, there aren't many areas with same colors around the nodes and rating is not confined to a specific zone only, and the fact that some of the periphery networks do contain high rated nodes, which would confirm the fact that there are teams which do not search divided for a match

Thank you all for reading through, if you have question feel free to ask me anything.

I'm willing to get down for a beating
Raquaza 900

why are you living on such a low resolution?

you should increase the resolution of your monitor in display options

I was wondering if it was possible to make a mod to force a game to end, without killing the coms outright.

If it was possible to do and also to make it an option in lobby (off;5min;10min;15min;30min) it could make for some interesting games

And I guess also have it show up a countdown counter UI element ingame, but I think it would be a little bit more complex

I don't think there is an option to avoid it, but what you can do is select all lower level engies, set them to assist the higher levels and then select the high tier engies and use them.

Also subs do attack while moving

Of course, my bad for not explaining it in detail

So, from 2 tables in https://kazbek.github.io/FAF-Analytics/tmm2v2, the Most active TMM 2v2 teams table and the Most active TMM 2v2 players, I have extracted from each table respectively the Players in Teams table and the Player name, the rating and their most played faction from the players table

From this I have generated for each player an ID, then created 2 files (CSV format) to keep the information of the rating and the most played faction sourced from the players table, so pairs of [PlayerID, Rating] and [PlayerID, MPF]

Then I have generated another file (CSV format) that rappresents an edge list with each line having a pair of values [PlayerID, PlayerID], in this case the PlayerIDs sourced from the Teams table, that explain the connection between players, or to say it differently the team composition.

All of this files are then read by a python file using the networkx package, which is a library to create manipulate and study graphs, with which I loaded the graph from the edge list, and then coloured with the other pairings to get these graphs.

Sure, here they are

factionColor.csv

ratingColor.csv

FAFTMM2v2.csv

Just a small note on the last CSV file, it is not in CSV format, since it's just for each line IDs divided by a single space, so you'll need to rename the extension to .edges, because I used the function networkx.read_edgelist ('filePath', nodetype=int) to import the data correctly for the graph format.

could you post a replay where these bugs occur?

Download and start Supreme Commander:Forged Alliance from Steam, then you'll be able to create a game

it could be that at the settings in SCFA are on fullscreen and it resizes them, and for some reason on FAF the default setting is below the resolution on 1024*768.

Could you post your game.prefs here, might be a problem from there

the game should take the video options for SCFA, so try opening SCFA and changing the video options there

why are you living on such a low resolution?

you should increase the resolution of your monitor in display options

Sure, here they are

factionColor.csv

ratingColor.csv

FAFTMM2v2.csv

Just a small note on the last CSV file, it is not in CSV format, since it's just for each line IDs divided by a single space, so you'll need to rename the extension to .edges, because I used the function networkx.read_edgelist ('filePath', nodetype=int) to import the data correctly for the graph format.

Of course, my bad for not explaining it in detail

So, from 2 tables in https://kazbek.github.io/FAF-Analytics/tmm2v2, the Most active TMM 2v2 teams table and the Most active TMM 2v2 players, I have extracted from each table respectively the Players in Teams table and the Player name, the rating and their most played faction from the players table

From this I have generated for each player an ID, then created 2 files (CSV format) to keep the information of the rating and the most played faction sourced from the players table, so pairs of [PlayerID, Rating] and [PlayerID, MPF]

Then I have generated another file (CSV format) that rappresents an edge list with each line having a pair of values [PlayerID, PlayerID], in this case the PlayerIDs sourced from the Teams table, that explain the connection between players, or to say it differently the team composition.

All of this files are then read by a python file using the networkx package, which is a library to create manipulate and study graphs, with which I loaded the graph from the edge list, and then coloured with the other pairings to get these graphs.

Hello everyone, I've been part of this community for a while and I wanted to do something to showoff some interesting informations I find particulary relevant.

Since I am doing a small university project, I wanted to analyze a small dataset that would show a network of the various TMM 2v2 teams that play during a small slice of time, to see if there is anything special that stands out.

For this analysis I've picked up the TMM 2v2 tables from https://kazbek.github.io/FAF-Analytics/tmm2v2, specifically for the period of 30 days from 08/05/2022 to 07/06/2022 (Date is in D/M/Y format)

The data that was picked was:

• the player name, which was subsequently trasformed in an ID to anonymize the data
• the TMM 2v2 teams, which makes up the connection between the nodes
• the player TMM 2v2 rating
• the player TMM 2v2 most played faction

For the availability of this data I want to thank the FAF team for allowing me usage of this data and @Kazbek, for the amazing tool that he created.

So, let's start with the first graph

From this first view, the thing that strikes out the most is the structure of this graph, which is called a core-periphery structure, where near the center there is a thightly connected graph and around it a bunch of other small networks.

This already gives and idea of the types of groups present in TMM 2v2, some players only play TMM with another person or they just play it once, while others play with multiple friends or just enter matchmaker alone.

But, let's also see the faction and the rating distribuition

This is the faction graph, where each node is colored with their respective faction, so:

• blue -> UEF
• red -> Cybran
• green -> Aeon
• yellow -> Seraphim

At a quick glance, there isn't an overall most played faction, and most of the distribuition seems equal between all the factions, maybe with both aeon and uef having more picks than cybran and seraphim

Another note is that in the periphery there are teams that play mostly the same factions, being just a small amount of the overall selection.

This is the rating graph, where each node is colored with the respective rating bracket, so:

• rating < 200 -> red
• 200 <= rating < 700 -> yellow
• 700 <= rating < 1200 -> green
• 1200 <= rating < 1700 -> blue
• 1700 <= rating -> grey

As we can see, much of the graph is not uniform, there aren't many areas with same colors around the nodes and rating is not confined to a specific zone only, and the fact that some of the periphery networks do contain high rated nodes, which would confirm the fact that there are teams which do not search divided for a match

Thank you all for reading through, if you have question feel free to ask me anything.

It sounds like a good idea to put a "play with ai" section in the tutorials tab, probably it would be a refinement to have a simplified options panel of sorts (with relevant options as type of ai, players, setup, map, etc..) that would then be auto setup ingame so that new players don't get lost in the lobby and in the options there

Yes, the wiki is a bit underutilized and it would require to get a bit of work, possibly changing the learning SupCom page to be about learning vanilla FA and have a "Transitioning to FAF" page since there are a couple of small but core changes to the gameplay, and a lot of other components to talk about that are not even mentioned, however I think the wiki is open to changes just like any other wiki so even doing a bit of work solo to improve it can be quite helpful.

I might get a stab into changing some stuff myself, since it is a useful resource for newer players.