me 902

Set up a CPU tournament to look through some results based on latest ME..

Level 5 and level 6 teams

The league standings:

1st: Pacy 433 6*

2nd: Pacy 433 5*

3rd: Pacy, low stamina 433 6*

4th: Towering forward 451 6*

5th: Pacy, low stamina 433 5*

6th: Lacking strength, pacy 451 6*

Pace wins matches. Always has. Valid for FML ME 902 / FM 2011..@
If I were to choose, I'd most likely use a 433/451 variant..

Then there were none..

Dunno. Just thought that title sounded cool..

Nothing of interest to be found here. Might pick up FML again when 1.7 hits the high street.

In the meanwhile I've spent time watching some of those Starcraft II videos on youtube. I can't help it, but I just have to watch those fights. It's more fun than playing the game itself really.

Now imagine if we got some commentators uploading FML games to youtube with some 200k views for each game.. That would be cool. Don't think it would happen until at least 2.0 is out though. ;)

Stay tuned. Hopefully I'll give you some more numbers insight when I start playing FML again.

Other than that, not too much to report from rainy Norway. I'm in a new job currently assigned as a consultant for one of the top TV distributors in the country. No numbers yet, more working on the strategic level, but soon enough I'll find my way to their CRM database and spit out some brilliant advice based on a bit of number crunching! :)

Zimbabwe Time?

The FML economy is basically out of hands. Inflation rates did sky rocket. No cure was delivered before reset, though people geniunely believed that the economy would be tight in the start. Perhaps it was, but the only visible end result was that the most active teams would take the largest part of the money cake when inflation set.

Anyway. I think the economic situation in the GWs is the main reason for me not playing the game now. So I introduce a cure for the disease. Let SI move the servers to Zimbabwe, slice a 0 from everyones bank account, and move the servers back.

At the same time money flowing into the gameworld should be tightened and controlled. Add some transfer taxes, introduce some floating taxes. I mean, how hard can it be. Money in = Money out. It's not like our teams take up subprime loans or that SI Bank is Icelandic.

But back on topic. Moving the comma one step to the left probably favour teams who invest in assets (players). Well. So what? We need a solution, and we need it now. I'm quite sure the richest teams have a bank account of some 30m+ already. Well. I'll tell you a secret. They can live happy on their interest income the rest of their FML career. Is that fair to the newcomers? I think not.

I've probably reached the point where you say that I should get my education money back, because I'm introducing new problems. But at least I try to fix something broken. And it needs fixing. NOW.

Thanks.

Andreas F
MSc in Industrial Economics

The ultimate recipe..

Since I've basically stopped playing the game, I'll spit out all my findings through the last year for any of you number geeks out there.

The higher the number, the more important the attribute is for the given position. Con means Team Goals Conceded, Sco means Team Goals Scored.

Now it doesn't take much to see that Pace/Jumping is way too overpowered for strikers/defenders, and that high Jumping sky rocket any ratings.

Some simple number crunching from SI's part could easily have identified these issues long before we saw some 300 posts about this stuff on the official forums. I hope they take the time to balance the ME a bit more in the future. Heck, I'll even crunch these numbers for free for them if they send the proper data in my direction.

Apologies for bad formatting. 

 +------+------+------+------+------+------+------+
| att  | pos  | Con  | Sco  | AvR  | GpG  | ApG  |
+------+------+------+------+------+------+------+
| Acc  |      | NULL | NULL | NULL | NULL | 1.3  |
| Agg  |      | NULL | NULL | NULL | NULL | -0.2 |
| Agi  |      | NULL | NULL | NULL | NULL | -0.8 |
| Ant  |      | NULL | NULL | NULL | NULL | -1.6 |
| Bal  |      | NULL | NULL | NULL | NULL | 1.6  |
| Bra  |      | NULL | NULL | NULL | NULL | -1.7 |
| Cnt  |      | NULL | NULL | NULL | NULL | -2.8 |
| Com  |      | NULL | NULL | NULL | NULL | 0.1  |
| Cre  |      | NULL | NULL | NULL | NULL | 0.9  |
| Cro  |      | NULL | NULL | NULL | NULL | -2.7 |
| Det  |      | NULL | NULL | NULL | NULL | 1.5  |
| Dri  |      | NULL | NULL | NULL | NULL | 3.6  |
| Dsc  |      | NULL | NULL | NULL | NULL | 1.3  |
| Fin  |      | NULL | NULL | NULL | NULL | 4.1  |
| Fir  |      | NULL | NULL | NULL | NULL | 2.7  |
| Fla  |      | NULL | NULL | NULL | NULL | 3.1  |
| Hea  |      | NULL | NULL | NULL | NULL | -0.8 |
| Jum  |      | NULL | NULL | NULL | NULL | 1.1  |
| Lsh  |      | NULL | NULL | NULL | NULL | -0.9 |
| Nat  |      | NULL | NULL | NULL | NULL | 1.1  |
| Off  |      | NULL | NULL | NULL | NULL | 4.8  |
| Pac  |      | NULL | NULL | NULL | NULL | 2.5  |
| Pas  |      | NULL | NULL | NULL | NULL | 1.0  |
| Pos  |      | NULL | NULL | NULL | NULL | -2.5 |
| Sta  |      | NULL | NULL | NULL | NULL | 3.4  |
| Str  |      | NULL | NULL | NULL | NULL | -0.4 |
| Tea  |      | NULL | NULL | NULL | NULL | -2.4 |
| Tec  |      | NULL | NULL | NULL | NULL | 0.8  |
| Wor  |      | NULL | NULL | NULL | NULL | -2.0 |
| agg  | AM   | NULL | 1.0  | NULL | NULL | NULL |
| agi  | AM   | NULL | 0.6  | NULL | NULL | NULL |
| ant  | AM   | NULL | 0.8  | NULL | NULL | NULL |
| bal  | AM   | NULL | 0.9  | NULL | NULL | NULL |
| cmp  | AM   | NULL | 0.2  | NULL | NULL | NULL |
| cnt  | AM   | NULL | 1.3  | NULL | NULL | NULL |
| cre  | AM   | NULL | 0.1  | NULL | NULL | NULL |
| cro  | AM   | NULL | 0.3  | NULL | NULL | NULL |
| det  | AM   | NULL | 0.2  | NULL | NULL | NULL |
| dri  | AM   | NULL | 0.4  | NULL | NULL | NULL |
| dsc  | AM   | NULL | 0.6  | NULL | NULL | NULL |
| fin  | AM   | NULL | 0.6  | NULL | NULL | NULL |
| fir  | AM   | NULL | -0.2 | NULL | NULL | NULL |
| hea  | AM   | NULL | 1.3  | NULL | NULL | NULL |
| inf  | AM   | NULL | -0.4 | NULL | NULL | NULL |
| jum  | AM   | NULL | 0.5  | NULL | NULL | NULL |
| lsh  | AM   | NULL | -0.9 | NULL | NULL | NULL |
| off  | AM   | NULL | -1.4 | NULL | NULL | NULL |
| pac  | AM   | NULL | 1.0  | NULL | NULL | NULL |
| pas  | AM   | NULL | 0.2  | NULL | NULL | NULL |
| pos  | AM   | NULL | 1.3  | NULL | NULL | NULL |
| sta  | AM   | NULL | 0.8  | NULL | NULL | NULL |
| str  | AM   | NULL | 1.8  | NULL | NULL | NULL |
| tec  | AM   | NULL | 0.8  | NULL | NULL | NULL |
| wor  | AM   | NULL | 0.6  | NULL | NULL | NULL |
| acc  | DC   | 1.2  | NULL | 7.0  | NULL | NULL |
| agg  | DC   | -0.0 | NULL | 4.7  | NULL | NULL |
| agi  | DC   | 1.9  | NULL | 2.8  | NULL | NULL |
| ant  | DC   | 2.0  | NULL | 0.8  | NULL | NULL |
| bal  | DC   | -0.2 | NULL | 0.2  | NULL | NULL |
| bra  | DC   | -0.6 | NULL | 4.1  | NULL | NULL |
| cmp  | DC   | 0.8  | NULL | 0.5  | NULL | NULL |
| cnt  | DC   | 1.7  | NULL | 4.3  | NULL | NULL |
| det  | DC   | 0.7  | NULL | 2.6  | NULL | NULL |
| dri  | DC   | -0.3 | NULL | -0.6 | NULL | NULL |
| dsc  | DC   | 0.5  | NULL | 3.1  | NULL | NULL |
| fin  | DC   | -0.0 | NULL | 0.2  | NULL | NULL |
| fir  | DC   | -1.0 | NULL | -0.5 | NULL | NULL |
| fla  | DC   | -0.2 | NULL | 6.1  | NULL | NULL |
| hea  | DC   | -2.8 | NULL | NULL | NULL | NULL |
| inf  | DC   | -0.5 | NULL | 3.9  | NULL | NULL |
| jum  | DC   | 3.1  | NULL | 22.2 | NULL | NULL |
| mar  | DC   | 0.4  | NULL | 6.3  | NULL | NULL |
| nat  | DC   | -0.8 | NULL | 3.3  | NULL | NULL |
| off  | DC   | -0.9 | NULL | -1.9 | NULL | NULL |
| pac  | DC   | 2.5  | NULL | 8.2  | NULL | NULL |
| pas  | DC   | 1.1  | NULL | 1.2  | NULL | NULL |
| pos  | DC   | 0.2  | NULL | 4.1  | NULL | NULL |
| sta  | DC   | 0.9  | NULL | -1.6 | NULL | NULL |
| str  | DC   | 0.2  | NULL | 3.6  | NULL | NULL |
| tac  | DC   | -0.1 | NULL | 7.1  | NULL | NULL |
| tea  | DC   | 0.2  | NULL | 3.9  | NULL | NULL |
| wor  | DC   | 0.5  | NULL | 6.0  | NULL | NULL |
| acc  | DL   | 1.4  | 1.2  | 5.5  | NULL | NULL |
| agg  | DL   | 0.1  | -0.2 | 4.4  | NULL | NULL |
| agi  | DL   | 0.8  | 0.6  | 3.6  | NULL | NULL |
| ant  | DL   | 0.7  | 0.7  | 1.2  | NULL | NULL |
| bal  | DL   | -0.2 | -0.0 | 0.1  | NULL | NULL |
| bra  | DL   | -0.9 | -1.0 | 3.8  | NULL | NULL |
| cmp  | DL   | 1.3  | -0.6 | 2.6  | NULL | NULL |
| cnt  | DL   | 2.0  | 2.6  | 3.3  | NULL | NULL |
| cre  | DL   | 0.6  | 1.0  | 1.3  | NULL | NULL |
| cro  | DL   | 0.3  | 0.7  | 3.0  | NULL | NULL |
| det  | DL   | 1.3  | 0.8  | 3.5  | NULL | NULL |
| dri  | DL   | 1.4  | 0.5  | -0.4 | NULL | NULL |
| dsc  | DL   | 0.9  | -0.4 | 2.5  | NULL | NULL |
| fla  | DL   | -0.3 | 2.4  | 5.7  | NULL | NULL |
| inf  | DL   | -1.2 | NULL | 3.7  | NULL | NULL |
| jum  | DL   | 2.7  | 3.0  | 16.8 | NULL | NULL |
| mar  | DL   | -0.8 | -0.0 | 4.3  | NULL | NULL |
| off  | DL   | -1.1 | -0.8 | -3.1 | NULL | NULL |
| pac  | DL   | 2.8  | 1.3  | 8.8  | NULL | NULL |
| pas  | DL   | -0.5 | 0.5  | 2.4  | NULL | NULL |
| pos  | DL   | 0.6  | -0.7 | 3.3  | NULL | NULL |
| sta  | DL   | 2.0  | 2.4  | -0.5 | NULL | NULL |
| str  | DL   | -0.3 | -1.6 | 2.2  | NULL | NULL |
| tac  | DL   | 0.7  | 1.4  | 7.8  | NULL | NULL |
| tea  | DL   | 0.7  | 1.6  | 6.5  | NULL | NULL |
| tec  | DL   | 0.9  | 0.3  | 0.9  | NULL | NULL |
| wor  | DL   | -0.5 | -0.3 | 5.7  | NULL | NULL |
| acc  | DM   | 1.5  | NULL | NULL | NULL | NULL |
| agg  | DM   | 1.3  | NULL | NULL | NULL | NULL |
| agi  | DM   | 0.4  | NULL | NULL | NULL | NULL |
| ant  | DM   | 1.4  | NULL | NULL | NULL | NULL |
| bal  | DM   | 1.5  | NULL | NULL | NULL | NULL |
| cmp  | DM   | 0.8  | NULL | NULL | NULL | NULL |
| cnt  | DM   | 1.2  | NULL | NULL | NULL | NULL |
| cre  | DM   | 0.8  | NULL | NULL | NULL | NULL |
| cro  | DM   | -0.7 | NULL | NULL | NULL | NULL |
| det  | DM   | 3.5  | NULL | NULL | NULL | NULL |
| dri  | DM   | -0.5 | NULL | NULL | NULL | NULL |
| dsc  | DM   | -0.5 | NULL | NULL | NULL | NULL |
| fla  | DM   | 1.2  | NULL | NULL | NULL | NULL |
| inf  | DM   | 0.7  | NULL | NULL | NULL | NULL |
| jum  | DM   | 3.1  | NULL | NULL | NULL | NULL |
| mar  | DM   | -0.1 | NULL | NULL | NULL | NULL |
| off  | DM   | -0.8 | NULL | NULL | NULL | NULL |
| pac  | DM   | 1.6  | NULL | NULL | NULL | NULL |
| pas  | DM   | 1.2  | NULL | NULL | NULL | NULL |
| pos  | DM   | 0.2  | NULL | NULL | NULL | NULL |
| sta  | DM   | 0.9  | NULL | NULL | NULL | NULL |
| str  | DM   | -1.4 | NULL | NULL | NULL | NULL |
| tac  | DM   | 0.3  | NULL | NULL | NULL | NULL |
| tea  | DM   | 0.6  | NULL | NULL | NULL | NULL |
| tec  | DM   | -1.2 | NULL | NULL | NULL | NULL |
| wor  | DM   | 0.6  | NULL | NULL | NULL | NULL |
| acc  | GK   | 3.9  | -1.1 | 5.3  | NULL | NULL |
| aer  | GK   | 1.8  | 0.5  | 9.9  | NULL | NULL |
| agg  | GK   | -0.4 | NULL | 1.1  | NULL | NULL |
| agi  | GK   | 0.9  | -1.0 | 1.3  | NULL | NULL |
| ant  | GK   | 0.7  | 0.3  | -4.2 | NULL | NULL |
| bal  | GK   | -0.1 | NULL | 1.5  | NULL | NULL |
| bra  | GK   | -0.8 | 1.3  | 9.7  | NULL | NULL |
| cmd  | GK   | 1.5  | -0.1 | 0.9  | NULL | NULL |
| cmp  | GK   | 1.1  | -0.1 | 2.5  | NULL | NULL |
| cnt  | gk   | 1.8  | 1.0  | 1.9  | NULL | NULL |
| com  | GK   | -0.7 | 0.5  | 0.7  | NULL | NULL |
| cre  | gk   | -0.6 | 0.8  | 6.2  | NULL | NULL |
| det  | GK   | 0.4  | 0.8  | 6.0  | NULL | NULL |
| dsc  | GK   | 0.5  | -0.6 | 3.7  | NULL | NULL |
| ecc  | GK   | 0.4  | 0.3  | 1.8  | NULL | NULL |
| han  | GK   | -0.4 | 1.9  | 5.5  | NULL | NULL |
| inf  | GK   | -0.1 | 0.6  | 3.4  | NULL | NULL |
| jum  | GK   | 0.1  | -1.1 | 2.9  | NULL | NULL |
| kic  | GK   | -1.2 | -0.9 | 2.7  | NULL | NULL |
| off  | gk   | -2.2 | -0.0 | 2.6  | NULL | NULL |
| one  | GK   | 1.0  | -0.8 | 1.7  | NULL | NULL |
| pac  | gk   | -1.8 | 2.0  | 1.2  | NULL | NULL |
| pos  | GK   | -0.0 | 0.4  | 3.7  | NULL | NULL |
| pun  | GK   | -1.0 | -2.3 | 4.0  | NULL | NULL |
| ref  | GK   | 3.0  | 4.7  | 4.7  | NULL | NULL |
| rus  | GK   | 0.4  | -0.3 | 7.1  | NULL | NULL |
| sta  | GK   | 1.5  | 1.5  | 1.4  | NULL | NULL |
| str  | GK   | -0.2 | -0.4 | 2.3  | NULL | NULL |
| tea  | GK   | 2.1  | NULL | 2.2  | NULL | NULL |
| thr  | GK   | -0.9 | NULL | 0.6  | NULL | NULL |
| wor  | GK   | 0.9  | NULL | -8.7 | NULL | NULL |
| acc  | M    | 2.6  | 1.9  | 4.3  | NULL | NULL |
| agi  | M    | -1.2 | -0.1 | 0.3  | NULL | NULL |
| ant  | M    | -1.6 | -0.1 | -5.2 | NULL | NULL |
| bal  | M    | -0.5 | 2.2  | 4.8  | NULL | NULL |
| bra  | M    | 0.7  | 0.1  | -0.3 | NULL | NULL |
| cmp  | M    | -0.2 | 1.8  | 2.5  | NULL | NULL |
| cnt  | M    | 2.5  | 1.4  | -1.5 | NULL | NULL |
| cre  | M    | 0.2  | 0.2  | 0.7  | NULL | NULL |
| cro  | M    | -1.5 | -0.5 | 4.8  | NULL | NULL |
| det  | M    | -0.2 | 0.6  | 6.1  | NULL | NULL |
| dri  | M    | 1.2  | 0.6  | -0.6 | NULL | NULL |
| dsc  | M    | -0.4 | -1.8 | 0.2  | NULL | NULL |
| fin  | M    | 0.7  | 0.4  | 0.6  | NULL | NULL |
| fla  | M    | 0.4  | -2.8 | 15.7 | NULL | NULL |
| hea  | M    | 3.0  | 2.1  | 5.7  | NULL | NULL |
| inf  | M    | -1.8 | 0.4  | 2.0  | NULL | NULL |
| jum  | M    | 1.6  | 0.6  | 8.3  | NULL | NULL |
| off  | M    | 0.3  | 2.3  | -0.2 | NULL | NULL |
| pac  | M    | 3.7  | 2.5  | 9.1  | NULL | NULL |
| pas  | M    | 0.9  | 2.2  | 1.7  | NULL | NULL |
| pos  | M    | 1.0  | 0.9  | 0.6  | NULL | NULL |
| sta  | M    | 0.4  | 3.1  | -0.6 | NULL | NULL |
| str  | M    | 0.6  | -0.6 | 0.5  | NULL | NULL |
| tea  | M    | 1.8  | -0.0 | 10.6 | NULL | NULL |
| tec  | M    | 0.5  | -0.8 | 5.4  | NULL | NULL |
| wor  | M    | 0.1  | -1.1 | 6.8  | NULL | NULL |
| acc  | S    | NULL | 1.3  | 8.2  | 0.9  | NULL |
| agg  | S    | NULL | 0.5  | 3.6  | -2.1 | NULL |
| agi  | S    | NULL | 0.2  | 1.5  | -0.3 | NULL |
| ant  | S    | NULL | -0.3 | -0.8 | 0.5  | NULL |
| bal  | S    | NULL | 1.6  | 4.9  | 2.4  | NULL |
| Bra  | S    | NULL | -0.1 | 1.7  | -0.2 | NULL |
| cmp  | S    | NULL | 0.8  | 4.3  | -0.4 | NULL |
| cnt  | S    | NULL | 1.4  | -1.0 | 2.1  | NULL |
| cre  | S    | NULL | 0.9  | 0.8  | -0.0 | NULL |
| cro  | S    | NULL | -0.1 | 2.6  | -0.2 | NULL |
| det  | S    | NULL | 1.4  | 3.2  | 1.1  | NULL |
| dri  | S    | NULL | 1.5  | 1.0  | 0.6  | NULL |
| dsc  | S    | NULL | -0.1 | -0.1 | 0.4  | NULL |
| fin  | S    | NULL | -0.2 | 5.0  | 0.2  | NULL |
| fir  | S    | NULL | -2.1 | 4.1  | -1.5 | NULL |
| Fla  | S    | NULL | -3.3 | 11.9 | -1.7 | NULL |
| hea  | S    | NULL | 0.4  | 2.3  | 0.4  | NULL |
| inf  | S    | NULL | 0.7  | 1.6  | -0.4 | NULL |
| jum  | S    | NULL | 1.9  | 15.4 | 0.9  | NULL |
| lsh  | S    | NULL | 0.5  | 0.4  | 0.6  | NULL |
| off  | S    | NULL | 0.1  | 3.2  | -0.3 | NULL |
| pac  | S    | NULL | 3.8  | 11.6 | 2.7  | NULL |
| pas  | S    | NULL | -0.3 | 1.1  | -0.3 | NULL |
| pos  | S    | NULL | 0.8  | -2.8 | 0.3  | NULL |
| sta  | S    | NULL | 1.8  | 1.4  | 2.6  | NULL |
| str  | S    | NULL | -0.2 | 0.9  | 0.5  | NULL |
| Tac  | S    | NULL | NULL | NULL | -1.0 | NULL |
| Tea  | S    | NULL | -0.3 | 2.7  | -1.2 | NULL |
| tec  | S    | NULL | 0.8  | 2.4  | -0.1 | NULL |
| wor  | S    | NULL | -0.1 | 6.6  | -0.5 | NULL |
+------+------+------+------+------+------+------+

Still alive..

Still alive. Well. Sort of.

I just cancelled two of my three active subscriptions today. And the reasons? Same as I've been complaining about the last couple of years. Give me something fun to do in the game.

One subscription should be enough anyway. I guess many people take on several subscriptions just because managing one team simply gets boring in the long run as things are now. Maybe the PvE post in the forums can bring some playability back to the game if it is implemented.

I'm really not that concerned about the faults of the ME as many other bloggers/forum posters. I'll just adapt to whatever it takes and play the game for the current ME. After all, this is a numbers game, so if pace 20 is what I need, then pace 20 is what I'll buy. If 433 is the winning formation, then 433 is what I'll play. But I'm really disappointed by the lack of clarity shown by the developers when forum users raise genuine concerns. There are countless examples. My biggest gripes being lack of transfer taxes, uncontrolled flow of money in and out of the Gameworlds, way to much money floating up to the top (trading) teams and generally the fact that the game is quite linear and uninspiring most of the time.

I don't know what it takes to truly fix the game, I'm not a games developer. But I know what it takes to fix the game for me. I know why I was addicted to CM in it's early days. Make it interesting. Make it something I want to play. Make it an alternative to watching movies. Make it into a talking point with my friends. Make it keep me constantly checking my email to see if anything interesting has happened in the game. And ultimately. Make me play FML hour after hour. Just one more match. Just one more win. Just five more minutes. Until I force myself to go to bed only to be totally excited about getting up in the morning to check for any changes that might have occurred while I was asleep.

BZZZZZZZZZZZZZZZZZZZZZZZZZ!?!

So 6 hours a day with vuvuzela isn't enough to annoy the hell out of your girlfriend?

Well here is the solution: Bring the vuvuzela to FML!

1. Install match sounds from add ons in FML
2a (Mac). Navigate to ~/Library/Preferences/Sports Interactive/Football Manager Live/fml_live_v1/add_ons/match_sounds/
2b (Win). Navigate to %appdata%/Sports Interactive/Football Manager Live/fml_live_v1/add_ons/match_sounds/
3. Backup/rename FM_loop2.wav
4. Download and replace with this file FM_loop2.wav
5. Restart FML & wave goodbye to your girlfriend when she moves out so you get more time to play FML! :)

Either footed strikers

Numbers pulled from GW Vald, 2001 observations:

An either footed player hits target 1.8% more often than a one foot/one foot only player (41.8%)
An either footed player scores 1.6% more often than a one foot/one foot only player when the shot hits target (31.3%).
An either footed player scores 1.4% more often than a one foot/one foot only player when he shoots at goal (13.2%).

Some numbers on two-footed Passing

Following my last blog, and the heavy, but interesting FM thread about player attributes linked by T-Bag, I decided to take a look at impact of two-footedness in the current GWs.

This data is taken from 2000 players in Vald.
The means of the passing attribute (passing between 8 and 17):
One Foot Only: 12.5
One Foot: 12.7
Either Foot: 12.2

Completed passes:
One Foot Only: 70.0%
One Foot: 71.7%
Either Foot: 71.5%

Even though Either Footed players have 0.3 points less in passing they complete 1.5% more of their passes compared to One Foot Only.

An interesting observation is that it the difference between One Foot Only and One Foot seems to be bigger than the difference between One Foot and Either Foot.

But let's compare apples and apples. As described in the thread from the FM forums, attributes will most likely be lower when a player is two-footed, and that is also why the mean from my test sample is lower for two-footed players.  So how does a player with One Foot compare to a player with Either Foot when they have the same attribute points for passing?

I'd say One Foot has about 1.5 % higher percentage of passes completed than One Foot Only when the passing attribute is equal. And Either Foot has around 0.5% higher than One Foot.

So how much do 2% translate to in terms of the Passing attribute? Well, that is the interesting thing. It seems like there is a clear threshold when reaching Passing 15 and Decisions 14, independent of footedness.

Below Passing 12 the passes completed rise very slowly, then there is a big jump of 5% up to Passing 15 before it again reaches a plateau. The same is true for Decisions, from 12 to 14 the passes completed jump up by 4%.

So to summarize, I started investigating two-footedness under the assumption that it impacts passing a lot. I've found that it improves around 2% of passes completed, but that getting a player who is not One Footed Only is probably a better bet. I've also found that having a total sum of Passing + Decisions above 28 is very important for completing passes, more so than how many feet they use...

So my next midfielder will probably be a one-footed player competent with his other foot, with Passing 15 and Decicions 14!

Sorry if this post didn't make too much sense, I just wrote down stuff as I found them so it might have ended up a bit unorganised.. :)

Wanted: Low Attributes!

Somewhere online I found a data file consisting of 290 000 FM players.
Well, what is more natural than to deep dive into the numbers to try to find something interesting?

For all you youth managers out there, here are the attributes you don't want a high score on if you plan to let your guys develop to the next wonderkids of the world. The higher these attributes are, the less of the CA points can be spent on other attributes.

1: Natural Fitness
From what I've found, Natural Fitness in FML doesn't really help much with anything except probably postponing the player aging a bit.  However, it eats up a decent amount of points that could otherwise go to more vital attributes. Next time you get a new graduate, you should pray to the FML-lord that he gives you a 5* potential player with Natural Fitness 1.

2: Two-footedness
I'll admit I haven't checked the impact of two-footedness in the ME yet (something for an upcoming post I guess). But what I can tell you is that two-footedness takes points from other attributes at the same rate as Natural Fitness.

3: Flair
As I've mentioned before, Flair does not help a player's team score more goals, neither will it help the team concede less. Flair does improve the average rating, but so does high jumping, and I know which one I would prefer. ;)

4, 5 and 6: Aggression, Workrate & Teamwork
The usefulness of these attributes depends a bit on the position your players play on the field. But for an attacking player, I'm quite certain I could do without these attributes and rather see the physical attributes sky rocket to fulfill the Potential Ability available.

On the other end of the scale, here are the main attributes that impact the CA in a positive way.
For strikers:
Pace, Off the Ball, Decisions, Crossing, Dribbling, First Touch, Heading, Acceleration, Anticipation

And for DCs:
Tackling, Marking, Positioning, Decisions, Heading, Anticipation, Jumping, First Touch, Passing

I'm sure someone somewhere must have blogged/forum posted the same results within the FM community. If you know where to find more information, feel free to post some links down in the comment field! :)

FML Myths Uncovered #1: Jumping is the only attribute needed to win headers

I'll start a new weekly column. FML Myths. Every week I'll try to verify or bust forum posts that come out with blunt statements regarding stats and attributes in FML. This is the first post in the new FML Myths Uncovered series!

Btw, the reason for this post is that I'm on one of my regular boat trips from Stavanger to Bergen, and I need to kill some time!

So what influences headers won? Several forum posts go a long way indicating that jumping is the only attribute needed to win a header.

Well, let the numbers answer the question.

Myth:
Jumping is the only attribute needed to win headers. Forum post link to one of the threads claiming this.

Data set:
1932 random players in Voller (sorted by apps, excluding GKs)

Attributes looked into:
acc agg agi ant cnt bal cre det dri hea jum off pac pas pos sta wor str fla mar tac

Result:
Jumping is the most important attribute for winning headers by a landslide, over 5 times as important as the next attribute. The importance of attributes for headers won in ranked order: Jumping - Heading - Strength - Anticipation - Marking

Conclusion:
FML Myth confirmed!
Jumping is the only attribute needed to win headers!

Youth attributes

I'm sort of switching jobs and stuff so haven't been too active on the blog lately. Michael over in Miller wanted some details about youth attributes, so I did some analysis and thought I'd write up my findings on how kids' attributes compare to grown ups'.

What I did was to filter all 15-17 yos in Voller and compare them to 28-29 yos.

Now, I've just thrown all positions (minus GKs) under the same bus here, but hopefully the data sets are comparable with each other. The reason for this is that I needed a minimum sample of 1000 players, and couldn't get that without either increasing the age range or including all positions. The three individual regression results are using Average Rating, Team Goals Scored and Team Goals Conceded.

Key findings:
Work Rate stands out as the most influental attribute for youth. The higher the work rate, the higher the chances of getting improved match ratings and scoring more team goals. The reason behind this is not up to me to answer, but perhaps there is a stronger correlation between high teamwork and CA?
For the grown ups, Work Rate is not that important, although it is still scores as a top 5 attribute for impacting Average Rating.

Flair, as we already know is important for increasing Average Rating for both kids and grown ups.

Some other points to note is that Acceleration and Pace are less important for youth, and that Heading is more important. From previous research, this could indicate that the youth data set might have included a higher number of midfielders, but since I never displayed playing position when mining the data, that is anyone's guess! :)

Anticipation, Creativity, Concentration and Off The Ball also look to be less important for youth in terms of Average Rating, whilst Balance and Passing is more important for the youth players compared to the 28/29 yos.

Overall, the mean Average Rating for the senior data sample was 6.87, the mean Average Rating for the youth data sample was 6.79, so it shows that age is still golden, if only by a small amount! :)

Again, I wouldn't read too much into this data. It all gets a bit fuzzy when all playing positions are merged together in one big group.

Here are the means for grown ups and youth respectively.

Blog Summary: May

During the last weeks I've looked into lots and lots of FML numbers.

Here is a short summary of what I've blogged about. My Voller test team (based on a weighted list of attributes) is currently ranked 15th in the GW. I'm quite happy with that given the fact that I started off as a lower league team from season 1 and also considering all the great managers that are playing in the GW.

The weighted team approach
This is where I described what I did to identify which players to buy.

GK attributes
The complete list I identified for important attributes for GKs. Of course, I have the same list for all the other positions, but I'm not going to hand them out to you. I still think there should be some mystery involved in attributes, and my weighting can only be used as guidelines anyway.

I've also written up a post on flair and one on the most important stats for increasing average rating for midfielders. It has changed my tactical set up to let all my players play with run with ball and creative freedom maxed out!

Further on - the comparison of height vs jumping, Anthony helped me out and made a great graph of the same stats that you can find here.

Feel free to use the comment field if you have anything specific you want me to look into for future posts. Or catch me in one of the GWs I play in: Voller, Vald or Sanchez!

And if you are a fan of the blog remember to add the RSS feed, or add me to your bloglist if you want to read new posts as soon as they are written!

Stamina

I've always assumed that stamina is a measurement for the amount of time a player lasts on the pitch before their condition drops to an unacceptable level.

Could this be totally wrong?

The thing about stamina that I don't understand is that I've found it quite important for all positions on the pitch. And I struggle to make sense of this using the above explanation for stamina.

Could it be that stamina is actually an indicator for how long time a player will last for each individual run?

That would explain a lot to me. Pace 20, stamina 5. The player starts off quick, but after 20 meters he is just drop dead exhausted and can't finish his run towards the goal...

Just a thought, could very well fit into the crazy ramblings category.. :)

Muda, Mura, Muri...

If you are familiar with production practices, Lean Manufacturing, derived from the Toyota Production System is arguably the most succesful and effective way of producing assets for the lowest cost.

I'll show you how I think in terms of stadium building, based on lean manufacturing.

First, I'll identify what my rep will be the coming seasons:

The top one is for my Upper league team, the bottom one for my Lower league team. I assume I'll get a top 3 position each season.
 
The strength of the new rep system is that it is very predictable, we also know when rep changes will happen in the future. So let's introduce the concept Just in Time

What I do is that I identify the time until these rep changes occur. I'll list my rep changes for my team in the GW Vald:

Current: 7.35
Tonight: 9.27
23 days: 11.07
28 days: 12.61
51 days: 14.41
86 days: 16.03

Do you get the idea here? Lean manufacturing defines anything created before these timelines as waste. Anything created after these timelines is of course equally inefficient.

So plan ahead. If your corporate boxes are going to be filled in 28 days, you better make sure that it doesn't take 50 odd days to build them!

By the way, I totally miscalculated my own rep (I blame this post from Jakswans blog), that is why I'm way ahead of what is actually needed for my stadium right now!

Team stats

A couple of days ago I scraped all available stats from the 22583 official matches played in Voller in Season 2.

2.4 goals per match in average. 1.31 YCs per team per match. 0.07 RCs.
330 passes, 17 dribbles, 19 crosses and 41 tackles in average per team per match.
16.9% goals from corners.

Some truly *doh* moments there when I looked into the stats. Like the fact that more tacklings won per match equals less goals conceded.

But I also found some decent findings. Dribbling for instance. The more dribbles a team has per game, the more likely it is to score goals. This does not impact goals conceded however, so I see no reason whatsoever not to let your pawns dribble as much as they can.

An observation I found on passing is that a higher amount of total passes per match does not impact the amount of goals scored, but it is important for conceding less goals.

An increased amount of passes NOT completed is a clear indication of more goals conceded. Maybe not rocket science, but this actually connects the dots quite well for me. I've found that the passing attribute is somewhat important for defensive players to not concede team goals, and this finding supports that theory. So my advice to you, get defensive players that can actually pass the ball!

On the topic of the hot potato of headers, the results are the same. The more headers you win, the more likely you are to score. The more headers you lose, the more likely you are to concede.

Did you expect anything else? :)

Some quick correlations

1.0 means they are always identical. This is based on my data sample for DCs in Sanchez.

Acc vs Agi: 0.72
Acc vs Pac: 0.59
Agi vs Pac: 0.61
Ant vs Cnt: 0.55
Ant vs Dec: 0.54
Jum vs Str: 0.73

Thats it. Some other attributes that didn't quite make it above 0.5:
Cnt vs Dec, Ant vs Pos, Agi vs Sta

Golden Retrievers

It's been a while since I posted anything about my Golden Retriever test team in Voller.

I just won the third run of the EFA lower league, I came second the two runs before.

I'm not too concerned about average rating, I really don't think ratings give the proper view of how my players are impacting goals scored and goals conceded.

My team consists of the following starting xi right now (including respective rank on my weighted list):
Position - AF - Av Rat - Weighted Rank - Natural Position
GK - 16k - 6.89 - crap - GK
DL - 55k - 7.55 - 200+ - DL
DC - 60k - 7.30 - 13th - DC
DC - 350k - 6.89 - 8th - DM
DR - 22k - 6.95 - crap - DR
DMC - 475k - 7.03 - 10th - AM
MCC - 90k - 6.94 - 17th - DM
MCC - 28k - 7.05 - crap - AM
FCL - 375k - 7.21 (0.3 GpM) - 9th - DR
FCC -  110k - 7.27 (0.8 GpM) - 4th - S
FCR - 150k - 7.10 (0.2 GpM) - 40th - AM

As you can see, I still have some positions left to fill. My income is limited though since I'm in the lower league. From a financial point, there is not much incentive for me to sign new players as long as I win my league.

I'm ranked 50th in the ranking system, decent enough knowing that most of the teams ahead of me are playing in the upper league.

Jumping vs height (#2)

Anthony over in Voller made this neat looking graph to view statistics from the last post about jumping visually.

Really a cool graph, I suggest you print it out and put it on your wall! :)

Dribble, pass, score!!

I've just looked into stats for 1200 matches for this MC.

He has a very high average rating, so I thought I'd find out why, and more importantly, what makes the average rating increase for players.

Remember these are still early findings and that I've only looked at stats for this one MC.

What I found is that there are 4 variables that really improves an attacking minded midfielder's match ratings.

These are:
Key passes
Assists
Dribbles
Goals

An easy way to test this is to go to Matches -> Stats for your favourite midfielder. Sort by either one of these variables, count the number of 8.0+ match ratings. Then sort descending on the same variable and count the number of 8.0+ match ratings again.

I'm thinking this could be the reason why flair is so overrated as an attribute for match ratings too. Surely a player with high flair will dribble more?

Yeah, and if you miss heading, it is number 5 on the list.. :)

Top AF country distribution

I'm not going to tell you which GW this list of countries is gathered from (I'm in 4 GWs in total, so you can still take a qualified guess).

Should give an indication of where you want your Academy to be placed to maximise high output (given that high AF = top talent). For me at least one country stood out. Still, I don't think YAs are financially worthwile in their current form, so I won't build anything there.

The list is gathered for top 1125 players with highest AF.

countr	count(countr)
CGO	1
HON	1
KAZ	1
IAM	1
MKD	1
TOG	1
LBR	1
CAN	1
GUI	1
ISL	1
LTU	1
UAE	1
MTN	1
EGY	1
GAB	1
IRQ	1
AUS	1
ALB	1
ZAM	1
SV	1
EOG	1
IRN	2
FIN	2
BOL	2
BEN	2
KOR	2
TUN	2
GRE	2
BLR	2
KSA	2
CIV	2
SVN	2
RSA	3
SWE	3
POL	3
COD	3
AUT	3
MNE	3
SVK	3
GEO	4
USA	4
IRL	4
ECU	4
BIH	4
WAL	5
CN	5
PAR	5
GHA	5
IPN	6
CMR	6
SCO	6
NOR	6
ISR	6
SEN	6
MLI	7
DEN	7
BUL	8
CHI	10
ROU	10
SUI	10
PER	10
RUS	11
CRO	13
MEX	14
CZE	14
UKR	15
BEL	15
COL	17
NGA	18
URU	18
SRB	19
TUR	26
POR	29
NED	39
GER	44
ENG	60
ESP	91
FRA	102
ITA	107
ARG	118
BRA	153

Expectation of the jumping attribute based on height

What a boring title of this post! :D
 
Anyway, I just had a chat with Hristo, the manager of BG United in Voller (he is one of the few ppl that actually added me as a friend due to my blog).

We started talking about his youth and what he could expect for his relatively low graduate DC to reach in terms of the jumping attribute.

And here is what I found out, these numbers are based on 24-29 year old random DCs in Voller.

Oh, and if you use imperial settings, then I suggest you switch over to metric numbers. Tell me again, how many inches is in a foot? :p

Looks like you need to hit 186 cm for your regens if they really are going to make it as a strong jumpers.


The cross-referenced list:

The Magical Mystery World of Flair

I'll be honest. I have no idea what flair is. Really.
A FM definition I found somewhere described it this way: "Flair is a player's unpredictability and willingness to try to pull off spectacular things."

Well. Flair is actually quite interesting when it comes to FML. 

From the numbers I have pulled, flair is in fact the most important attribute for improving match ratings for midfielders, and also the second most important attribute for strikers (after jumping)!

Further on, flair is the 6th most important attribute in terms of match rating for both DCs and DLs.

But then, the really interesting part is that fielding a player with high flair will not necessarily help your team score more goals.

The problem is, that for the life of me, I can't see what part of the match rating flair influences.
Jumping, for instance, is easy, it is the attribute that increases headers won %, which ultimately improves the match rating.

Anyone got a clue, what is the magic thing flair does that I have missed?

Goals scored - All good.

Checked 3 lower league divisions in EFA Voller.

The random sample shows
1'-15': 261 goals (17%)
16'-30': 241 goals (16%)
31'-45': 257 goals (17%)
45' + injury time: 43 goals (3%)

45'-60': 209 goals (14%)
61'-75': 220 goals (15%)
76'-90': 227 goals (15%)
90' + injury time: 55 goals (4%)

Nothing unusual here. In general, more goals are scored in the first half. Not sure how that relates to real life.

No exceptional amount of goals are scored in injury time.

When are my goals scored?

I had a quick look for my Golden Retrievers team in Voller.

When do I score goals, and when do I concede. The clips tab in FML where I got the info from only dates back two weeks, but here are the numbers I found:

Scored goals:
1'-15': 14 (16%)
16'-30': 19 (23%)
31'-45': 12 (14%)
46'-60': 15 (18%)
61'-75': 8 (9%)
76'-90': 16 (19%)

Conceded goals:
1'-15': 3 (10%)
16'-30': 6 (21%)
31'-45': 8 (28%)
46'-60': 6 (21%)
61'-75': 3 (10%)
76'-90': 3 (10%)

I'm not sure how much information I can read into this, except that it seems like I play my best in the first 15 and last 15 of each match.

The most interesting information is that 60% of all my goals scored within the last 15 minutes are actually scored from the 88th minute or later. Even though that almost makes sense if each match includes 4 minutes injury time.

No idea why the results are like this, but I'll note that I *never* sub players unless they get injured. And I've played most of my games without using match plans, so I think it could come down to higher stamina for my players when looking at goals scored by the end of my matches.

Or it could just all be totally random! :)

The secret sauce of GKs

Fact is. I don't think too many people visit this blog. So I'll give you the secret GK sauce to whoever reads this stuff!

The data samples come from two different GWs. I've probably analysed stats for more than 800 GKs in each of the GWs.

The results are the same for both GWs.

This is where you want to say that my numbers are wrong, or that this just doesn't make sense. But the numbers are right. Still, I'll admit. It doesn't really make sense.

The most important attribute for not conceding goals for a GK is Acceleration.

Yes, I'll repeat that.

The most important attribute for not conceding goals for a GK is Acceleration.

I have no idea why. But then again, I just crunch the numbers and act from there. Someone else can probably tell me why Acceleration is so important for GKs.

Here is the full list from my research for attributes for GKs for not conceding goals.

 
If someone can tell me why Acceleration is on top, please let me know..

Patrik Christensson

My newest striker. Identified as 3rd highest GpM player in Voller.

14 goals in 14 matches for me. Got him for £750k.

The other guy caused a bit of disturbance in the forums, so I thought it was better to play him as DR which is his natural position.  :)

On another note, I actually expect my team to be a bit better than it is. I'm ranked 80th-ish in the GW, not too bad for being a lower league team. Still, I've only got 4 players on the team now that isn't top 30 in their position, so I do expect more from my team!

Time will tell. It's hard to assemble a good squad with the limited resources available from not playing in the upper league. I still think the gap is too large depending on which league teams ended up in from season 1, but there is nothing to do except to look forward. At least my GpM indicator doesn't seem too far off.. :)

Peak of attributes..

Low activity in the blogosphere today so thought I'd post whatever (un)interesting stuff I have been up to since last time.

Here is a dump of average attributes for different age groups from the three GWs I've indexed.
Ignore the actual numbers (they include zeros and stuff), but take a look at the deltas and when the attributes peak.

Not much more to say really. At least you can get an idea of when and how attributes increase for youth. The data set is representative enough I guess, but remember that I've indexed players based on attributes. For instance, I've sorted players on jumping with filter u18, and this is probably why jumping has a higher average for younger players.

My newest signing..

GW Voller: Matias Perez - D/M R plays as FCC - £22k wages - 12 goals in 11 matches

He scored 4th highest on my GpM test. Just imagine what his wage would have been if he had been a natural striker!

Conundrum of the day..

If Jumping is the most important attribute for not conceding goals, why is it that Jumping is not the most important attribute for scoring goals?

The times catched up on me!

I'll let you know why I will never become number 1 in any GW.

The reason?
I'm too old. Aged 29!

If I'd been playing this game 10 years ago, I know I would have been all over the market place. I would have pestered people, chatting in the lobby, making myself a name in the GW, always looking for good buys, selling crap players expensively and generally strike good deals all day long.

Nowadays, I can't be bothered. If I want to sell a player, I'll put him up in auction for a reasonable price, and he is picked up within a couple of days. If I want to buy a player, I'll submit a couple of over-the-top offers and hope someone will accept at least one of them.

So that leaves me in a position just outside the very best of managers. I know I make mostly correct decisions in the game and that strategically speaking, I'm probably one of the best managers out there. But it doesn't add up when I'm not at pair in the transfer market. Add the fact that my tactical knowledge is somewhat limited, the conclusion is that I don't think I'll ever make it to the top spot for an extended period of time.

I feel a bit like a footballer in his last years. Even though I now have a much better understanding of the game and I know how everything will play out, I just don't have the youthful dedication that is needed for the final push towards true glory!

Lower league??!?

Okay. Here comes a little rant about the qualifying system.

In the Voller EFA I was one spot from making it to the upper tier. Which means I'm stuck in the lower tier. And it really annoys me, because I lose out a lot of money and opportunities to play against the best teams. And what really gets me is that it takes soooooo long to climb back up to the top. The first theoretical chance I have of winning the premiership is now in season 5.

The thing is. After I changed to my weighted approach to players I've done really well. So well in fact that I truly believe that with my current team I'll walk over both the lower league(s) this season and the middle league(s) next season.

So really, from a financial perspective, why would I bother buying players and paying more wages when I know the current squad will do perfectly well for at least two seasons?

I'm driven by two things in life. Business and impatience. Luckily, impatience outweighs my business sense, so I'm already on the ball looking for new good players to be found!

On the topic of my Golden Retrievers team, I signed a DM ranked 20th and an AM ranked 27th. They cost me a small fortune, but hey, we're only using Monopoly money anyway.

I've set a goal to break into the top 10 ranked teams in Voller. It might be tough since I miss out quite a bit in terms of price money as a result of being in the lower league. On the other hand, I've got a list of attributes for over 3500 players printed on my wall. Surely that must count for something?

And in case you wondered, I'm 112th ranked right now.. :)

Preferred foot

Simple test. Low data sample.

Does two-footedness improve GpM rate for strikers?

432 strikers in Voller appearances 7+.

# observations - Preferred foot - Average GpM
13 - Either - 0.22
35 - Left - 0.25
199 - Right Only - 0.28
166 - Right - 0.28
19 - Left Only - 0.30

Conclusion is that two-footedness does not help. But the disclaimer is that the sample size is low and the variation is high due to the low number of appearances.

Golden Retrievers: A Flying Start

Following from my last blog entry The weighted approach to glory!

First two days of new season in Voller has passed, and I've got some good signings based on my weighted attributes list.

I have played 10 FA games with the following results: 7-3-0
Scored 17 and only conceded 5.
Remember though, this is the lower league of the EFA, so there are better teams to play out there :)

My team consists of the following players now. I'll list their placement on my weighted list for each position. N/A means these are my old players not even close to matching the best attributes for their position!

Position (Natural position) - AF - Weighted rank
GK (GK) - £28k - N/A
DL (DL) - £50k - 239th
DC (DM) - £220k - 17th
DC (DC) - £35k -8th
DR (DR) - £20k - N/A
DML (AMC) - £16k - N/A
DMC (AMC) - £230k - 7th
DMR (DMC) - £28k - N/A
FCL (DMC) - £16k - 178th
FCC (AMC) - £24k - 68th
FCR (DMC) £40k - 123th

All in all, I'm really happy with my defence. Those two DCs which score high for defenders on my weighted list are really spot on. Assisted with the brilliant DMC, I have a really strong central defence.

Looking at the attacking players, I'm not sure if I use the correct parameters. Right now my list is based on maximising Goals per Match (GpM), but I'm thinking for the team as a whole that it would be better to base the list on attributes needed for scoring team goals. However, I'll leave it for now.  My FCR has a 0.7 GpM ratio, the FCC has 0.5 and the FCL 0.1.

Next for my team, I think I'll secure my defence by getting a new GK, DL and DR.

On a side note, I'm also looking at a project of creating a team in another GW which will be based on maximising the average rating. Obviously the players would all need jumping 20, but it could be an interesting project to see how much average rating actually corrolates with team performance.

The weighted approach to glory!

So I've decided to put my Voller team Golden Retrievers up as my test team.

The background is that by now I've gathered enough information for identifying most important attributes for DCs, DLs and GKs for not conceding team goals. I also have a fairly good overview of which attributes are needed for FCs to score goals (GPG).

The second thing I've done is to sort all players in Voller based on these attributes, screen grabbed from FML, and used OCR to get all the data into a SQL database I set up.

So there we are. On one side I know the weighting of attributes, on the other side I have the actual player data from Voller. Perfect.

By combining the data, I've got a weighted list of best players in Voller for each respective position. I've now started purchasing some of the top (cheap) guys based on my weighted list. So far I've bought two DCs and three FCs.

Well, my FCs are technically not FCs. They are 2xDMs and 1xAM. They still came quite high up on my FC GPG-test though, so I think they will do quite well even though they play in a bit of an akward position.

The results so far, including where the players rank in my weighted GPG system:
My target man FCC (AM - £26k AF - 63rd) has scored 1 in 7 matches
My poacher FCL (DM - £35k AF - 170th) has scored 4 in 6 matches
My poacher FCR (DM - £16k AF - 120th) has scored 3 in 6 matches

More to follow on my progress. I don't expect to build a top team by playing DMs as strikers and there are still many players out there better than mine. But I do think my team will end up stronger than many others, plus I'll get the bonus of a much lower wage bill..

Come have a look at my team in Voller. It's called Golden Retrievers.

And now for something else: Side backs.

First, I'll define side backs as players with their primarily function to avoid conceding team goals. That makes things a bit easier.

Remember when I said that jumping was the most important attribute for side backs? Well, I was wrong. Partly. Jumping is the most important attribute for improving a side back's average rating.

Jumping is the 7th most important attribute if you don't want to concede team goals!

Looking at the most important attributes for DL/DRs if you want to score team goals however, jumping is still the most important one. I guess this comes down to the now-fixed corner exploit where many of the cornerbangers were actually side backs.

So what is the main attribute you should look for if you want your side backs to concede less team goals?
Well, it's not a technical attribute, neither is it a mental one.. ;)

JUMP!

It's been a while since my last post. Partly because my trial version of SPSS ran out, partly because I've been busy with other stuff, but most importantly because this is my blog and I can update it whenever I want!

I'll follow up the discussion from this thread. The question asked:
Does the 6.2 rating of a bad jumping DC (good otherwise) or the 7.2 rating of a good jumping DC (bad otherwise) have the equivelant effect on the final result?
Can a team with the 6.2 DC win more games than the team with the 7.2 DC?

Well. To find the result, I simply removed average rating from the equation.

I did a regression analysis on team goals conceded per game for DCs to find the most important attributes for not conceding goals. I've OCRd 1196 DCs from Voller, so the numbers should be significant enough to provide quite a clear result.

And the result?

*drumroll*

*more drums*

*drumrollllllll*

Get a DC that can jump! ;)

Feed the ball..

A quick look in Valderrama today.

View: Position; Assists Per Match; Crosses; Appearances
Filter: Corners < 10; Appearances > 20

By simply scrolling down the list, it looks like most of the players with high number of assists per match either play AMC or Striker. Their crossing/appearances ratio isn't too impressive either.

The conclusion?
Either most people prefer to play a narrow formation (4-3-3 anyone?), or ball distribution from the centre is most effective for scoring goals.

I say a bit of both! :)

Skin Magic!

Here are three cool skin tricks I've messed around with.

#1: Improve the font size
You can do this by changing the variables font_x_sz and font_y_sz in skins.cfg and default.cfg in your skins folder. I prefer font size 14.

#2: Change the font
This one is cool. Grab some free fonts for instance from here. Replace (and rename) the downloaded fonts with the ones existing in your fonts folder.

#3: Mess about with your image files
Just when you thought you couldn't have more fun!
In this example I changed the grass to purple, why not let the pitch match my team's ugly shirt colors?

Launch Photoshop, navigate to the image files in your client/app/data/images folder and start getting creative!

Run for your life..

I'll give you some secret info. This is like at uni when the professor told the few poor souls that met up for the 8 am lecture on Mondays what stuff they really should focus on before the exams. Same thing here, if you found the blog, I'm happy to share some information with you.

Today's topic is Acceleration and Pace. How important are these attributes to boost the average ratings for players in certain positions?

I did a screendump of all players in their respective positions in Clough before it closed down, and here is what i found. I'll even throw in new and revised Jumping attributes..

Again, no guarantee for any sort of validity to these numbers, but I think they are fair to use as a guide as a minimum.. :)

DLs: Acc: 9th - Pac: 3rd - Jum: 1st
DCs: Acc: 3rd - Pac: 5th - Jum: 1st
DMs: Acc: 13th - Pac: 7th - Jum: 2nd
MCs: Acc: 4th - Pac: 28th (!) - Jum: 2nd
AMs: Acc: 5th - Pac: 15th - Jum: 3rd
STs: Acc: 2nd - Pac: 5th - Jum: 4th

MLs are not included due to the simple fact that I normally play 4-3-3, so why focus on players I will never buy?

So as you can see, some differences for Jumping compared to my previous post which was based on Nicholson stats!

Now go get those Jumping 20 players from your GW!

So you think you can Jump?

It is with great interest I read the posts in the FML beta forums where people say jumping and speed are the only attributes needed to get a good team nowadays in FML.

Well, the proof is in the pudding. Over the weekend I created some scripts for my MBP to easily grab screenshots from FML and convert them to hard numbers to play around with.

The results were found through regression analysis, with (almost) all attributes set up as independent variables and average rating as the dependent variable. Criteria for the filters were 20+ major appearances and only players playing in a specific position were included to avoid false data.

In this scenario, a positive outcome means that an attribute has a higher chance of increase the average rating, a negative outcome means that the attribute is less important for the average rating.

Here are the Jumping B-coefficients, note that these numbers are by no means decisive, but I think they offer an indication of the ranking of attributes for specific positions:
GKs: 0.084 - 8th most important attribute
DCs: 0.357 - 1st most important attribute
DL/Rs: 0.395 - 1st most important attribute
MCs: 0.067 - 9th most important attribute
ML/Rs: 0.032 - 14th most important attribute
STs: 0.105 - 7th most important attribute

The conclusion? If you want to get high average ratings for your defenders, you definitely need someone with high jumping attributes! :)

More to follow.. Perhaps some stats around pace and acceleration. I'm waiting for players to get enough matches in Miller so I can get some updated numbers from there too..

Oh, and one more thing. I'm not really going to hand over to you the list of the important attributes.. Where would the fun be in that? ;)

When to close down your YAs?

First things first. I don't believe youth academies are wortwhile in their current format. At least not in the long run. The demand is just too high, and supply is perfectly inelastic given the need of balancing the total potential in a GW.

But let's have a look at the numbers, that's why we are here.

Let's define the average market price as it would be if all graduates were sent directly to transfer auction.

I'd say something along the lines of this in a well-established GW:
5.0*: £3m
4.5*: £1.5m
4.0*: £500k
3.5*: £200k
3.0*: £20k

How can someone predict these numbers I hear you say. Well, I can't. Still, I think they give a rough estimate of the average market value for players in a GW.

In my live GW i found the following youth distribution of potential for one season:
5.0*: 15
4.5*: 31
4.0*: 81
3.5*: 235
3.0*: 450

So in total, youth for one season are worth:
£3m*15 + £1.5m*31 + £500k*81 + £200k*235 + £20k*450 ≈ £200m

We will assume that 75% of youth are generated through YAs = £150m.

Every graduate costs £25k in running costs to produce (running cost of academy / graduate output). The equlibrium can be calculated as total youth market value from academies / graduate cost: £150m / £25k = 6000 graduates each season in the GW.

This roughly comes down to 700 3* academies.

So there you have it. Once you see more than 700 academies in your GW you really should close down your academies and spend your hard earned cash elsewhere.

Of course, this doesn't take into account the start-up costs for the YAs. If we expect a 3 year lifetime for the YAs we build, we can add around £15k pr graduate in construction costs and the total cost for a graduate wil be £40k.

Taking into consideration these costs, you should probably close down your academy after around 400 academies have been established in your GW.

From a financial perspective, to have the slightest chance of expecting a positive return from your YAs, you should go all-in from the start of the GW with the knowledge that you will have to pull out once enough academies have been established in the GW.

Practically speaking though, my gut feeling is that the overall equilibrium numbers shown here are too high, and that YAs never will be worthwhile if you choose this route as a way of improving your finances graph.

Of course, unless you manage to find some obscure country producing raw talent day in and day out!

Get the most out of your skill points

I'll give you three simple rules to follow when distributing your hard earned skill bonus points after reset. First you need to prioritize your specialization areas from 1 to 5.

#1: Pick 5* learning in specialization area 3 and 4
#2: Spend the rest of your bonus points in specialization area 1 and 2
#3: Do not learn any learning skills in areas where you are distributing bonus points

#1: Pick 5* learning in specialization area 3 and 4
This is where you will spend most of your time learning skills after reset. It is also where it takes the longest to learn new skills, 25% reduction of the time taken here is essential in the long run.

#2: Spend the rest of your bonus points in specialization area 1 and 2
Chances are that you will max out at least one specialization with your remaining bonus ponts. If you have a decent amount of points, you will come quite far in specialization area 2 as well. There is always the trade-off that you could do some learning skills for specialization 2 if you will be learning this area for 50+ days, but do not let this influence your choice of spending your skill points on 5* learning in specialization 3 and 4.

#3: Do not learn any learning skills in areas where you are distributing bonus points
Simples. You want to get as much as you can from your bonus points. For every point you put into a area you already have a learning skill in, you essentially lose 25% of the net worth of that point.


Personally, I will choose Finance and Scouting as my specializations 1 and 2. Coaching and Physio will be my 3rd and 4th choice. The reason being that both Finance and Scouting need 5* of a skill to get the wanted results (Commercial and JP). Coaching and Physio however, only require 4* to achieve the wanted results (Attribute training & Injury specialization). Infrastructure is just not worth it for me, as I will only bother with stadiums. I'll get the essential skills up to 3* at some point, and that should be enough for my construction expenses.

Happy skill distribution..