I guess the finding hasn't been updated for FM24 though, which has changed options too.
Not sure if anyone else has tested it. I doubt it since you can't set it all up in advance. Might be worth looking at down the track, but it likely has negligible effect.
LightningFlik said: I've got the test harness working now. I'll be releasing the tool maybe this evening or tomorrow, once I've removed all the hacky debug stuff)
It works by exporting out of RAM the entire NEXT_FIXTURE object and then passing that as an argument in to the FM's GAME_MATCH__Run function (which it does via an injected DLL). And that works and you can run it multiple times with the same inputs but only changing the seed and it works great.
One thing I noticed about repeated edits of players and dumps of the next-fixture blob are that the game generates role ratings for each player across all 22 roles, except it uses the lower value of either itself or the player's adjusted CA (CA but modified slightly by Important Matches, Consistency, Determination and Natural fitness (which are all worth about 0.2 CA per point). I saw Ollie Watkins' CA go from 144 to 149 (his real value is 146)).
These roles are calculated using the attributes the game highlights for you on player screens, so Pace only appears in something like four of them, not almost all like you'd actually expect.
Changing these derived values makes no impact on results; changing raw attributes does. I suspect these role ratings are used for AI player selection (because I drastically altered some and the players slipped out of the opponent's starting XI).
There's another value that changes whenever you modify your attributes and regenerate a fixture and I'm calling that the Leadership Value. It's a number from 1 to 4,406 and its weighted by these attributes: Leadership 184.92+-127.02, Teamwork 1.20+-0.68, Work Rate 1.05+-0.69, Consistency 0.60+-0.35, Important Matches 0.55+-0.33. I couldn't tell you what it does though. It looks like how you'd rate a captain's ability but I've not seen where it's used.
Also, positional familiarity has a cliff. At 12 and above, it contributes about 1-2 points to the role ratings described above (which, again, make no difference to actual performance). At 11 and below it collapses:
John McGinn's trquartista rating goes from 113 at familiarity 12 to 47 at 11 and 44 at 10. Below 10 he gets dropped out of the starting XI altogether.
Consistency, Determination, Important Matches and Natural Fitness only make a difference between 5 an 14. They shift the ratings by about 2-3 points at the extremes.
I've not tested yet the impact of these things on actual performance, just expected performance so far. Expand Good work man
Sounds promising
With position proficiency, I reckon that might have something to do with the fact that the CA weighting calculation also changes around proficiency ~10-12. I.e. at 13 ST proficiency it would use ST weights for attributes, but at 10 proficiency it would use DM weights if his main position is DM.
The leadership -5 = -0.35 goals finding is an odd one. Tempted to give leadership another test already to check if there's something in it. Anticipation also doesn't seem right.
The data seems to reveal what attributes feature in contests and which don't. For example:
-5 pace to Aston Villa = -0.59 goals for Aston Villa, +0.40 goals for Bristol. So pace is a contest.
-5 determination to Aston Villa = -0.28 goals for Aston Villa, +0.01 goals for Bristol. So determination is not a contest at all.
Composure, leadership, dribbling, are some more examples of non-contested attributes.
-5 dirtiness is +0.24 goals to Aston Villa, -0.08 goals to Bristol. This suggests that only a quarter of the downside of dirtiness is what you concede, the majority of dirtiness actually prevents you from scoring goals.
By contrast, low aggression mainly results in more goals conceded rather than less scored. In fact you score more with lower aggression, but also concede more than what you gain in scoring, making high aggression a net benefit in spite of less clean sheets so to speak.
Passing, decisions, long shots, technique - lower values reduce both teams scoring output. Maybe this helps to explain why I've observed significant g/d differences with these attributes once you start reaching very high performance levels (i.e. all games won, +150 g/d). Perhaps when you're winning games regularly 5-0, the extra +10 to a few technicals are culminating in an extra +1 g/d or so per match, which would be +38 g/d over a Premier League season.
Might be reading into this too much given the confidence interval, but gives an idea of what we might be able to deduce from more of this data.
LightningFlik said: The Reddit post I highlighted a few days ago (the one where the guy got the game's match engine running independently of the game) has inspired me to get serious about disassembling the logic for FM 24. So far I've managed to find where the game's seed is supplied to the match engine, which means you can set a breakpoint before going to the match (it only works for when you click the Instant Result/Simulate Match button right now), set the seed to a fixed value (right now it comes from your machine's current date/time) and then get the same result every time.
I can share this if people think there's a use-case for it, but I'm going to be cracking on with my digging regardless. Expand Are you saying that the match result is partially determined by the system time?
I've wondered if this is the case, from seeing Paradox do it with their saves
I will refer to 'V4' as 'V7', as it is actually named V7 in the file. On the site, it is V4.
I didn't record the data neatly and most of my samples are for variants not mentioned here, so there's more to it that I will talk about in my notes below, but this data provides you with some direct comparisons.
To give you some bearings, at Rushall 'FM24.4 WARPIGS 4231 RM P111 CC' placed 20th and LxS 433 placed 6th.
The 433 V7 tactic is neck and neck with the Knap tactic. By virtue of the fact that it's a different formation to the 424, I was planning to upload my own variation, but it turned out that default V7 is about as good as the best variations I tried. So I simply credit the author.
Now the FM Arena testing has given V7 a dud score putting it on page 30. You can't see it in the data presented above, but what I noticed during testing was that V7 more temperamental than the Knap tactic. An unselected Luton could get 9th for example. Variant B did a good job of tamping this down I believe. My speculation is it's that there is more variance in the opposition's DCs (vs V7's extra ST) than their DL (vs Knap's AMR), and that the opposition quality is more likely to pip your own in the central areas in the Premier League.
Whatever the cause, the stability of Knap's tactic does matter, and so I would personally still stick with it over the V7, but the V7 does give another option, particularly if you lack decent wingers. What I've found through this testing is that team selection matters quite a lot. Again the data above doesn't fully show it, but I would often get consistent 2nds with Luton with the right team selection (you can see this with 424 Classic v164). Delegating to the assistant, as low as 6th-8th becomes much more frequent. So that's ~2nd-8th > consistent 2nd through team selection, which is bigger than the difference between many of these tactics that get uploaded.
As you can see from Variant A, sometimes a tactic can do great in one league and yet relatively poor in another. Both the Knap tactic and V7 are exceptional well-rounders. Generally speaking though, 8 or 9 times out of 10 the better the tactic, the better it does in all conditions. So I don't think the 'underdog' tactic is really a thing - and it's certainly not switching from 'attacking' to 'positive' or the like, which does nothing at best and ruins the tactic at worse. You have to keep in mind these tactics have already been refined to select the optimal mentality it performs with.
I found that 'Position/Duty/Role' in tactic familiarity makes no observable difference. You can get 0% in this if you don't make your player train in that specific role. Since specific role training is detrimental to attribute growth through training, be reassured you don't have to chose between them. 0% role familiarity is fine. This will also show up in your 'partnership' green lines on the tactic, which will say the role combination is suitable/unsuitable if you hover over it - again, this doesn't seem to matter, so ignore this.
The stars your player has for the role also doesn't seem to matter. Selecting the player with the highest % score in FMSS or Genie Scout is what matters. Leave the role as what the tactic dictates it is, as role changes do change the tactic effectiveness.
I had the idea that perhaps less instructions = more adherence/more familiarity = better results. So I gradually stripped down the V7 tactic until I ended up with this:
The result was 2 x 5th. I reckon it's probably true for other tactics as well, but it's still a slight disbenefit rather than a benefit. And we see with the Rushall results that it doesn't matter how crappy your players are, they handle the extra instructions fine.
I should also note that Diabolo4480 has uploaded a number of variations the same fundamental tactic, up to version 30. I haven't tested all of these, but I tried a few and settled on V7 as the best starting point to work with. V28 for instance is roughly equal, but not better.
The following is with just a 4 team sample, with Wales Div 1 being the loaded division.
England Unloaded 'Unimportant':
Year 1 - 138.75 median PA, 152.25 peak PA, 4.75 players Year 5 - 131.125 median PA, 144.5 peak PA, 5.5 players
Average 134.94 median PA, 148.37 peak PA, 5.125 players
England Unloaded 'Very Important':
Year 1 - 150 median PA, 167 peak PA, 4.75 players Year 5 - 127.375 median PA, 151.75 peak PA, 5.25 players
Average 138.69 median PA, 159.37 peak PA, 5 players
Here we see a 6.9% drop in peak PA and a 2.7% drop in median PA. The median PA drop is probably just due to the low sampling size. Number of newgens retained by clubs did not change.
Some significant caveats here are that I'm using a small database, am only testing England (which may be a specially treated nation), and things could further change when we get to year 10, 15, and so on. I will also say that when I tested Game Importance a few years back, I observed a steeper drop off in quality than just 8.3% after a few years. Perhaps though that happened due to other factors.
So this isn't the final word on game importance, but I think it's reasonable to simplify Game Importance as a flat 8.3% effect on peak PA and 0% effect on median PA, regardless of whether it's newgens from your own team, another team in your division, or a team in an unloaded nation.
Because it has no effect on the median PA, it doesn't come into the newgen calculation. Nonetheless it's something to keep in mind, if you are assessing USA ('fairly unimportant' ) say, or doing editing.
As I was looking for what other people may have tested and found, I came across this from the SI forums:
I've done some testing to more definitively measure the effect of 'Game Importance'.
I concurrently player-managed all 20 Premier League teams for this test, to ensure I have the complete picture.
England Div 1 'Unimportant':
Year 1 - 122.1 median PA, 145.7 peak PA, 133.6 4th PA Year 2 - 126.5 median PA, 149.7 peak PA, 139.9 4th PA Year 3 - 127.8 median PA, 151.9 peak PA, 138.9 4th PA Year 7 - 118.0 median PA, 143.8 peak PA, 135.6 4th PA
Average: 123.6 median PA, 147.8 peak PA, 137.0 4th PA
England Div 1 'Very Important' (Default):
Year 1 - 124.2 median PA, 161.1 peak PA, 140.6 4th PA
Conclusion: Game Importance 'Very Important' > 'Unimportant' = 0% median PA difference, -8.3% peak PA
This isn't the end of the story yet however. For the intake of your own team, the takeaway is game importance has a minor affect on high-PA newgens you get. But there may be different effects for intakes of unmanaged teams and unloaded divisions.
England Div 1 Unmanaged 'Unimportant' 4 team sample:
Year 1 - 127.37 median PA, 149.5 peak PA Year 5 - 122.5 median PA, 148 peak PA
So it does not appear that player-managed makes a difference.
Next I'll see if loaded vs. unloaded division makes a difference.
BTW something curious happened on Year 7 of the first test - several teams had 19-24 players in their intake, rather than the usual 16:
Could be a bug. Or perhaps it has to do with something like player retirements, where some extra players are sprinkled in to make up for the retirements.
MeanOnSunday said: What about the variation in the number of teams in a country? If there are more players generated because there are more teams, are high rated players unlimited to make the median stay constant or does the median drop because the pool of high rated players becomes depleted? Expand I doubt it is the number of teams, for two reasons:
1) As I mentioned in my post, the initial way I verified the factor (years ago) was by swapping England and Romania completely (all clubs, competitions, etc.) and observed a ~25% median PA change.
2) There are 1217 clubs in Germany and 56 possible active clubs. There are 4693 clubs in Italy and 100 possible active clubs. Germany has +10% median PA when all else is even.
That said, there is something going on involving club rep & youth facilities of more than just those in the active division. As with the two examples above, I can see some potential inroads through logical deduction.
In regards to your second question, I'm not sure what picture you have in mind exactly, but I will say that the nation average median PAs don't reflect an actual average median the nation maintains. It is only the average for the top division in each nation, in term determined by each club within the division.
For example, my artificial test of Singapore Div 1 has the average median PA at 73. But in real play, where the club rep and whatever else is significantly lower, the average probably drops to ~40 based on the Balestier results. It doesn't get fed through again to the nation/division factor to ensure it lines up with a table of numbers where the median Div 1 French club is 120 median PA.
It's a simplification therefore to say that France is 120 and Italy is 100, but this doesn't diminish its usefulness.
GerardFM24 said: @GeorgeFloydOverdosed Thanks for the reply! Looking forward to seeing your final filter. Expand To at least have a decent chance of reaching the essentials of a top player you would need:
10 professionalism, pressure, consistency, important matches
10< dirtiness
>100 PA
Age 15-17, but also consider age 18 players
In my test save, there still weren't that many results, but you only need a few each year. You could drop it the visible attributes to 6 if need be, but I wouldn't go any lower than that.
If you can't buy anything at 6, say because you're a lower league club, it's best to then just focus on high pace/acc/sta players. That would be 11+ pace/acc and 7+ stamina starting. I wouldn't even add anything else to it, as things like work rate can be trained up adequately and hiddens such as pressure & important matches matter less in lower leagues.
Each year about 7 players will need to graduate from the u18s, so if we assume a squad size of 21 players. If 3 of these are from intake, that leaves 4 for purchases. So we only need a few viable options to be available.
Of course there's the option of loan farming 100 u18 players, but players on loan won't train correctly, so this can be considered as an extra rather than alternative.
GerardFM24 said: There is some amazing research here that really keeps my interest. If you put your data to the test and created a shortlist filter of attributes for a player aged 15–16, what would be necessary for them to become a world-beater? Expand I was thinking of just mentioning a few attributes that are difficult/impossible to improve, as I'm not really interested in 16-year-old filtering. My thinking is that you can mold almost any newgen into what you want, so long as they have high PA.
But actually it's more interesting the more I think about it. There are very limited spots for first team experience, and it's too easy to stack the youth squads with too many players causing injuries through low match fitness & high workload on training staff, which reduces the growth all around. Being able to filter even those mid/high PA newgens would be useful.
Rather than answer this right now, first I'll do a test to update my knowledge of what can realistically be achieved in training.
It would have to be that it's set for division or nation, otherwise a division 7 club could never improve to a high level. But then how would it determine the ranking of specific clubs, as we see in the actual results?
I realized what instead it could be is the position the club is at in the league at the time of intake. I've done some tests with Luton so far that seem to show this is the case:
Luton 19th/20th = 124, 111.5, 115, 109.5 = 115 average Luton 12th = 122, 107 = 114.5 average Luton 5th = 143.5 Luton 4th = 129.5, 133, 125 = 129.16 average Luton 2nd = 135.5, 135 = 135.25 average
Note this does not mean there is not a hidden factor, as the 1st team in Gibraltar is still worse than the 20th team in the EPL, when all relevant visible factors are equal. But it does explain another piece of the puzzle that can be added to the factors list.
This is still compatible with the team PA list hypothesis in this way: Rankings (i.e. 3rd, 4th, 5th, etc.) in a division could be assigned PAs, and who ranks where is determined on intake day by the actual competition data.
Something else I noticed is that the peak PA also seemed to change, from ~130s for 20th to ~150s for 2nd. What stands out to me about it actually is that the PA range always seemed quite compressed. Usually with very high median PA teams one should get the random 178 or 192 PA newgen. But not here with Luton, where the peak is consistently quite low - 158 or 159 or even 148. Makes me suspect that factors such as Youth Recruitment and perhaps Youth Rating are solely responsible for those very high PA newgens.
Edit: I do not find the same for Balestier in Singapore:
This doesn't mean the Luton case is invalid, but it means that we cannot assume other divisions have a similar position effect. We can say it's true for the English Premier League. It's possible that Singapore simply has identical PAs set for its ranking list, but that is just one of a whole bunch of possibilities, so we cannot assume that.
Another thing I've noticed is that in spite of all team factors being made identical, club median PAs still tend to follow the conventional reputations. For instance, Maccabi Tel-Aviv is well ahead of other Israeli teams, and same for CSKA Moscow in Russia. AS Monaco is 1st in France, AC Milan in Italy.
I thought initially it's just that these clubs are the ones most likely improve their facilities/rep in those few months, or that perhaps continental competition qualification plays some small role. But then I saw this also happened for a short ~4 month wait until intake, and with nations that don't really have continental competition/qualification of note.
My hunch is that each team actually has a set target PA as part of the hidden factor. I know that sound a bit far-fetched or convenient, but consider the following:
1) From this guide for Italian volunteer researchers for FM20, they are instructed to match a list of average CAs for each team & division:
So it's evident the SI team are already going to these lengths even before the volunteer researcher data comes in. Rather than a general nation or division factor, it could simply be these values are being given a weighting in the outcome.
2) This is speculative, but it makes sense to me from a game balance and narrative perspective why they would include this in the formula. This way, if Liverpool somehow bomb out a few years into the game, they are somewhat cushioned from the prospect of relegation, and even if they are relegated, it allows for them to bounce back. Or if they neglect their Youth Recruitment which we know is very competitive in England and drops with time without investment, then this helps to keep them propped up as a source of quality youth.
Now separate to all that, there are two other things that are relevant to my formula but not included in the formula:
1) Game Importance. This is a difficult factor to measure because it seemingly affects distribution more than median PA, and it also seems to have an effect that reveals itself over several seasons rather than just one (which may carry a more pronounced effect on median PA as time goes on). Thankfully this isn't a factor you can improve in-game, and most of the best nations have the highest game importance anyway, so it's not a huge deal if we set it aside and simply say only expect good newgens from 'very important' nations if you want to be on the safe side.
2) In this thread I detail a curious impact I found in regards mainly to Club reputation and Youth facilities of other clubs within the nation. Club Rep 1 of all other clubs results in ~10% less median PA, while combining it with Youth Facilities 1 of all other clubs somehow cures it completely. Ordinarily I have found that club rep has no effect on PA, aside from Youth Recruitment tie-breaking. The underlying logic to all this remains unclear. Overall, I felt it's not necessary to complicate the formula with this. The point of having a formula would be to quickly deduce things with minimal inputs, and if its ~90% accurate, I reckon that's good enough.
Yarema said: What are the intake sizes in those countries? Expand I add a manager for each team before intake day, so 16 players per team x the number of teams in the division.
It occurred to me during the process of getting the data that perhaps the hidden factor is merely to do with the number of teams in the division. After all, Singapore & Switzerland & Scotland have fewer teams than the England Premier League, but Italy has 20 and Argentina has 28, so this doesn't explain it.
It's equally valid I think (based on actual past experience) just to take the median of non-managed teams, even if they only retain say 7 of the newgens, but I wanted to make sure + distinguishing between existing & newgen 16 year olds can be painful that way.
cadoni said: The SI answer back in 2023, comes about Dynamic Youth Rating (which was failure) and only; and stated that those factors are remain fixed (e.g Economic Factor, State of Development, FA Financial Power and Game Importance).
Appreciate your efforts to test those things, but for hardcore players is not a secret newgen mechanism. What was a surprice, before 2020; was about Youth Facilities - not affecting PA. There was a vast majority of people thought that Youth Players was Training in Youth Facilities, which is not true. Expand Not sure what you're saying exactly
If you mean that those 4 factors are what I am identifying, that is not the case.
Of those, only game importance has any effect (at least on PA, but I daresay CA too), and game importance has little effect on median PA. It has a unique effect on the distribution curve, but let's put that aside here.
You make a point of saying these 4 factors are fixed, based on that SI post. I suspect you're taking it to mean that it's fixed, as in it stays the same in spite of whatever value you enter in the editor. This is not true, as game importance can be changed with observable effects. What he would mean by 'fixed' is that it doesn't change once you've started a save.
Also it wouldn't make any sense anyway:
France - 19 economic factor, 17 financial power, developed state, 'very important', 155 youth rating Germany - 19 economic factor, 20 FA financial power, developed state, 'very important', 155 youth rating
How then does France do 10 PA better than Germany on average, when all relevant competition & club factors are identical?
As far as I'm aware, no one else has even suggested this hidden factor I've identified exists, and we see it is significant - the difference between even Italy & England wipes out a 13 difference in Youth Recruitment, judging by the approximate formula.
MeanOnSunday said: I thought competition reputation was editable. Also wouldn’t the country population have a large impact? Expand I forgot to add that to the control list, but yes, it was actually controlled for. I'll add it in. I set competition reputation of all Level 1 divisions to 130.
I don't have competition reputation on my list of factors, which means it shouldn't have any effect on PA. I can't actually remember this, since I tested it back in 2020, but I can confirm my 2020 notes say it has no effect on PA. Note that CA works a bit differently.
Country population has no impact - we know it can't be a hidden factor, because there are obvious counter-examples and rationales we can infer: China and India have much higher populations than England. If population mattered, even a little in regards to certain cases, it would make a real mess of things.
Further suggestive evidence for this is that cities and regions don't matter, and turn out to be entirely cosmetic under careful examination.
All the talk you've heard from SI staff about it taking into account economic factor, youth facilities, players being recruited from nearby cities, etc. is baloney.
Haaland Luton team 15 technique + 13 long shots + 13 passing = +147gd, 108pts, 1st (+142gd, 107pts, 1st second sample)
Haaland Luton team 15 technique + 13 all technicals except cro, long, pass = +159gd, 107pts, 1st
Haaland Luton team 13 all technicals except cro, long, pass, tech = +121gd, 97pts, 1st
So the passing did a lot better than crossing combo.
I did 15 technique + 13 long shots + 13 passing with the Haaland Luton team and got much better results than technique + long shots + crossing, which only got around +100gd.
But when I tested this with the normal meta team as 14 for tech + long + pass, results were 6th, 3rd, 4th, 6th, 10th = 5.8. Which is zero benefit.
So what we see here with these specific technical combos is that they do improve things if you're already absolutely dominating, but it's not really going come into the equation for realistic play.
ZaxanR said: Quick question - When searching for players using the attributes in the first post, is it better to leave them as they are but match 15/16 or 14/16 or even down to 12/16 - or to reduce the value of all attributes by 1 until you find a player that matches the filter?
For example, it's really quite hard to find a CD with 13 dribbling, but 10 pops up easily enough. Or would the better CD be one who has all of the other stats but only 4 dribbling? Expand There's no real right or wrong answer, but I'd probably go with reducing values by 1 until you get options, because perfectly balanced/proportional players seem to do well even with low values (i.e. ~12).
A problem is that the attribute figures given are hard minimums, as the whole template is pushing the envelope of what's possible, and it's not really known how well it scales going down the leagues. However I have done a bit of testing with lower leagues, and it does seem to work scaled down.
With dribbling in particular btw, I have actually tried reducing the template to around ~10 for dribbling recently, because it didn't get high weight from the real player comparisons. However it simply did not work. I also tried a bunch of other minor alterations to other attributes, and similarly got dud results. So I left it alone after that, and it means the template really is quite brittle. You can't get away with a minor subtraction here and there.
If dribbling is really the only problem, not overall quality, then I would suggest perhaps compensating by looking for higher pace/acc or higher key mentals such as anticipation and work rate. Maybe that would offset the loss, is my guess.
EBFM tested it and found it did pretty much nothing
I guess the finding hasn't been updated for FM24 though, which has changed options too.
Not sure if anyone else has tested it. I doubt it since you can't set it all up in advance. Might be worth looking at down the track, but it likely has negligible effect.
It works by exporting out of RAM the entire NEXT_FIXTURE object and then passing that as an argument in to the FM's GAME_MATCH__Run function (which it does via an injected DLL). And that works and you can run it multiple times with the same inputs but only changing the seed and it works great.
One thing I noticed about repeated edits of players and dumps of the next-fixture blob are that the game generates role ratings for each player across all 22 roles, except it uses the lower value of either itself or the player's adjusted CA (CA but modified slightly by Important Matches, Consistency, Determination and Natural fitness (which are all worth about 0.2 CA per point). I saw Ollie Watkins' CA go from 144 to 149 (his real value is 146)).
These roles are calculated using the attributes the game highlights for you on player screens, so Pace only appears in something like four of them, not almost all like you'd actually expect.
Changing these derived values makes no impact on results; changing raw attributes does. I suspect these role ratings are used for AI player selection (because I drastically altered some and the players slipped out of the opponent's starting XI).
There's another value that changes whenever you modify your attributes and regenerate a fixture and I'm calling that the Leadership Value. It's a number from 1 to 4,406 and its weighted by these attributes: Leadership 184.92+-127.02, Teamwork 1.20+-0.68, Work Rate 1.05+-0.69, Consistency 0.60+-0.35, Important Matches 0.55+-0.33. I couldn't tell you what it does though. It looks like how you'd rate a captain's ability but I've not seen where it's used.
Also, positional familiarity has a cliff. At 12 and above, it contributes about 1-2 points to the role ratings described above (which, again, make no difference to actual performance). At 11 and below it collapses:
John McGinn's trquartista rating goes from 113 at familiarity 12 to 47 at 11 and 44 at 10. Below 10 he gets dropped out of the starting XI altogether.
Consistency, Determination, Important Matches and Natural Fitness only make a difference between 5 an 14. They shift the ratings by about 2-3 points at the extremes.
I've not tested yet the impact of these things on actual performance, just expected performance so far.
Good work man
Sounds promising
With position proficiency, I reckon that might have something to do with the fact that the CA weighting calculation also changes around proficiency ~10-12. I.e. at 13 ST proficiency it would use ST weights for attributes, but at 10 proficiency it would use DM weights if his main position is DM.
The leadership -5 = -0.35 goals finding is an odd one. Tempted to give leadership another test already to check if there's something in it. Anticipation also doesn't seem right.
The data seems to reveal what attributes feature in contests and which don't. For example:
-5 pace to Aston Villa = -0.59 goals for Aston Villa, +0.40 goals for Bristol. So pace is a contest.
-5 determination to Aston Villa = -0.28 goals for Aston Villa, +0.01 goals for Bristol. So determination is not a contest at all.
Composure, leadership, dribbling, are some more examples of non-contested attributes.
-5 dirtiness is +0.24 goals to Aston Villa, -0.08 goals to Bristol. This suggests that only a quarter of the downside of dirtiness is what you concede, the majority of dirtiness actually prevents you from scoring goals.
By contrast, low aggression mainly results in more goals conceded rather than less scored. In fact you score more with lower aggression, but also concede more than what you gain in scoring, making high aggression a net benefit in spite of less clean sheets so to speak.
Passing, decisions, long shots, technique - lower values reduce both teams scoring output. Maybe this helps to explain why I've observed significant g/d differences with these attributes once you start reaching very high performance levels (i.e. all games won, +150 g/d). Perhaps when you're winning games regularly 5-0, the extra +10 to a few technicals are culminating in an extra +1 g/d or so per match, which would be +38 g/d over a Premier League season.
Might be reading into this too much given the confidence interval, but gives an idea of what we might be able to deduce from more of this data.
I can share this if people think there's a use-case for it, but I'm going to be cracking on with my digging regardless.
Are you saying that the match result is partially determined by the system time?
I've wondered if this is the case, from seeing Paradox do it with their saves
433 Mars Attacks V4 (+custom variants) 🏆🏆🏆🏆🏆
EF 424 IF HP V2 P101 AC (+custom variants) 🏆🏆🏆
I will refer to 'V4' as 'V7', as it is actually named V7 in the file. On the site, it is V4.
I didn't record the data neatly and most of my samples are for variants not mentioned here, so there's more to it that I will talk about in my notes below, but this data provides you with some direct comparisons.
To give you some bearings, at Rushall 'FM24.4 WARPIGS 4231 RM P111 CC' placed 20th and LxS 433 placed 6th.
V7 Variant A:
Rushall - 2nd(96pts), 4th(83pts)
Luton optimized selected - 5th, 2nd, 4th, 5th, 5th, 5th, 4th, 1st(85pts) = 3.875
Man City - 1st(87pts,83gd)
V7 Variant B:
Rushall - 2nd(103pts), 2nd(94pts), 3rd(91pts)
Luton - 4th, 5th, 5th, 5th, 6th, 3rd, 6th, 5th, 4th, 5th, 6th, 1st = 4.583
Liverpool - 1st(101pts,114gd)
Man City - 1st(100pts,106gd), 1st(101pts,105gd)
Full Haaland Luton - 1st(103pts,152gd)
V7 default:
Rushall - 2nd(100pts), 2nd(97pts), 2nd(88pts)
Luton default unselected - 4th
Luton optimized unselected - 2nd
Liverpool - 1st(99pts,117gd)
Man City - 1st(100pts,100gd), 1st(107pts,113gd)
Full Haaland Luton - 1st(110pts,137gd)
EF 424 IF HP V2 P101 AC:
Rushall - 1st(95pts), 4th(83pts), 5th(81pts)
Luton default unselected - 4th, 5th, 8th, 4th, 7th, 7th = 5.833
Luton optimized selected - 3rd, 4th, 1st, 8th, 4th, 3rd = 3.833
Man City - 1st(101pts,124gd) - taken from FM Arena tactic screenshot
Full Haaland Luton - 1st(105pts,147gd)
424 Classic v164:
Rushall - 3rd(88pts)
Luton optimized selected - 2nd, 2nd, 2nd, 7th, 2nd, 4th, 5th, 3rd = 3.375
FM24.4HGF4231 V3X RM P107 ALL CUPS:
Rushall - 2nd(88pts)
Luton default unselected - 3rd, 6th
Luton optimized selected - 4th
PERFECT STRANGERS 42112 WIWBA P107 EF:
Rushall - 1st(96pts), 4th(88pts)
Luton optimized selected - 2nd
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Notes:
The 433 V7 tactic is neck and neck with the Knap tactic. By virtue of the fact that it's a different formation to the 424, I was planning to upload my own variation, but it turned out that default V7 is about as good as the best variations I tried. So I simply credit the author.
Now the FM Arena testing has given V7 a dud score putting it on page 30. You can't see it in the data presented above, but what I noticed during testing was that V7 more temperamental than the Knap tactic. An unselected Luton could get 9th for example. Variant B did a good job of tamping this down I believe. My speculation is it's that there is more variance in the opposition's DCs (vs V7's extra ST) than their DL (vs Knap's AMR), and that the opposition quality is more likely to pip your own in the central areas in the Premier League.
Whatever the cause, the stability of Knap's tactic does matter, and so I would personally still stick with it over the V7, but the V7 does give another option, particularly if you lack decent wingers. What I've found through this testing is that team selection matters quite a lot. Again the data above doesn't fully show it, but I would often get consistent 2nds with Luton with the right team selection (you can see this with 424 Classic v164). Delegating to the assistant, as low as 6th-8th becomes much more frequent. So that's ~2nd-8th > consistent 2nd through team selection, which is bigger than the difference between many of these tactics that get uploaded.
As you can see from Variant A, sometimes a tactic can do great in one league and yet relatively poor in another. Both the Knap tactic and V7 are exceptional well-rounders. Generally speaking though, 8 or 9 times out of 10 the better the tactic, the better it does in all conditions. So I don't think the 'underdog' tactic is really a thing - and it's certainly not switching from 'attacking' to 'positive' or the like, which does nothing at best and ruins the tactic at worse. You have to keep in mind these tactics have already been refined to select the optimal mentality it performs with.
I found that 'Position/Duty/Role' in tactic familiarity makes no observable difference. You can get 0% in this if you don't make your player train in that specific role. Since specific role training is detrimental to attribute growth through training, be reassured you don't have to chose between them. 0% role familiarity is fine. This will also show up in your 'partnership' green lines on the tactic, which will say the role combination is suitable/unsuitable if you hover over it - again, this doesn't seem to matter, so ignore this.
The stars your player has for the role also doesn't seem to matter. Selecting the player with the highest % score in FMSS or Genie Scout is what matters. Leave the role as what the tactic dictates it is, as role changes do change the tactic effectiveness.
I had the idea that perhaps less instructions = more adherence/more familiarity = better results. So I gradually stripped down the V7 tactic until I ended up with this:
The result was 2 x 5th. I reckon it's probably true for other tactics as well, but it's still a slight disbenefit rather than a benefit. And we see with the Rushall results that it doesn't matter how crappy your players are, they handle the extra instructions fine.
I should also note that Diabolo4480 has uploaded a number of variations the same fundamental tactic, up to version 30. I haven't tested all of these, but I tried a few and settled on V7 as the best starting point to work with. V28 for instance is roughly equal, but not better.
England Unloaded 'Unimportant':
Year 1 - 138.75 median PA, 152.25 peak PA, 4.75 players
Year 5 - 131.125 median PA, 144.5 peak PA, 5.5 players
Average 134.94 median PA, 148.37 peak PA, 5.125 players
England Unloaded 'Very Important':
Year 1 - 150 median PA, 167 peak PA, 4.75 players
Year 5 - 127.375 median PA, 151.75 peak PA, 5.25 players
Average 138.69 median PA, 159.37 peak PA, 5 players
Here we see a 6.9% drop in peak PA and a 2.7% drop in median PA. The median PA drop is probably just due to the low sampling size. Number of newgens retained by clubs did not change.
Some significant caveats here are that I'm using a small database, am only testing England (which may be a specially treated nation), and things could further change when we get to year 10, 15, and so on. I will also say that when I tested Game Importance a few years back, I observed a steeper drop off in quality than just 8.3% after a few years. Perhaps though that happened due to other factors.
So this isn't the final word on game importance, but I think it's reasonable to simplify Game Importance as a flat 8.3% effect on peak PA and 0% effect on median PA, regardless of whether it's newgens from your own team, another team in your division, or a team in an unloaded nation.
Because it has no effect on the median PA, it doesn't come into the newgen calculation. Nonetheless it's something to keep in mind, if you are assessing USA ('fairly unimportant' ) say, or doing editing.
As I was looking for what other people may have tested and found, I came across this from the SI forums:
I concurrently player-managed all 20 Premier League teams for this test, to ensure I have the complete picture.
England Div 1 'Unimportant':
Year 1 - 122.1 median PA, 145.7 peak PA, 133.6 4th PA
Year 2 - 126.5 median PA, 149.7 peak PA, 139.9 4th PA
Year 3 - 127.8 median PA, 151.9 peak PA, 138.9 4th PA
Year 7 - 118.0 median PA, 143.8 peak PA, 135.6 4th PA
Average: 123.6 median PA, 147.8 peak PA, 137.0 4th PA
England Div 1 'Very Important' (Default):
Year 1 - 124.2 median PA, 161.1 peak PA, 140.6 4th PA
Conclusion: Game Importance 'Very Important' > 'Unimportant' = 0% median PA difference, -8.3% peak PA
This isn't the end of the story yet however. For the intake of your own team, the takeaway is game importance has a minor affect on high-PA newgens you get. But there may be different effects for intakes of unmanaged teams and unloaded divisions.
England Div 1 Unmanaged 'Unimportant' 4 team sample:
Year 1 - 127.37 median PA, 149.5 peak PA
Year 5 - 122.5 median PA, 148 peak PA
So it does not appear that player-managed makes a difference.
Next I'll see if loaded vs. unloaded division makes a difference.
BTW something curious happened on Year 7 of the first test - several teams had 19-24 players in their intake, rather than the usual 16:
Could be a bug. Or perhaps it has to do with something like player retirements, where some extra players are sprinkled in to make up for the retirements.
I doubt it is the number of teams, for two reasons:
1) As I mentioned in my post, the initial way I verified the factor (years ago) was by swapping England and Romania completely (all clubs, competitions, etc.) and observed a ~25% median PA change.
2) There are 1217 clubs in Germany and 56 possible active clubs. There are 4693 clubs in Italy and 100 possible active clubs. Germany has +10% median PA when all else is even.
That said, there is something going on involving club rep & youth facilities of more than just those in the active division. As with the two examples above, I can see some potential inroads through logical deduction.
In regards to your second question, I'm not sure what picture you have in mind exactly, but I will say that the nation average median PAs don't reflect an actual average median the nation maintains. It is only the average for the top division in each nation, in term determined by each club within the division.
For example, my artificial test of Singapore Div 1 has the average median PA at 73. But in real play, where the club rep and whatever else is significantly lower, the average probably drops to ~40 based on the Balestier results. It doesn't get fed through again to the nation/division factor to ensure it lines up with a table of numbers where the median Div 1 French club is 120 median PA.
It's a simplification therefore to say that France is 120 and Italy is 100, but this doesn't diminish its usefulness.
Thanks for the reply! Looking forward to seeing your final filter.
To at least have a decent chance of reaching the essentials of a top player you would need:
11 acceleration, concentration
10 pace, anticipation, determination
9 natural fitness
8 stamina, jumping reach, dribbling, aggression, composure, work rate
6 balance
5 agility
However very few age age 15-17 newgens can meet that criteria.
To make it viable, as well as easier to filter, I recommend:
7 aggression, anticipation, composure, concentration, work rate, dribbling, determination, acceleration, jumping reach, natural fitness, pace, stamina
10 professionalism, pressure, consistency, important matches
10< dirtiness
>100 PA
Age 15-17, but also consider age 18 players
In my test save, there still weren't that many results, but you only need a few each year. You could drop it the visible attributes to 6 if need be, but I wouldn't go any lower than that.
If you can't buy anything at 6, say because you're a lower league club, it's best to then just focus on high pace/acc/sta players. That would be 11+ pace/acc and 7+ stamina starting. I wouldn't even add anything else to it, as things like work rate can be trained up adequately and hiddens such as pressure & important matches matter less in lower leagues.
Each year about 7 players will need to graduate from the u18s, so if we assume a squad size of 21 players. If 3 of these are from intake, that leaves 4 for purchases. So we only need a few viable options to be available.
Of course there's the option of loan farming 100 u18 players, but players on loan won't train correctly, so this can be considered as an extra rather than alternative.
I was thinking of just mentioning a few attributes that are difficult/impossible to improve, as I'm not really interested in 16-year-old filtering. My thinking is that you can mold almost any newgen into what you want, so long as they have high PA.
But actually it's more interesting the more I think about it. There are very limited spots for first team experience, and it's too easy to stack the youth squads with too many players causing injuries through low match fitness & high workload on training staff, which reduces the growth all around. Being able to filter even those mid/high PA newgens would be useful.
Rather than answer this right now, first I'll do a test to update my knowledge of what can realistically be achieved in training.
Luton 1st with custom tactic, non-424:
I also got another 1st result with the Knap EF 424 IF HP V2 P101 AC tactic
These two tactics are the only ones to reach 1st so far
Some more I tested/retested:
424 Classic v164 - 2nd
Meta 433 - FM DNA - 6th
Knap WOF 25 424 MT P104 EF - 4th
It would have to be that it's set for division or nation, otherwise a division 7 club could never improve to a high level. But then how would it determine the ranking of specific clubs, as we see in the actual results?
I realized what instead it could be is the position the club is at in the league at the time of intake. I've done some tests with Luton so far that seem to show this is the case:
Luton 19th/20th = 124, 111.5, 115, 109.5 = 115 average
Luton 12th = 122, 107 = 114.5 average
Luton 5th = 143.5
Luton 4th = 129.5, 133, 125 = 129.16 average
Luton 2nd = 135.5, 135 = 135.25 average
Note this does not mean there is not a hidden factor, as the 1st team in Gibraltar is still worse than the 20th team in the EPL, when all relevant visible factors are equal. But it does explain another piece of the puzzle that can be added to the factors list.
This is still compatible with the team PA list hypothesis in this way: Rankings (i.e. 3rd, 4th, 5th, etc.) in a division could be assigned PAs, and who ranks where is determined on intake day by the actual competition data.
Something else I noticed is that the peak PA also seemed to change, from ~130s for 20th to ~150s for 2nd. What stands out to me about it actually is that the PA range always seemed quite compressed. Usually with very high median PA teams one should get the random 178 or 192 PA newgen. But not here with Luton, where the peak is consistently quite low - 158 or 159 or even 148. Makes me suspect that factors such as Youth Recruitment and perhaps Youth Rating are solely responsible for those very high PA newgens.
Edit: I do not find the same for Balestier in Singapore:
Balestier 1st = 19 (75 peak), 32.5 (84 peak)
Balestier 9th = 31.5 (74 peak), 33 (58 peak)
Balestier artificial 3rd = 80.5 (92 peak)
Balestier artificial 9th = 82 (99 peak)
This doesn't mean the Luton case is invalid, but it means that we cannot assume other divisions have a similar position effect. We can say it's true for the English Premier League. It's possible that Singapore simply has identical PAs set for its ranking list, but that is just one of a whole bunch of possibilities, so we cannot assume that.
I thought initially it's just that these clubs are the ones most likely improve their facilities/rep in those few months, or that perhaps continental competition qualification plays some small role. But then I saw this also happened for a short ~4 month wait until intake, and with nations that don't really have continental competition/qualification of note.
My hunch is that each team actually has a set target PA as part of the hidden factor. I know that sound a bit far-fetched or convenient, but consider the following:
1) From this guide for Italian volunteer researchers for FM20, they are instructed to match a list of average CAs for each team & division:
So it's evident the SI team are already going to these lengths even before the volunteer researcher data comes in. Rather than a general nation or division factor, it could simply be these values are being given a weighting in the outcome.
2) This is speculative, but it makes sense to me from a game balance and narrative perspective why they would include this in the formula. This way, if Liverpool somehow bomb out a few years into the game, they are somewhat cushioned from the prospect of relegation, and even if they are relegated, it allows for them to bounce back. Or if they neglect their Youth Recruitment which we know is very competitive in England and drops with time without investment, then this helps to keep them propped up as a source of quality youth.
Now separate to all that, there are two other things that are relevant to my formula but not included in the formula:
1) Game Importance. This is a difficult factor to measure because it seemingly affects distribution more than median PA, and it also seems to have an effect that reveals itself over several seasons rather than just one (which may carry a more pronounced effect on median PA as time goes on). Thankfully this isn't a factor you can improve in-game, and most of the best nations have the highest game importance anyway, so it's not a huge deal if we set it aside and simply say only expect good newgens from 'very important' nations if you want to be on the safe side.
2) In this thread I detail a curious impact I found in regards mainly to Club reputation and Youth facilities of other clubs within the nation. Club Rep 1 of all other clubs results in ~10% less median PA, while combining it with Youth Facilities 1 of all other clubs somehow cures it completely. Ordinarily I have found that club rep has no effect on PA, aside from Youth Recruitment tie-breaking. The underlying logic to all this remains unclear. Overall, I felt it's not necessary to complicate the formula with this. The point of having a formula would be to quickly deduce things with minimal inputs, and if its ~90% accurate, I reckon that's good enough.
I add a manager for each team before intake day, so 16 players per team x the number of teams in the division.
It occurred to me during the process of getting the data that perhaps the hidden factor is merely to do with the number of teams in the division. After all, Singapore & Switzerland & Scotland have fewer teams than the England Premier League, but Italy has 20 and Argentina has 28, so this doesn't explain it.
It's equally valid I think (based on actual past experience) just to take the median of non-managed teams, even if they only retain say 7 of the newgens, but I wanted to make sure + distinguishing between existing & newgen 16 year olds can be painful that way.
Appreciate your efforts to test those things, but for hardcore players is not a secret newgen mechanism. What was a surprice, before 2020; was about Youth Facilities - not affecting PA. There was a vast majority of people thought that Youth Players was Training in Youth Facilities, which is not true.
Not sure what you're saying exactly
If you mean that those 4 factors are what I am identifying, that is not the case.
Of those, only game importance has any effect (at least on PA, but I daresay CA too), and game importance has little effect on median PA. It has a unique effect on the distribution curve, but let's put that aside here.
You make a point of saying these 4 factors are fixed, based on that SI post. I suspect you're taking it to mean that it's fixed, as in it stays the same in spite of whatever value you enter in the editor. This is not true, as game importance can be changed with observable effects. What he would mean by 'fixed' is that it doesn't change once you've started a save.
Also it wouldn't make any sense anyway:
France - 19 economic factor, 17 financial power, developed state, 'very important', 155 youth rating
Germany - 19 economic factor, 20 FA financial power, developed state, 'very important', 155 youth rating
How then does France do 10 PA better than Germany on average, when all relevant competition & club factors are identical?
As far as I'm aware, no one else has even suggested this hidden factor I've identified exists, and we see it is significant - the difference between even Italy & England wipes out a 13 difference in Youth Recruitment, judging by the approximate formula.
I forgot to add that to the control list, but yes, it was actually controlled for. I'll add it in. I set competition reputation of all Level 1 divisions to 130.
I don't have competition reputation on my list of factors, which means it shouldn't have any effect on PA. I can't actually remember this, since I tested it back in 2020, but I can confirm my 2020 notes say it has no effect on PA. Note that CA works a bit differently.
Country population has no impact - we know it can't be a hidden factor, because there are obvious counter-examples and rationales we can infer: China and India have much higher populations than England. If population mattered, even a little in regards to certain cases, it would make a real mess of things.
Further suggestive evidence for this is that cities and regions don't matter, and turn out to be entirely cosmetic under careful examination.
All the talk you've heard from SI staff about it taking into account economic factor, youth facilities, players being recruited from nearby cities, etc. is baloney.
Haaland Luton team 15 technique + 13 long shots + 13 passing = +147gd, 108pts, 1st (+142gd, 107pts, 1st second sample)
Haaland Luton team 15 technique + 13 all technicals except cro, long, pass = +159gd, 107pts, 1st
Haaland Luton team 13 all technicals except cro, long, pass, tech = +121gd, 97pts, 1st
So the passing did a lot better than crossing combo.
I did 15 technique + 13 long shots + 13 passing with the Haaland Luton team and got much better results than technique + long shots + crossing, which only got around +100gd.
But when I tested this with the normal meta team as 14 for tech + long + pass, results were 6th, 3rd, 4th, 6th, 10th = 5.8. Which is zero benefit.
So what we see here with these specific technical combos is that they do improve things if you're already absolutely dominating, but it's not really going come into the equation for realistic play.
Will be interesting to see what results for attributes he gets
For example, it's really quite hard to find a CD with 13 dribbling, but 10 pops up easily enough. Or would the better CD be one who has all of the other stats but only 4 dribbling?
There's no real right or wrong answer, but I'd probably go with reducing values by 1 until you get options, because perfectly balanced/proportional players seem to do well even with low values (i.e. ~12).
A problem is that the attribute figures given are hard minimums, as the whole template is pushing the envelope of what's possible, and it's not really known how well it scales going down the leagues. However I have done a bit of testing with lower leagues, and it does seem to work scaled down.
With dribbling in particular btw, I have actually tried reducing the template to around ~10 for dribbling recently, because it didn't get high weight from the real player comparisons. However it simply did not work. I also tried a bunch of other minor alterations to other attributes, and similarly got dud results. So I left it alone after that, and it means the template really is quite brittle. You can't get away with a minor subtraction here and there.
If dribbling is really the only problem, not overall quality, then I would suggest perhaps compensating by looking for higher pace/acc or higher key mentals such as anticipation and work rate. Maybe that would offset the loss, is my guess.