EvensenFM said: This is with the vanilla match engine, correct?  I'm curious to know whether the new FM Match Lab means "meta" tactics are no longer so important. Expand

I have tested four different engines (two from FM Match Lab and two from the "playgm" forum), and the top 8 most important attributes ranked at the top 's Ranking of importance will not change.

For those at the back, after replacing the engine, their impact is still small, but the order of importance (depending on different changes) may change

I uploaded the part of the test that I didn't finish
This can be referred to, but the sample is insufficient

https://pixeldrain.com/u/BBUqQPjh
Or
https://pan.baidu.com/s/1DtMXrbVhJHt2JSv42hv1aQ 提取码: umu7

Orion said: I know it's not always THAT simple but can we make a rule of thumb that many 'semi-decent' tactics (or just self made) will improve significantly just by turning on 'Get stuck in' and probably using for most of the team Player's Instruction 'Tackle Harder' - since most meta tactics use both of these features?

So if your tactic is not total garbage that doesn't make sens, just tick press more, get stuck in, tackle harder and you're ready to overperform. Expand


can we make a rule of thumb that many 'semi-decent' tactics (or just self made) will improve significantly just by turning on 'Get stuck in' ...... 'Tackle Harder'...... press more, ......

——Yes, you're right.
Because at least the results of my test do not show any results that opposite to this rule , So I can come to such a conclusion .
That is to say, some instructions/tactics, (due to the imperfection of the game engine), naturally have a major disadvantage, while others naturally have a major advantage





Then in fact, there are still some unknown things that have not been solved.
But This test consumed too much time , so I don't have the enthusiasm and time to continue for now.
I need to have a rest and play for some time
And I haven't been to the forum for a long time. I found a large number of replies and private messages. I'm sorry, but I don't have the energy to reply now

1.Position Proficiency位置熟练度





This has little to do with the main theme, so I present it independently
这个和主题关系不大，所以我独立的将他摆出来


(Test Condition setting and Preparation Section)
（测试条件设置和准备部分）
↓

2.The primary purpose of the test is to solve a previously hard-to-explain problem:
Why does an increase in attributes (under some given conditions) lead to a decrease in goal difference/winning rate instead?
Why does the reduction of attributes (under some given conditions) lead to an increase in goal difference/winning rate instead?

测试的最主要目的是解决一个之前难以解释的问题，为什么（特定情况下）属性的增加，反而导致净胜球/胜率的下降？
为什么（给定条件下）属性的减少，反而导致净胜球/胜率的增加？


3.According to the principles of science. I hope to be able to control variables, facilitate observation measurement and statistics, be reproducible in the same environment, and have a sufficient sample size to eliminate random errors.
However, I have to sacrifice a certain degree of universality (that is, testing under more different preset conditions), and because I lack software tools, knowledge related to automation testing and high-performance computers to do so.

But, even if not entirely rigorous, this is still more reliable than the vast majority of "empiricism".
And, due to time constraints, I am also unable to produce a very large number of samples.
The lack of sufficient samples will weaken the credibility to a certain extent. However, if the same situation occurs multiple times and the distinction is obvious, it can prove that this is credible.

根据科学的原则。我希望能够控制变量、方便观测测量统计、同环境可重复再现、有足够的样本量来排除随机误差。
而我必须牺牲一定程度的普适性（即在更多不同的预设条件下测试），因为我缺乏软件工具、自动化相关的知识和高性能的电脑来这样做。

但是，即使不完全严谨，这也比绝大部分“经验主义”可靠。
而由于时间关系，我也没法做到非常大量的样本。
缺少足够样本会一定程度削弱可信程度，但是如果同样的情况多次出现并且区分很明显，可以证明这个是可信的。

4.One of the leagues being tested comes from "DZEK" on the forum. One of the league archives I modified, the download link is:
https://pixeldrain.com/u/7vaWxyAC

测试的其中一个联赛来自论坛的“DZEK”。我更改后的联赛的存档中的其中一个，下载链接是：https://pixeldrain.com/u/7vaWxyAC

Part of the test tactics used is:https://pixeldrain.com/u/Smeh7B9e
Part of the test data is:https://pixeldrain.com/u/KuemeJ9R

使用到的测试战术的一部分是：https://pixeldrain.com/u/Smeh7B9e
测试数据的一部分是：https://pixeldrain.com/u/KuemeJ9R

5.Thank you to the players of the "Bao Peng" forum for reverse engineering the game engine (have pictures in previous post).

According to his achievements,he crack the attribute is "propensity (number of attempts)" + "success rate".———— And if this is true, because "trying" or increasing "attempts" is not always a good thing, in some cases it is bound to have much more negative effects.
And if this logic holds true, this phenomenon will be clearly visible in many situations. In different circumstances, different attributes will be increased, which will have a negative effect on the team's performance.

感谢“爆棚”论坛的玩家对游戏引擎的逆向工程（我在之前的帖子里有图片）。
根据他的成果，属性是“倾向性（尝试次数）”+“成功率”。————而如果这个成果是真的，因为“尝试”或者增加“次数”并不总是好事，在一些情况下它必然会导致负面效果。而如果这个逻辑成立，这个现象会在很多情况中清晰可见。在不同的情况下，将有不同的属性被增加后，对球队的表现会是负面效果。


For example, a high "passing" attribute = "prefer passing more attempts" (and to some extent ignore your tactical instructions) + "Higher passing success rate"

比如“传球”属性高=”更喜欢传球“（并且一定程度无视你的战术指令）+”传球成功率更高“

It is possible that high attributes led the player to go against the correct tactical choices and do what he likes. Although his success rate in doing this was higher and his post-game rating might have been higher as well, it hurt the team. (And vice versa)

可能：属性高导致了球员违背了正确的战术选择而去做他喜欢做的事情。尽管他做这个事情的成功率更高了，他的赛后评分可能也更高，但是这伤害了球队。（反之亦然）


(Test Condition setting and Preparation Section)
（测试条件设置和准备部分）
↓

6.Early test
Using FMRTE to fix various variables and create a test condition, I first try to see if I can directly observe some results that conform to the theory
使用FMRTE固定各种变量，并创造一个测试条件，我首先尝试能不能直接观察到一些符合理论的结果


Playing the same opponent 120 times under the same conditions, the player's team is much stronger than AI under the set conditions
与同一个对手反复比赛60场在相同的条件下，在设置的条件下玩家更强

Test result
↓

Then, all the the player on player's team simultaneously added 10 attributes such as passing, Finishing and tackling, and played another 60 games.
It was found that in the group with higher shots, the ratio of "actual goals" to "expected goals" increased by nearly 25%, making "expected goals" almost equal to "actual goals".
However, the number of shots and the number of shots on target did not increase significantly, nor did the number and success rate of passes and tackles increase significantly
I guess this set of data might be due to the fact that the player's team is too strong while the AI's team is too weak, so that the player's data has reached the upper limit of some kind of "soft limit", making it impossible to distinguish.
然后，全队球员同时增加了10的传球、射门、抢断等属性，再比赛60场。
发现，射门高的一组，“实际进球”/“预计进球”增加了接近25%，让“预计进球”几乎等于“实际进球”
但是，射门次数和射正次数并没有明显增加、传球和抢断的次数和成功率也没有明显增加
我猜想是这组数据可能是玩家球队太强而AI的球队太弱，以至于玩家的数据已经到达了某种“软限制”的上限，所以无法区分。


7.I changed the test conditions to make the attributes of both sides basically the same.
我更改了测试条件使双方属性基本相同，




This time, taking the addition of "Shots Finishing" as an example, the added player had more "Shots Attempted" and "Shots On Target".
But he will "snatch" some shooting opportunities from other players

这次结果，以增加“射门Finishing”为例，被增加的球员有更多的“Shots Attempted”和“Shots On Target”。
但是他会“抢走”一部分其他球员的射门机会

This result fits with the above theory, attribute is "propensity (number of attempts)" + "success rate".
这个结果符合上面的理论，属性是“倾向性（尝试次数）”+“成功率”


(Test Condition setting and Preparation Section)
（测试条件设置和准备部分）
↓

8.I began to modify the test league to make it closer to the actual tactics of the AI head coach in the actual game.
The reason is that I found that all the  AI head coach 's "tendencies" in the original save have been completely removed,
and many of the "Formations" selected are tactics that AI coaches would never choose in actual games.

我开始更改测试联赛，让它更接近实际游戏里AI主教练的实际战术。
原因是我发现原来的测试联赛中的设置战术中的““tendencies””被全部清除了，而选用的“Formations”很多都是实际游戏里AI教练完全不会选择的战术。



Open FMRTE and you can read the panel of each AI head coach, which is divided into two parts. Among them, the "Formations" and "tendencies" on the second page are the main parts that have a significant impact.
打开FMRTE，可以读取每个AI主教练的面板，分成2个部分，其中第二页的“Formations”和“tendencies”是起主要影响的部分。

Tests have found that if these two parts of the two AI head coaches are swapped, under the control variable, their overall performance will also be swapped. This indicates that this information of the AI head coaches dominates all their behaviors in the game
测试发现，如果将两个AI主教练的这2个部分调换，在控制变量下，他们的整体成绩也会调换，这说明AI主教练的这些信息就是主导了他们在游戏里的一切行为

I selected 15 head coaches (the head coaches of 15 major teams at the beginning of the game) as the subjects of the test. On the player's side, they are free to choose the tactics I have selected. The attributes of both sides are exactly the same, and the attributes of each player are also the same. Moreover, the attributes of players are not distinguished based on the responsibilities of their positions and roles, thus avoiding the influence of bias and also preventing the impact of some "overly strong attributes"
我选择了15个主教练（15个主要球队在游戏开始时的主教练）作为被测试的对象。而玩家这边则是自由选择我选定的战术。双方的属性是完全一样的，每个球员的属性也是一样的，并且不按照位置角色职责来区分球员属性，从而避免倾向性的影响，也避免一些“过度强势属性”带来影响

These 15 head coaches (and their corresponding teams) are respectively:
这15个主教练（和他们对应的队伍）分别是：

XAVI哈维
Barcelona巴塞罗那

CARLO ANCELOTTI安切洛蒂
Real Madrid 皇家马德里

DIEGO SIMEONE西蒙尼
Atletico Madrid马德里竞技

LUIS ENRIQUE恩里克
Paris Saint-Germain Football Club巴黎圣日耳曼

MARCO ROSE罗斯
RasenBallsport Leipzig e.V莱比锡红牛

THOMAS TUCHEL图赫尔
Bayern Munich拜仁慕尼黑

EDIN TERZIC特尔季奇
Borussia Dortmund多特蒙德

STEFANO PIOLI皮奥利
Associazione Calcio Milan AC米兰

SIMONE INZAGHI因扎吉
Inter Milan 国际米兰

MASSIMILIANO ALLEGRI阿莱格里
Juventus尤文图斯

PEP GUARDIOLA瓜迪奥拉
Manchester City曼城

ERIK TEN HAG泰恩哈格
Manchester United曼联

MIKEL ARTETA阿尔特塔
Arsenal阿森纳

KLOPP克洛普
Liverpool Football Club利物浦

MAURICIO POCHETTINO波切蒂诺
Chelsea切尔西


每组测试若干赛季。得到这样一个表格。
如果你需要这些战术的下载，链接是：https://pixeldrain.com/u/Smeh7B9e
Each group is tested for several seasons. Obtain such a table.
If you need to download these tactics, the link is:https://pixeldrain.com/u/Smeh7B9e

Test result
↓


Simply looking at it, if the table is in blue, it indicates that this tactic is an advantage for this AI head coach.
If it is red, it represents a disadvantage.
Therefore, if a row of AI head coaches is all red, it indicates that he is very difficult to deal with. His tactics and "tendencies" are very much in line with the "META" of game engines.
简单的看，如果表格里是蓝色，那说明这个战术对这个AI主教练是优势，
如果是红色，则代表是劣势。
因此如果一个AI主教练的一列都是红色，那说明他很不好对付，他的战术和“tendencies”非常符合游戏引擎的“META”


You will notice that the intensity of the tactics of several AI head coaches is even on par with the player's "best of the best" tactics (indicated in red in the table). It even has an advantage over some of these AI head coaches.
While some other head coaches can find that almost all their tactics can defeat him by a large margin.
This indicates that some of these AI head coaches are actually using the "Meta" strategy

你会注意到，几个AI主教练的战术的强度甚至是和玩家“最好中的最好”的战术打成平手（在表格里是红色表示）。甚至对于其中的一些AI主教练有优势。
而另一些主教练，可以发现几乎所有战术都能以大优势赢他。
这说明其中一些AI主教练实际在用“Meta”战术


Among them, the best-performing head coach is:

MARCO ROSE
From RasenBallsport Leipzig E.V. RB Leipzig

DIEGO SIMEONE
From Atletico Madrid

STEFANO PIOLI
From Associazione Calcio Milan AC

其中，表现最好的主教练是：
MARCO ROSE 罗斯 来自 RasenBallsport Leipzig e.V 莱比锡红牛
DIEGO SIMEONE 西蒙尼 来自 Atletico Madrid 马德里竞技
STEFANO PIOLI 皮奥利 来自 Associazione Calcio Milan AC米兰


You will also notice that two of the tactics are the "Presets" tactics
They can only draw with some of the "worst" AI head coaches, such as "PEP GUARDIOLA"
你还会注意到，其中有2个战术是“Presets 预设”战术
他们仅能和一些“最差”的AI主教练，比如“PEP GUARDIOLA”打平手

9.The vast majority of the "Presets" tactics can be clearly seen in the table.
In fact, they are all at a disadvantage or an absolute disadvantage.
Because "Preset GegenPress 4231" in the table is already the best one among the "Presets" tactics.
而绝大部分的“Presets 预设”战术，在表格里可以明显看到，
实际上都处于劣势或者绝对的劣势，
因为表格里的“Preset GegenPress 4231”已经是“Presets 预设”战术里最好的一个了，


The performance of other "Presets" tactics was even worse
The table is another independent and earlier test used to compare the strengths and weaknesses of different "Presets" tactics
其他“Presets 预设”战术的表现更差
表格是另一个独立的更早期的测试，用于比较不同“Presets 预设”战术的强和弱

If you employ the "Presets" tactic or some less powerful tactics, you will naturally be at a significant disadvantage when facing the previous "meta" head coaches
如果你使用“Presets 预设”战术，或者一些没那么强的战术，那么你对上一部分“meta”主教练的时候就天然处于非常大的劣势

Part of the test tactics used is:https://pixeldrain.com/u/Smeh7B9e
使用到的测试战术的一部分是：https://pixeldrain.com/u/Smeh7B9e



I also tested a lot of other tactics, and the results showed that if you don 't do the' extreme Meta ', say, I cancelled 'tackling -- Get stuck in',
These meta tactics, against opponents such as MARCO ROSE, DIEGO SIMEONE, and STEFANO PIOLI, have a "loss rate" that is 10-20% higher than the "win rate", that is, they are overwhelmed

我还测试了更多的其他战术，结果表面，如果你不做到“极致Meta”，比如说，我取消掉了"凶狠逼抢"，
这些meta战术，对手比如MARCO ROSE，DIEGO SIMEONE，STEFANO PIOLI，“败率”会比“胜率”高10-20%，即被压倒了


(Test Condition setting and Preparation Section)
（测试条件设置和准备部分）
↓

10.Main part
主要部分

I have fixed to using one of the tactics, 3421.Change one attribute each time and test with these 15 head coaches as opponents
我固定了使用其中一个战术3421，每次改变一个属性，用这15个主教练做对手来测试

Part of the test data is:https://pixeldrain.com/u/KuemeJ9R[/b]
测试数据的一部分是：https://pixeldrain.com/u/KuemeJ9R


Test result
↓



The red part indicates:
Although the attributes have increased, the team's performance has not improved significantly (neutral), or has even worsened.
红色的部分表示：虽然属性增加，但是球队表现没有明显变好（中立），或者是变更差了。

The appearance of red is common:
This is in line with our initial inference ,The attribute is "tendency (number of attempts)" + "success rate"
红色的出现普遍：这符合了我们最初的推断,属性是“倾向性（尝试次数）”+“成功率”



Much earlier, I focused on testing the attributes of different roles/positions/responsibilities, but the results were very chaotic. The occasional "negative" attributes simply couldn't be explained properly.
Because an "ineffective" attribute can be understood as the engine not referencing it, but a "negative" attribute cannot be explained

Using the original theories (some widely circulated strategies and videos all repeat these theories, but have their own opinions, but all cannot do without the "highlighting attribute of character responsibilities" provided by the official)
"Position XX, because YYY is a highlighted attribute, the YYY attribute is even more needed." This doesn't explain why, theoretically, everyone knows that players need the YYY attribute, but why adding YYY would lead to a decline in the team's performance.

在更早以前，我对不同角色/位置/职责的属性的侧重测试，但是结果非常混乱，偶尔出现的“负面”属性根本无法正常解释，因为一个“无效果”的属性可以理解为引擎没有引用它，但是一个“负面”的属性，就无法解释

用原来的理论（一些广为流传的攻略和视频都是重复这些理论，只是有各自的见解，但是都离不开官方提供的“角色职责的高亮属性”）“XX位置，因为YYY是高亮属性，更需要YYY属性”，这解释不通为什么理论上，人人都知道球员需要YYY属性，但是为什么增加YYY会导致球队表现下降。The result of the test can conform to the theory.
Your tactics, the individual Settings within the tactics, are what you expect the players to do in the game (but without changing the "success rate".

The attributes of the player are "What will this player actively do?" plus "If his attributes are high, it will increase his success rate." This kind of enthusiasm or attempts is not necessarily beneficial (it attempts too much or an untimely attempt); sometimes it is harmful.

测试的结果能够符合理论。
你的战术、战术中的个人设置，是希望球员在比赛里怎么做（但是不改变“成功率”）
而球员的属性，则是”这个球员会积极的干什么“+“他的属性如果高会给他增加成功率”。这种积极性不一定是有益的(过多的尝试或者不合时宜的尝试)，有的时候是有害的。

Is this the reason why, in my previous multiple tests, some attributes showed "negative"
这就是为什么，在我之前的多种测试中，一部分属性出现“负面”的原因


This table is the initial attribute from 10 or 12 in this test. Change to 18.
The increase in the resulting winning rate

It can roughly be used to compare the importance of attributes (some of which may be inaccurate. For example, the Work Rate is very, very important at the stage of 1-10).
这个表格是这次测试里从10或者12的初始属性，
改变到18，
导致的胜率的增加值，

大致可以用来比较一下属性的重要性（其中会有一部分不准确，比如说Work Rate在1-10的阶段非常非常重要）


11.In the “Champions League Final”, test "Important Matches"
And the difference between "Go on holiday" and "commanded by the player himself"

在“欧冠决赛”，测试“Important Matches”
以及"度假"和“玩家亲自指挥”的区别





Change the hidden attribute “Important Matches = 18” to “Important Matches = 1”
Then compare the grades.
There are a total of 3 sets of data, with 60 matches in each set

把隐藏属性“大赛18”改成了“大赛1”
然后比较成绩，
一共有3组数据，每组60场比赛

The results show that major competitions do have an impact on important events like the UEFA Champions League.
And the most influential factor is the ratio of "expected goals" to "actual goals"
结果说明：大赛确实影响“欧冠”这种重要比赛。
并且影响最大的是“预计进球”和“实际进球”的比

“
（预计进球，实际进球）是4.07，3.03（手打）和4.03，3.20（度假）
（预计进球，实际进球）是0.74，0.67（手打，皇家马德里）和0.79，0.67（度假，皇家马德里）
……
传球成功率，玩家86.8%比皇家马德里85.0%
抢断成功率，玩家79.4%比皇家马德里77.7%
（手打）
传球成功率，玩家86.4%比皇家马德里84.9%
抢断成功率，玩家82.8%比皇家马德里72.8%
（度假）
……
玩家射门，射正，射正率（都是平均）
31.7，13.4，42.2%
（手打）
玩家射门，射正，射正率
31.9，13.7，42.0%
（度假）
……
3.38，2.73（手打），0.77，0.77（手打，皇家马德里）
3.30，2.43（度假），0.88，0.77（度假，皇家马德里）
……
（预计进球，实际进球）是4.65，4.53（手打）和4.45，4.50（度假）
（预计进球，实际进球）是0.57，0.43（手打，皇家马德里）和0.68，0.53（度假，皇家马德里）
”

Here is a series of data that is rather troublesome to translate. Moreover, I didn't make the table well, resulting in a very messy table, so I ignored it
这里有一连串翻译起来比较麻烦的数据，而且我没有很好的制作表格导致表格很混乱，就忽略了

Then, an earlier test has already proved that "consistency" is an effective and well-performing attribute
然后，更早的一个测试，已经证明了，“稳定consistency”是一个有效的效果不错的属性

“Important Matches”is same , an effective and well-performing attribute
大赛类似，是一个有效的效果不错的属性

Then comes "Go on holiday" and "commanded by the player himself"
然后比较“度假”和“手打”
Players do not use "Shouting" (I don't know how it is translated into English) in the competition.
Those with yellow cards and injuries in the midfield were substituted
玩家在比赛中不使用“喊话”（我不知道翻译成英语是怎样）。
中场黄牌和受伤的换下

Simple conclusion:
简单总结

"Player Control" does indeed present you with more shooting animations, but the final expected goal and the actual goal will be exactly the same as "Go on holiday"
For example, in "Go on holiday", your team takes 20 shots and scores 2 goals. However, in "Player Control", it takes 30 shots but still scores 2 goals
“玩家控制”确确实实的给你呈现了更多的射门动画，但是最后预计进球和实际进球，会和度假完全相同
比如说，“度假”统计你的球队射门20次，进2球，但是到“玩家控制”就会射门30次，但是还是进2球

The condition of our test was "no Shouting".
And "Shouting" can boost the morale of players. So, without changing the tactics at all, the greatest significance of the "Player Control" is to boost the morale of the team and thereby increase the winning rate (because morale is very important).
我们测试的条件是“不喊话”。
而“喊话”会提高玩家的士气。所以，在完全不改变战术的情况下，玩家操作的最大意义就是提高了球队的士气从而增加了胜率（因为士气很重要）

Bentaygax said: So for an all around training schedule that dont lose to much of tehnicals/mentals what do you recommed ? Expand

1. First of all,
You already know that "training" is "distributional growth," and "Addtional Focus" is "distributional growth."

Second,
You already know that the loss of these attributes is actually converted to more valuable "Pace" "acceleration"


2. Then

For players with high PA, they can maintain the growth of "physical" attributes while having enough PA to ensure the growth of "tehnicals/mentals"

For players with low PA, they can only choose more valuable "physical" attributes

Their strategies are different


3. Because
"Addtional Focus" is "Forced Distribution" and "unlocks growth caps"


4. So
“Addtional Focus Quickness”
It is bound to rob some of the growth that should belong to "tehnicals/mentals"


5. And "Double Intensity"
It is a tool to reinforce the weight of "assigned growth.


6. Therefore, the way to not lose (or lose less) "tehnicals/mentals" is to reduce the weight assigned to "physical" attributes


7. As we already know, "Addtional Focus Quickness" and "Double Intensity" enhance the weight assigned to the "physical" attribute

You can choose to cancel one of them , to not lose (or lose less) "tehnicals/mentals"

8.
Then, various exercises in the training program that increase the "physical" attributes will increase the weight,
and you can optionally reduce their proportion , to not lose (or lose less) "tehnicals/mentals"


example :
[Physical]+[Match Practice]x2+[Overall]
(Since it also eliminated "Addtional Focus Quickness," "Double Intensity," assigned more weight to "tehnicals/mentals," and added a [Match Practice], Increase the proportion allocated to "tehnicals/mentals" .)

(If you don't want to lose that much "physical" attribute, you can choose to add back "Addtional Focus Quickness" or "Double Intensity" , like , [Physical]+[Match Practice]x2+[Overall]+[Addtional Focus Quickness] )

Bentaygax said: So what schedule would you recommend for a random club like Catania ?
cause i am lost in this conversation (it extended in 9 pages)
I want to have a schedule that will produce results Expand

"Full rest" training



In fact, when I use this method,

After 3 years and 3 months of training, starting from March 2023, with the "Chinese Football League 1 (second tier)" team (starting with only half star training facilities), by 2026, the average Chinese team of 70CA won the World Cup

The pace and acceleration of this game is very, very strong
For middle and low level teams, their players have low PA and can't take into account all attributes,

Then "Full rest" tries to distribute the attributes in pace and acceleration, which is the strongest attribute allocation method (of course, this method is too strong, and will affect your immersion to a certain extent).

At the same time, you can notice that the training results of young people are far better than those after the age of 23
Since I was able to win the World Cup with an average pace and acceleration of 70CA and an average of 18 players (major opponents averaged 170CA),

China's youth rating is only 60 , Italy has youth rating 144,

Then with the young players in Italy, you have a better chance of training after 3 years to get a lot players of pace and accelerate 18-20



[Rest]+Addtional Focus Quickness+Double Intensity
Or
[Recovery]x7+Addtional Focus Quickness+Double Intensity

It causes almost no injuries to your players in training, which means that your players avoid the time when they stop growing due to injuries, and they don't miss important league games.

At the same time, since there is no training program, you need to take care to schedule weekly friendly matches to maintain attributes like Sharpness

Yarema said: @harvestgreen22 If you are doing any more tests on training, could you check how quickness additional focus works if a player already has 20 pace and 20 acceleration? For example with a [Quickness]+[Match Practice]+[Attacking]+[Recovery]x7+[Double Intensity]+[Addtional Focus Quickness] schedule. Mainly interested if CA growth remains the same even though the attributes it targets have reached the maximum. Expand

Now that I remember it, here's what happened:
If the attribute has reached 20, The grow will still be assigned to this attribute,
So this part of the distribution is wasted.


10rmc10 said: What about pre-season training? Any tips of what to do? Expand

I will stay the same and play friendly matches twice a week

Milo said: @harvestgreen22 Do we know what match review does? I would like to add it for the main team?

Would this be bad for development? Expand

match review : I forgot about testing him. I guess you'll have no problem adding on top of what you already have. It shouldn't affect much


Robbo84FM said: Seeing as Concentration is one of the best Meta attributes what is the best way to target this attribute as no additional focus training targets it? Expand

There is no good way to add it (or I haven't found it yet) because there is no additional focus corresponding to it.

From a revenue perspective,

If you think the player's Physical classes attribute are good enough (like Pace, acceleration, Jump Reach, Agility... Already arrived 19/20)

Then you can choose additional focus to Add the Work Rate (or Dribbling or Anticipation), Which is very important
If the Work Rate or Dribbling or Anticipation is enough,
Then we can cancel the additional focus, Equalizing the distribution of attributes,  It naturally strengthens all attributes, including Concentration

Yarema said: Friendlies don't have the same reputation as league games, in fact they are really low - at least that's how I remember it (could be wrong). So more realistic scenario would be 10 rep for friendly maybe even lower, and league games 100+ Expand


The "reputation" in the table is indicates the reputation of the league in which it is playing , Nothing more (and I don't doubt that league prestige affects friendly matches)

All friendlies in the test were against "Very small reputation teams (foreign)" here




azsumnasko said: @animatron Unfortunately not really. Reduce the size of the squad and it he will be played.

@harvestgreen22 Have you tested with let's say 40,50,60 games (friendlies) per year? Do you think we can increase the gains if we have more games? Expand


While it is true that friendlies match can be played twice a week and 60 games a season, I have only tried 40 friendlies match at most in tests

Reason: I didn't continue to increase the number of friendlies match mainly because the schedule itself will have its own matches,
I can't schedule friendlies match on the same day of the match,
and I don't know how to make/build a blank match league, so I can only schedule so many matches because of the positions that have been occupied

can increase the gains if we have more games : yes ,conjecture based on the logic of its operation



animatron said: Is there a way to tell my U19 manager to play a specific player every single match? Expand

I don't know

Robbo84FM said: Ok thanks just checked that out so it's like

Pace / Acceleration
Stamina / Anticipation / Jump Reach / Dribbling
Balance / Work Rate
Concentration / Strength / Finishing Expand


This is my version where you can refer to the results of multiple people at the same time,
Different people start from different angles and initial test conditions, so they may get different results,
Mine may not be right.

Then, briefly, on the top table, the greener the number, the more important, and the larger the number, the more important

Robbo84FM said: Ok so you think all rest is also best for the first team? would this not have a negative affect on all the technical & mental attributes? Expand

https://pixeldrain.com/u/953xPdxx
In the long term (more than 4 years), strategies 1 and 2 (they both used different training programs in different years)
Can better use up the PA, so that players before reaching the age of 25 (26 = Age of The end of growth) they have more time to increase the CA to the maximum PA

But in the short term (4 years or less), strategy 3 (pure "full rest" provides the most "fighting power" (when in equal the conditions), so you can send these players to various match without worrying about their "fighting power" is not enough and drag down the team


and for the technical & mental attributes:
If from the perspective of the sense of gaming immersion, the lack of them will cause the UI is not very good looking (turn red and lowering technical & mental), and "insecurity",

but from the perspective of "combat power",
compare to Bad and Good
Bad:the loss of these technical & mental attributes
Good:increased more Pace and acceleration

the latter "Good" is much greater



axRayz said: I personally trust @Zippos test the most as it's something he's done for years.



In I17 and J17 (Training English 10), I observed slightly better meta growth with only 1 [Recovery] instead of x7, and in B21, I also saw [Resistance] perform above [Quickness] in the standard:

[Quickness]+[Match Practice]+[Attacking]+[Recovery]x7+[Double Intensity]+[Addtional Focus Quickness]

So, I wonder If, combining both observations, the following schedule is the most optimal for overall growth:

[Resistance]+[Match Practice]+[Attacking]+[Recovery]+[Double Intensity]+[Addtional Focus Quickness]

What are your thoughts? Expand

"I observed slightly better meta growth with only 1 [Recovery] instead of x7 "
I think this is a random error in the test, because I only tested everything in this list once.
They're actually pretty close


"
[Resistance] perform above [Quickness] in the standard:

[Quickness]+[Match Practice]+[Attacking]+[Recovery]x7+[Double Intensity]+[Addtional Focus Quickness]

So, I wonder If, combining both observations, the following schedule is the most optimal for overall growth:

[Resistance]+[Match Practice]+[Attacking]+[Recovery]+[Double Intensity]+[Addtional Focus Quickness]

What are your thoughts?
"

They are also very close, and some random error in B21 compare to A21 or the A21 upper one (Caused by the low number of tests , only test once)

you can look at the difference between B17 and C17, Resistance provides slightly more Strength (a less economical attribute) and Slightly less with some other attributes ,
in Pace and Acceleration , they are almost the same (If increase the number of test samples, reduce the random error)

So: they are all very good and all can be used

Juicebat said: I've been setting my U-20 training schedules to full rest double intensity and my first team schedules to v7. Is this ideal for overall growth?

Also do you recommend loaning out prospects for game time at the cost of not having control over training? Expand

https://fm-arena.com/thread/14456-corrigendum-for-previous-test-error-friendly-matches-actually-count-as-the-number-of-matches-playing-friendly-matches-can-increase-ca/

You can take a look at this. I just sent it


Jacko933 said: What’s the take away from this? What’s the best training schedule? Expand
Bafici said: I just figured it out.

[Defending from the Front]+[Double Intensity]+[Addtional Focus Quickness]

Is this the best one for the just meta attributes, not all rounded development. Expand

Robbo84FM said: I know fantastic work has gone into this and i do appreciate it but it's too much for my simple mind looking at all these charts and tons of post saying this and that, has anyone actually had in game success with what schedules for u18, u21 & first team and is it just worth focusing on the meta attributes or overall growth, thanks Expand


For most simplicity, you can just use
"All-Rest+[Double Intensity]+[Addtional Focus Quickness]"  (It is one of the most effective)

for all team , include u18, u21 & first team




plus30 said: does this training have an impact on the matches ? Expand

Fewer training programs,
Bad: It will be harder to maintain "match sharpnes" ,so it will have a Indirect effect
Good: Much lower injuries , much higher "overall condition"

In contrast, I would suggest increase "match sharpnes" or "Familiarity" by scheduling friendly games.
check this link
https://fm-arena.com/thread/14456-corrigendum-for-previous-test-error-friendly-matches-actually-count-as-the-number-of-matches-playing-friendly-matches-can-increase-ca/



Middleweight165 said: Do you think this no training strategy is optimal for a non league side? How long does the development usually take? I'll probably refresh my squad completely every 2 seasons so maybe its not worth it Expand

Since Pace and Acceleration are the strongest attributes in the game, using "The All rest" is a way to get your players to "the maximum combat effectiveness" in the shortest time,, so as long as you don't sell your players to your current competitors, you are earning ,

and that's the best as You guarantee that you can win the current game as much as possible, so that the player gets the reputation of winning the game, and the reputation can be translated into the price when selling




Middleweight165 said: Why do you say Recovery is not necessary? I may have missed it but what is the rationale for including 7 x recovery sessions in the original? Expand

1.
The role of Recovery itself:
Compared with rest ,
Bad: Increase less condition and Fatigue
Good: Reduce Sharpness by less , Reduce extra injury risk , Do not reduce Cohesion


2.
What Recovery does when assigning attributes:
It has the effect of "pulling" the attribute assignment more into the Physical class attribute.
However Its "pull" effect is weak (not like things like "[Quickness]", so once you have no room to put it in schedule , the lack of it doesn't matter much

svonn said: Thank you for that detailed response! Very interesting findings regarding the locking mechanism. I've also been using the same test setup, how many games / seasons is your sample size? Expand

In terms of the size of the standard deviation, about 10-15 seasons (30 games per season) can get a sufficiently accurate value ,
You can compare the accuracy of different sample sizes directly in that excel


Middleweight165 said: Does this information change anything about the important attributes for each position from the opening paragraph in the following post?

https://fm-arena.com/thread/14201-fm24-experiment-most-important-attributes-for-each-respecitve-positions-with-their-coefficients/page-1/ Expand

good , I'll write it down and look at it later when i have time, I'm busy in things of life right now

1.Before, I tested the "friendly matches" by banning matches. I noticed an anomaly when I did a comparison test, so I checked the archive of previous tests and found that the type ofsuspensionI had before was wrong


The correct situation is:
Any player, playing in U18s, U23s, friendlies, official leagues, international competitions... :

All games counted as "matches" (regardless of whether it is a league of any prestige ,and How many prestige the  opponent have ) will allow players to "grow more" .




2.Two test groups, each with 11 coaches, each responsible for 1 type of training (set in the coach page)


Set all coaches to half stars (all abilities of all 11 coach are = 1) and compare all coach abilities to 5 stars (abilities = 20) :

I only tested it once, and the difference was about 3%, which is a pretty small difference,

It could be a random error (That is, the coach has no influence in game.) ,
Or it could be that the coaching staff's influence is simply very small



Edit 26/2/2025 :
A more precise representation of the numbers looks like this:


Before the age of 24, there is basically no need to worry about participating in high Reputation leagues , You can supplement almost 100% growth with friendly matchs.

Starting at age 24: The mechanics change, at this point friendlies no longer get you all the growth, you need to add a certain level of official play.
And the Reputation of the official games began to affect growth.

svonn said: Hello @harvestgreen22 , thanks again for your work!
I have some questions about your test setup. I've been using the same base-file that you seem to be using (from EBFM), frozen with FMRTE, etc.

When doing some tactics testing, I've noticed that even when simulating 10-20 seasons, I couldn't reproduce the same results on average in many cases, because some variables can't be frozen (like player links).
Have you been able to reproduce your findings here?

I'm also curious why the "baseline" has a positive goal difference, shouldn't a baseline roughly have +0? Expand

This question has been discussed in the Chinese forum community, and it goes something like this:
1.FMRTE takes a few seconds to lock. Because the way it works is it checks every few seconds.
The software is not designed to be used for testing, it is designed to meet the needs of popular players, so it may be checked every few seconds to reduce memory /CPU usage.
2. If the Test league's match schedule is too dense, there is a chance to lock in failures (number of occurrences: normal)
3. When the amount of player data is too large, some players have the opportunity to lock failure (number of occurrences: rare)
4. For AI-controlled teams, lock often fails (frequency of occurrence: normal) . This failure locking Creat the most the Biggest Error in testing .
5. Locking methods include FMRTE locking and in-game editor locking

Some of these conclusions may be wrong or right, but to be on the safe side, we treat them all as if they need to be addressed

For the above 5 points, the tentative conclusion obtained after discussion is as follows:
1 and 2: Test the leagues used, the matches are not too dense, leaving enough time for FMRTE to re-lock
3. don't have too many teams and players
4. It is best not to have any AI teams, all teams used for testing should create a "player manager" to take over.
If there must be an AI team, it is best not to change any the attributes of the players in the AI team after creat the league
(because after modifying the attributes, I do not know why the attributes can easily get out of control and become completely different ,
When you change the AI's player's "current CA" to be the same as the" recommended CA", it sometimes randomizes the attributes immediately, even if you increase the PA to "PA = recommended CA +10" , or adjust the PA to 200.).
5. Use both types of locks


In the end,
we think we need to sacrifice a little "universality" and choose the EBFM league with only 4 teams,
It has fewer players, less intensive games (30 match per team and 120 per season), and all teams are operated by humans, which basically avoids the problems mentioned above
The downside is that it's less race-intensive, so you need a lot more time to test the same sample size



baseline :
It is the goal difference of team D at full attribute 10 (unmodified), the translation may be different, you can understand its role. If you need to be more "normalized," you can subtract all goal difference results from this standard value

Choosing different initial conditions will vary this criterion. For example, in the test, we chose to let team A and Team C get the better attributes (all 11), Team B get the best attributes (all 12), and Team D get the best tactics (all 10 attributes).



Yarema said: There is some error, I wouldn't make huge conclusions with 4 difference, but we can probably say that difference between 1 to 20 first touch isn't much. Expand

Yes, as you can see from the excel,
The statistical standard deviation (" =STDEV() "in excel) is generally around 15,
And the "standard error" varies depending on the sample size,
A float range of +-4 is normal (error due to insufficient sample)

ptacts said: @harvestgreen22  first of all i want to say thank you for your time testing everything. I have a question, in previous "best" trainings you suggested having 5 star fitness coach and average the rest, now that your testing shows very good results with trainings that have [attacking] training in them, do you use 5 star attacking coaches, or average? Expand


For the test, I used all 5 stars.
There's usually a notation on the excel
Unless I have specified the use of different coaches in the form, the default is all 5 stars

——you suggested having 5 star fitness coach and average the rest
I didn't go into further analysis of the coaching staff because it's too many combinations.
I am now testing other more interesting parts, which will not be tested for the time being.
And then whether you can do that, I think you can. Because purely from the point of view of game engine performance, physical attributes are more important than anything else



tottso said: Am I correct in assuming q10 is the best for varied development with emphasis on pace + acceleration. Also does it make sense to train players age 26 + at their peak on the additional quickness focus? Expand

This is a newer table. The data tested before may have some defects because it did not include all the attributes.
You can see if there's anything you need in the new excel

excel : https://pixeldrain.com/u/953xPdxx
Another excel : https://pixeldrain.com/u/AtUvd3hY



Teremin said: @harvestgreen22 Which schedule do you suggest for all round development? I mean for all players. Expand

[Physical]+[Match Practice]+[Attacking]+[Recovery]x7+[Double Intensity]+[Addtional Focus Quickness]
or
[Quickness]+[Match Practice]+[Attacking]+[Recovery]x7+[Double Intensity]+[Addtional Focus Quickness]

excel : https://pixeldrain.com/u/953xPdxx
Another excel : https://pixeldrain.com/u/AtUvd3hY
You can see if there's anything you need in the new excel


azsumnasko said: I noticed that if I do it like in the attached pic I still have low level of injuries and huge gains in ACC and PACE. also for some reasons I get the Jumping get high Expand


Ronaldo De Lima said: with this schedule, how do you face 2 matches/week on Wed and Sat? Expand



You can choose to separate them, and only need to "have these contents" in a week, and do not need to arrange them in any sort
Recovery is not necessary, if there is no room for so much training, you can reduce some or all of the Recovery

CBP87 said: Thank you for clarifying, I have to admit, I'm not following this at all in regards to the attributes, can you dumb down please? Expand

Average goal difference:
It shows how good the team played , each season have 30 match , test for several seasons , Record the goal difference


Decision = 10 ：
All attribute is 10 ,including Decision.
In the case of nothing changed , the goal difference is 8.3

Decision = 20 (1 player)：
1 of the players Decision +10 : In most cases ,goal difference increased .
goal difference higher than 8.3 .
This is logical , As attribute increase , goal difference increased .

Decision = 20 (10 player)：
All of the players (expect goalkeeper) Decision +10 :  the goal difference is 2.8
This is not-logical , As attribute increase , goal difference Decreased .

Decision = 1 (10 player)：
All of the players Decision -10 :  the goal difference is 7.0
This is not-logical , This means "Player's Decision =10 " > "Player's Decision =1 " > "Player's Decision =20 ".


Decision = 20 (3 player)：
Choose 3 of the 10 players , Decision +10 : the goal difference is 15.8
Decision = 20 (5 player)：
Choose 5 of the 10 players , Decision +10 : the goal difference is 8.5
Decision = 20 (7 player)：
Choose 7 of the 10 players , Decision +10 : the goal difference is 5.9
This means that the more people with "Decision = 20", the worse the team performs

Decision = 20 (11 player)：
Choose 10 of the 10 players , Decision +10 : the goal difference is 2.8
When everyone's Decision are high , the team perform the most worse



Summary: ( only refers to specific partial attributes )

No more than 3 players have high Decision. Decision have a positive effect
If more than 5 players have high Decision at the same time, Decision have a negative effect .
The more people with high Decision , the worse the team does .
The worst team you can have is whole team with Decision 20 .

This is completely unsimulated realistic ——It's just the mechanics of the game

CBP87 said: They are using a different match engine mate, @harvestgreen22 are these tests being done on the normal ME or the modified one? Expand

Are you referring to the "simate.fmf" file (new engine recently)? I used the original, unmodified


Alonso said: So basically this means that we have to have players with a spread of attributes to have the best result? Or is just for the attributes (Decision, Positioning, Technique, Flair ,Off the ball , First touch, Tackling) you mentioned? Expand


Only (Decision, Positioning, Technique, Flair ,Off the ball , First touch, Tackling) , Only they have the effect of "competing and hurting each other" attribute. (I didn't complete the test completely, so some of the list may be inaccurate or missing)


such as Decision , whole team player Decision = 1 (very low) , only 1 Attacking Midfielder Decision = 20 (only 1 man keep high), goal difference = 12.3
that's much better than whole team player Decision = 20 ,  goal difference = 2.8 (in the picture above)


Other attributes, usually show as " the more the better", such as Pace, the relationship between attributes is "cooperation", the more the performance is better, that is the so-called "1+1>2" attribute.

https://pixeldrain.com/u/m1QY3CTr
Test league
https://pixeldrain.com/u/Lf1YNXaC
Test data (I didn't translate it)




1. The standard group consists of 11 players who are all decision 10, with a goal difference of 8.3

Next, each test independently improved one position, full-back, forward, back, centre-back, wingers, decision increased from 10 to 20, goal difference increased (13.5,16.3,11.6,19.1,13.7).
Only the striker is slightly down(6.3).

2. If all non-goalkeeper 10 player's decision is increased from 10 to 20 , the goal difference is reduced to 2.8, which is less than the standard value (8.3)

That is, the stats have increased, but the goal difference has decreased
The better the stats , the worse the Team goal difference

3. If all non-goalkeeper 10 player's decision is reduced from 10 to 1, the goal difference is reduced to 7.0, less than the standard value (8.3)
So the stats are down, the goal difference is down

4. Select 3 players( Attacking Midfielder + Centre Back + Winger ), the decision is increased from 10 to 20, the goal difference is increased from 8.3 to15.8

5. Select 5 players( Attacking Midfielder + Fullback + Defensive midfielder + Centre Back + Winger ) to increase decision from 10 to 20 , goal difference increased from 8.3 to8.5

6. Select 7 players, increase decision from 10 to 20 , goal difference decreased from 8.3 to5.9

7. Select 11 players, increase decision from 10 to 20 , goal difference decreased from 8.3 to 2.8


That is to say, after the number of players with a high value of this attribute increase,
it does not produce the effect of "1+1>2", but becomes a negative effect, and becomes a negative attribute.

Decision, Positioning, Technique, Flair ,Off the ball , First touch, Tackling all seem to have this kind of tendency
This happens not only when it go from 10 to the highest 20, but also happen in other like from all-10 to 15 , all-5 to 10, ect


Another finding was that a player's rating did not fully reflect his impact on the team. In individual cases, by improving certain attribute, the player's rating goes up, but the team's score goes down



Other attributes are "1+1>2" mutual cooperation attributes, such as Pace, the higher the Pace of the whole team, the better the team's total result

I haven't finish tested it yet,
but I've seen it happen with Technique and Flair Performance like this in different tactics.


From 红骑士Sakura
https://www.playgm.cc/thread-971631-1-1.html

While modifying the game engine (previous post), he discovered some logically related Stats of the engine's behavior

As you can see, Stats not only affect whether the player can complete better, but also affect how much the player likes to do this action.

For example, Decision , there might be a situation where the team "too likes to do this action Led to negative results " ?
We don't know how the engine could have caused this,
But from this result, decision of a team can not have too many people at the same time have high attributes,

for example, there are 2-3 players with high decision attributes, the other 7-8 people maintain low attributes, which is good for the team.
If this logic is correct (I haven't completely tested it yet), Some player are low skilled in Specific attribute and a few player are high skilled in these, which is good for the team.

Update the translation of that picture