Control Meta (Knap EF 424 IF HP V2 P101 AC) vs Man Utd (Home) Fully healed 1000 sample = Win 676 (67.6%), Draw 196 (19.6%), Loss 128 (12.8%), avg. 2.36-0.86
V7 Variant B vs Man Utd (Home) Fully healed 1000 sample = Win 656 (65.6%), Draw 221 (22.1%), Loss 123 (12.3%), avg. 2.14-0.86
424 Mountain King vs Man Utd (Home) Fully healed 300 sample (not full familiarity) = Win 208 (69.33%), Draw 63 (21.00%), Loss 29 (9.67%), avg. 2.36-0.78
424 TON 424 HP HUB MC P105 EFC vs Man Utd (Home) Fully healed 300 sample (not full familiarity) = Win 221 (73.67%), Draw 50 (16.67%), Loss 29 (9.67%), avg. 2.15-0.75
424 'Park the Bus' (default tactic) vs Man Utd (Home) Fully healed 300 sample (not full familiarity) = Win 50 (16.67%), Draw 78 (26.00%), Loss 172 (57.33%), avg. 1.37-0.66
I think an issue with comparing tactics is that the players you are fielding matter, and in real play you're more likely to get good players in certain positions rather than others.
So we can say, this tactic does best in a controlled test, and that tactic does best in a default plug-and-play with Luton, but it would be difficult to find conclusively that X tactic is 2% better than Y tactic. But you could demonstrate it's 2% better in 5 or 6 scenarios say.
( edited 2 days, 7 hours ago by GeorgeFloydOverdosed )
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The results are suggesting that it is in fact the case that the negative of dirtiness only kicks in at lower fitness levels:
Control Meta (Knap 424 tactic) vs Man Utd (Home) Fully healed 1000 sample = Win 676 (67.6%), Draw 196 (19.6%), Loss 128 (12.8%), avg. 2.36-0.86
Control Meta (Knap 424 tactic) vs Man Utd (Home) Fully healed 300 sample = Win 217 (72.33%), Draw 44 (14.67%), Loss 39 (13.00%), avg. 2.35-0.83
Dirtiness 12 Meta vs Man Utd (Home) Fully healed 300 sample = Win 209 (69.67%), Draw 54 (18.00%), Loss 37 (12.33%), avg. 2.39-0.94
Dirtiness 12 Meta (1 day before) vs Man Utd (Home) Fully healed 300 sample = Win 197, Draw 67, Loss 36, avg. 2.33-0.91
We see above that Dirtiness 8 > 12 seems to make no difference when the squad is set to 100% fitness.
Longshots Technique Passing Crossing 16 vs West Ham (Away) ~90% condition 300 sample = Win 92, Draw 98, Loss 110, avg. 1.35-1.49
Dirtiness 12 Longshots Technique Passing Crossing 16 vs West Ham (Away) ~90% condition 300 sample = Win 92, Draw 79, Loss 129, avg. 1.35-1.62
By contrast, when fitness is left to standard levels - around ~90% condition ~100% match fitness on average in this case - there does seem to be a significant difference.
This will require further testing to clarify. Other attributes aren't working this way, and are producing results in line with expectations.
I believe that the new method I'm using has fixed up my previous teamwork & technicals results, which should be disregarded as erroneous. I think it's mainly that I didn't adjust CA upwards in the realtime editor to match the increases, which I've done accurately now. I'll probably edit the previous post later to remove the old results to avoid confusion.
Control Meta (Knap 424 tactic) vs Man Utd (Home) Fully healed 1000 sample = Win 676 (67.6%), Draw 196 (19.6%), Loss 128 (12.8%), avg. 2.36-0.86
Control Meta (Knap 424 tactic) vs Man Utd (Home) Fully healed 300 sample = Win 217 (72.33%), Draw 44 (14.67%), Loss 39 (13.00%), avg. 2.35-0.83
Teamwork 14 Meta vs Man Utd (Home) Fully healed 300 sample = Win 217 (72.33%), Draw 52 (17.33%), Loss 31 (10.33%), avg. 2.47-0.88
So 8 > 14 Teamwork is now seemingly marginally beneficial. This is what I would have expected.
Pressure 1 Meta vs Man Utd (Home) Fully healed 300 sample = Win 146 (48.67%), Draw 73 (24.33%), Loss 81 (27.00%), avg. 1.60-1.00
Thought I'd try a very obvious one just to make sure things are on the right track and provide a yardstick to compare these lesser attributes to. Big drop from Pressure 13 > 1 as expected.
Longshots Technique Passing Crossing 16 Meta vs Man Utd (Home) Fully healed 300 sample = Win 236 (78.67%), Draw 48 (16.00%), Loss 16 (5.33%), avg. 2.88-0.84
Control Meta (Knap 424 tactic) vs Wolves (Away) Fully healed 300 sample = Win 256 (85.33%, Draw 31 (10.33%), Loss 13 (4.33%), avg. 2.75-0.83
Longshots Technique Passing Crossing 16 Meta vs Wolves (Away) Fully healed 300 sample = Win 274 (91.33%), Draw 21 (7.00%), Loss 5 (1.67%), avg. 3.09-0.82
Longshots Technique Passing Crossing 16 Meta vs West Ham (Away) ~90% condition 300 sample = Win 92, Draw 98, Loss 110, avg. 1.35-1.49
These attributes performed a quite a bit better than I expected. I did find in my testing that this particular combination yields significantly higher goal difference at very high performance levels, but this is showing significant benefits even at a lower performance level - albeit at a cost of +32 total attribute points and a good deal of CA to match.
I've added a GUI to the app. If you're already comfortable with using the command line then this won't make much of a difference, except it lets you swap teams without having to generate a new fixture in-game, and it also reads the next fixture from the game without you having to call the `get-next-fixture` command yourself (although all the old behaviour is still retained).
21 members of backroom staff are included in fixture objects that get sent to the match engine and the results only differ when changing one of them: the manager's.
This sounds obvious but the Instant Result button does say "Are you sure you want to leave this match to your staff?" Turns out the staff have nothing to do with the result. I'm running an experiment now to see which of your attributes (and I imagine, by extension, AI managers too*) actually matter when simming results.
* I suppose I could run a dedicated experiment later to change the AI managers too and see if they get better win rates.
Also, earlier, I fudged the fixture object to make the same team play itself 300 times. I don't know what I was expecting but the win-rate was basically the same for both (I think the away team edged it just slightly). I might try mapping the stadium and competition data in the object later so people can run tests on what happens if you change those.
EDIT: only six attributes do anything for the manager when simming a game but they barely do anything:
- man management - motivating - coaching: mental - level of discipline - working with youngsters - tactics: attacking
To be clear, I played the same fixture over and over and changed _every_ coaching attribute of every member of staff one at a time. Only changing the above on the manager did anything.
The results didn't change a whole lot, but it occurs to me I'm already battering my opponent anyway so I'm going to repeat the experiment but against myself, so the baseline should be equal and there we'll really see deviations.
Question here. Is there a way to force certain seed to be processed in game? For example if one finds a seed that originally have made the team lose but then after tweaking some attributes the team won and one would like to see what has changed by checking match engine representation.
FREVKY said: Question here. Is there a way to force certain seed to be processed in game? For example if one finds a seed that originally have made the team lose but then after tweaking some attributes the team won and one would like to see what has changed by checking match engine representation. Expand Yep but only if you use the instant result button. See here for how https://fm-arena.com/find-comment/54572/
The reason you need to sim is because that's what my test does, so you hae to do it too to get the same outcome.
Fussballspieler said: What is the effect of the match preparation sessions done through the inbox? Expand
I had a quick look at this, but I've got much further to go. Here's what I've learned so far.
The Match Focus training slot provides no attribute training. It gives a +1 bump to cohesion and match sharpness, and a +2 bump to tactical familiarity. For comparison, Match Practice gives a +2 bump to all of those things. It has no condition, fatigue or injury cost and makes no difference to player happiness (Match Practice is +3 for happiness).
To explain what each tactical/set piece focus does, I need to briefly explain how attributes work during a match (and it's brief because I've not finished investigating this yet).
About half of the attributes are represented in game as random numbers between 0 and their actual value (some attributes are -1 or +2 to the upper limit and some have modifiers I've not tracked down yet which increase the ceiling by as high as 6). Every 15 match seconds, the values of each attribute are re-rolled to be somewhere in this range.
With that out of the way, here's what the match focus does:
- In possession: Off the Ball is rolled twice every 15 seconds (instead of just once) when your team has the ball and the higher of the two values is used. - In Transition: Teamwork is re-rolled after possession change and the higher of the two values is kept. - Out of Possession: Marking is rolled twice while the opponent has the ball and the higher of the two values is used. - Attacking corners: Anticipation is rolled twice when you have a corner and the higher result is used - Defending corners: same as above except when the opponent gets a corner - Attacking free kicks: same as above - Defending free kicks: same as above - Throw-ins: Re-rolls anticipation or gives a +1 bonus to the original value if it was already better - Penalties: a flat +1 bonus when taking a penalty
And by the way, I think that to get these bonuses, a player's current Concentration roll has to be >11, which means players with a low concentration attribute never see these buffs.
AI controlled teams randomly decide on these focuses before a match.
(No, before someone asks, this doesn't mean I've managed to dig in to the code and find where it says "Pace is worth 100, Acceleration is with 98, Stamina is worth blah". My investigation has been brief so far but it looks like the web of interconnectedness between attributes is massive. It might just be a hilarious coincidence that physicals render most of it unnecessary.)
EDIT: I looked in to this because @Dezhy asked the question in another thread and I never got back to him. Sorry mate.
EDIT 2: I might as well list what the 27 randomised attributes are:
- Teamwork - Technique - Work rate - Vision - Concentration - Anticipation - Decisions - Off the Ball - Marking - Flair - Positioning - Composure - Dribbling - First Touch - Pace - Acceleration - Heading - Balance - Agility - Strength - Reflexes - Command of Area - Aggression - Eccentricity - Bravery - One on Ones - Tackling
LightningFlik said: My investigation has been brief so far but it looks like the web of interconnectedness between attributes is massive. Expand
That leaves me wondering why didn't @Zippo find any correlation between them: https://fm-arena.com/find-comment/40372/ in their testing. As I mentioned back then, those findings were extremely surprising.
Couldn't say. I'm not sure what they tweaked or what they were measuring. Maybe they lowered crossing and technique but their xG didn't change so they said it wasn't important, and they just never noticed that their crosses attempted/completed plummeted, forcing their players to find goals elsewhere.
**EDIT:** I'm about to spend 400 minutes running 40,000 matches seeing what happens when I take a normal player in a normal match and set his passing and technique to every combination of 1-20. I doubt it will have a big impact on the average W/L/D record but it'll probably have some impact on his passing stats.
( edited 21 hours, 54 min ago by GeorgeFloydOverdosed )
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I used the harness to brute force test almost all possible settings for my 'Variant B' of the Mars Attacks V7 tactic with my meta team.
I cumulatively improved it from W 132, D 36, L 32, avg. 2.02-0.94 to W 142, D 39, L 19, avg. 2.21-0.79.
For comparison, the Knap 424 scored W 135.2, D 39.2, L 25.6, avg. 2.36-0.86.
I then tested it with Rushall, finished 3rd. I thought a bit disappointing, but not too bad.
I tried Luton next. I got 1st with an unselected team straight off the bat. I thought bloody hell, this is it. To get 1st with Luton with a selected team is very rare. To get it with an unselected team is unprecedented. I think I was even a few points clear of 2nd.
But then I got 6th and 10th. And with Man City, the results were 1st but only 93pts and 100pts.
But today I see that I accidentally used what scored W 135, D 39, L 26, avg. 2.35-0.89 instead.
After fixing it up, I've still only got 7th as unselected Luton and 4th as selected Luton as the two initial samples.
So perhaps tactics are going to be somewhat safe from this brute-forcing method. This and the dirtiness results suggests to me that the non-linear nature of attributes and their interaction with external factors such % condition, means that we can't simply universalize a tactic that works perfectly for one team. Each team would have a slightly different optimum, and the method seemingly can't really tell you what tactic works best with most teams overall.
But I guess what I can try next is to brute force Luton rather than the meta team, and see what that produces.
- In possession: Off the Ball is rolled twice every 15 seconds (instead of just once) when your team has the ball and the higher of the two values is used. - In Transition: Teamwork is re-rolled after possession change and the higher of the two values is kept. - Out of Possession: Marking is rolled twice while the opponent has the ball and the higher of the two values is used. - Attacking corners: Anticipation is rolled twice when you have a corner and the higher result is used - Defending corners: same as above except when the opponent gets a corner - Attacking free kicks: same as above - Defending free kicks: same as above - Throw-ins: Re-rolls anticipation or gives a +1 bonus to the original value if it was already better - Penalties: a flat +1 bonus when taking a penalty
And by the way, I think that to get these bonuses, a player's current Concentration roll has to be >11, which means players with a low concentration attribute never see these buffs. Expand
So it sounds like match focus is kind of like a consistency modifier for those particular attributes you mention. Sounds like 'transition phrase' would be the one to pick, since teamwork actually does a little something.
I think I can shed some possible light on this. My own testing has shown that there are certain combinations of technical attributes (i.e. crossing + long shots + technique) that produce a significant difference, but this only came through when the team was dominating.
I think what this means is that when Man City play Barrow in the FA Cup, they're going to go above and beyond what Burton would do to Barrow, but only so long as their physicals mean they can keep it up - and in this case, they should have enough physical superiority to keep it up all game, whereas Burton might have to call it a day at 5-0 60 minutes in.
But if its say Man City vs. Luton, Man City should start with enough physical advantage to execute all that technical finesse for a while, but then I think the idea is that it ends up becoming a slogfest for much of the match where whoever makes the least serious mistakes (i.e. low dirtiness, high concentration) wins the battle.
IMO I think it's deliberate but probably unintentionally unbalanced, because it's common sense that if a player is worn out, he's not going to be pulling off clever technical skills.
With these combinations not showing up in tests, it would likely be because these tests are controlled where all players about equal in fitness all through the match due to near identical attributes, healing, etc.
Ultimately, one team dominating the other is not a measurable metric (what threshold - and what metrics define the trigger - qualifies as "dominating"? noone knows, thus it is not useful), so unless the reverse engineering allows insight into it, it is not a very useful scenario.
Having both teams equal, so with raising or lowering of single (or groups of) attributes, their impact can be extracted appropriately. What @LightningFlik mentioned was that certain groups of attributes may be made more effective by an external attribute threshold (Concentration >11). This is measurable and thus can be tested.
Ultimately, as I suggested to harvestgreen back in the day:
Jolt said: The only way of making it clear is tedious work: You (or someone else) needs to run tests in groups of three attributes, raising them or lowering them together. This way, we'll gain insight into every 3 attribute combination, and by doing the average of every 3 attribute combination that have the same 2 attributes, we can also gain insight into which 2 attribute combination is good.
If you are correct, then the 3 attribute combination of 'Acceleration'+'Pace'+'Jumping Reach' should perform much better than any other combination, including 'Acceleration'+'Pace'+'Strength', or 'Passing'+'Decisions'+'Work Rate'. Expand
Of course, this is an utterly gargantuan amount of work. All visible attributes, plus the four hidden ones we know have an effect on performance (Consistency, Important Matches [this one would require editing match reputation or editing rivalry between the teams], Pressure, Dirtiness), and we're talking about close to 10,000 3-attribute combinations.
Even testing just 3 variations per attribute (7, 12, 17), and running 10 games per variation to get a sample size, and we're talking about hundreds of thousands of games.
So it is understandable that noone followed up on it.
Jolt said: Ultimately, one team dominating the other is not a measurable metric (what threshold - and what metrics define the trigger - qualifies as "dominating"? noone knows, thus it is not useful), so unless the reverse engineering allows insight into it, it is not a very useful scenario. Expand Dominating means winning almost all games with ease, which we measure with the points tally.
There is a high correlation with fitness and physical attributes. We don't know what it's specifically composed of, yes, but I wouldn't go so far as to say it's not measurable.
Jolt said: What @LightningFlik mentioned was that certain groups of attributes may be made more effective by an external attribute threshold (Concentration >11). Expand I think you've made an error of interpretation here. I think LightningFlik is saying that concentration >11 was necessary for certain miniscule match focus boosts, not for groups of attributes to kick in or be more effective.
It's possible similar things are going on with attributes, we don't have anything to suggest that yet.
One thing that suggests its not going to be like that is that I scaled down my template for lower leagues, and it seemed to work just as good. So I think it's about attributes relative to the opposition, rather than set thresholds for attributes to take effect.
Jolt said: Of course, this is an utterly gargantuan amount of work. All visible attributes, plus the four hidden ones we know have an effect on performance (Consistency, Important Matches [this one would require editing match reputation or editing rivalry between the teams], Pressure, Dirtiness), and we're talking about close to 10,000 3-attribute combinations.
Even testing just 3 variations per attribute (7, 12, 17), and running 10 games per variation to get a sample size, and we're talking about hundreds of thousands of games.
So it is understandable that noone followed up on it. Expand I think it's viable to do in a certain way
We can get rid of some attributes such as long throws, penalty taking, etc.
Then we can also discard some combinations such as vision + natural fitness + technique. And order of the combination doesn't matter either.
I did about 11,000 samples last night. Not sure on the exact numbers, but this means our processing capacity just increased at least 20-fold. Maybe applying AI to it can strip away the remaining problem of tedium.
I can't remember what I got this from, but I think combos can supposedly be up to 5 attributes.
What's holding me back right now is that it's super tedious to adjust CA in line with attribute changes in the realtime editor. Maybe the official realtime editor automatically does this, might see if I can use it later.
GeorgeFloydOverdosed said: What's holding me back right now is that it's super tedious to adjust CA in line with attribute changes in the realtime editor. Maybe the official realtime editor automatically does this, might see if I can use it later. Expand Why do you need to adjust CA? I don't think it affects a player's performance.
LightningFlik said: Why do you need to adjust CA? I don't think it affects a player's performance. Expand If I change an attribute that has CA weight, I have to change the player's CA to the new Recommended CA, otherwise it messes with the results.
I had my fingers crossed initially that the game would automatically just recalculate the CA for the player, but its not the case. It reduces every other attribute a little to make up for it.
When I added the correct CA, I got results that lined up with expectation (i.e. teamwork being marginally beneficial, rather than 8 > 14 teamwork producing significantly worse results)
GeorgeFloydOverdosed said: If I change an attribute that has CA weight, I have to change the player's CA to the new Recommended CA, otherwise it messes with the results.
I had my fingers crossed initially that the game would automatically just recalculate the CA for the player, but its not the case. It reduces every other attribute a little to make up for it.
When I added the correct CA, I got results that lined up with expectation (i.e. teamwork being marginally beneficial, rather than 8 > 14 teamwork producing significantly worse results) Expand Are you changing attributes in the game or the fixture object? If you edit the fixture JSON, changing the attributes should be all you need unless I'm very much mistaken.
LightningFlik said: Are you changing attributes in the game or the fixture object? If you edit the fixture JSON, changing the attributes should be all you need unless I'm very much mistaken.
(And, if I am, I can bake in the recalculation.) Expand I'm using a realtime editor to change the attributes in the game before exporting to JSON
I haven't taken the time yet to examine how your program works. So I don't know what you mean by 'fixture object'. Are you saying we can just edit the attribute data in the JSON file after export?
Let me know the process, because having to change the CA for each player is a real pain
GeorgeFloydOverdosed said: I haven't taken the time yet to examine how your program works. So I don't know what you mean by 'fixture object'. Are you saying we can just edit the attribute data in the JSON file after export? Expand Yes. The reason I added the much larger JSON type as an export is so people could modify it by hand. Open it and search for "home.player[0].ratings" (and [1] and [2] and so on). The first 15 values of that are positional ratings (1-20) then all the attributes. The name of the player is 15 lines up from this. I'm working to annotate more of these rows in the future but this should be enough to start.
EDIT: I'll try to make the fixture and players editable in the UI too, and improve the documentation.
37 outfield attributes were tested at absolute in-game levels 4, 8, 12, 16, and 20.
In each condition, one attribute was set to the selected level for all 20 Dijon matchday players (starters and substitutes). The Lens, tactics, lineup, all other attributes, and fixture settings were held constant.
Each condition and the unchanged baseline used the same 500 paired seeds (1-500), for 93,000 simulated matches in total.
Excluded attributes: Left Foot, Right Foot, Corners, Penalty Taking, Long Throws, and Free Kicks.
The outcome is the change in Dijon's goal difference relative to the unchanged baseline. Positive values favor Dijon; negative values favor Lens. A positive change in goals conceded means Dijon conceded more.
blackbird said: ATTRIBUTE-LEVEL RESPONSE IN FM24: DIJON VS LENS Expand
Love it! 93,000 matches though? Blimey. Some questions:
1. Did you write a script to perform these tests? 2. Did the game crash while running these matches? 3. How long did the entire thing take? 3. How large an increase do you consider to be significant and not just random noise? I notice some results dip with increases (Off the Ball) before rising again.
My passing/technique test finished. I changed a single player's passing and technique attributes to be every combination of 1-20 each and then played 100 matches. Here are the results: https://pastebin.com/D2iH45Vv
- Pass completion is 87% at the best and 85% at worst. Passing helps but it's a minimal impact. - Passes attempted seems to lightly correlate with a higher passing ability - Progressive passes is usually above 8 if passing and technique are double digits. It's usually below 7 when Passing is single figures (regardless of technique) - High technique correlates with shots on target, but that's probably for reasons not related to passing - Both correlate with key passes - Dribbles correlates more with high passing than technique - Possession lost correlates with low technique
I've made Aston Villa play an exact copy of itself at Villa Park; same starting XI, same tactics, 1000 games:
home 351 / draw 307 / away 342
I repeated this at Ewood Park (to check whether your team owning the stadium makes a difference):
home 400 / draw 216 / away 384, avg 2.70-2.71
It's interesting that the home team still has a marginal advantage but draws plummet in this scenario.
Next I'm going to choose another neutral venue but alter the number of home and away fans. Ewood Park holds 31,363; here's what happens when they're split variously:
100% home fans: home 588 / draw 181 / away 231, avg 3.38-2.36 100% away fans: home 234 / draw 173 / away 593, avg 2.18-3.19 50%/50% fans: home 399 / draw 191 / away 410, avg 2.72-2.73 No fans: home 407 / draw 199 / away 394, avg 2.70-2.74 75/25 fans: home 389 / draw 195 / away 416, avg 2.72-2.79 75,000/25,000 fans: home 512 / draw 202 / away 286, avg 3.08-2.46
(These last two tests were to see if the absolute value of fans made the positive impact or the relative strength. Given the absolute number of fans makes a difference then there are performance gains in upgrading your stadium as much as possible, since every fan counts.)
First results using a new, more precise method:
Control Meta (Knap EF 424 IF HP V2 P101 AC) vs Man Utd (Home) Fully healed 1000 sample = Win 676 (67.6%), Draw 196 (19.6%), Loss 128 (12.8%), avg. 2.36-0.86
V7 Variant B vs Man Utd (Home) Fully healed 1000 sample = Win 656 (65.6%), Draw 221 (22.1%), Loss 123 (12.3%), avg. 2.14-0.86
424 Mountain King vs Man Utd (Home) Fully healed 300 sample (not full familiarity) = Win 208 (69.33%), Draw 63 (21.00%), Loss 29 (9.67%), avg. 2.36-0.78
424 TON 424 HP HUB MC P105 EFC vs Man Utd (Home) Fully healed 300 sample (not full familiarity) = Win 221 (73.67%), Draw 50 (16.67%), Loss 29 (9.67%), avg. 2.15-0.75
424 'Park the Bus' (default tactic) vs Man Utd (Home) Fully healed 300 sample (not full familiarity) = Win 50 (16.67%), Draw 78 (26.00%), Loss 172 (57.33%), avg. 1.37-0.66
I think an issue with comparing tactics is that the players you are fielding matter, and in real play you're more likely to get good players in certain positions rather than others.
So we can say, this tactic does best in a controlled test, and that tactic does best in a default plug-and-play with Luton, but it would be difficult to find conclusively that X tactic is 2% better than Y tactic. But you could demonstrate it's 2% better in 5 or 6 scenarios say.
The results are suggesting that it is in fact the case that the negative of dirtiness only kicks in at lower fitness levels:
Control Meta (Knap 424 tactic) vs Man Utd (Home) Fully healed 1000 sample = Win 676 (67.6%), Draw 196 (19.6%), Loss 128 (12.8%), avg. 2.36-0.86
Control Meta (Knap 424 tactic) vs Man Utd (Home) Fully healed 300 sample = Win 217 (72.33%), Draw 44 (14.67%), Loss 39 (13.00%), avg. 2.35-0.83
Dirtiness 12 Meta vs Man Utd (Home) Fully healed 300 sample = Win 209 (69.67%), Draw 54 (18.00%), Loss 37 (12.33%), avg. 2.39-0.94
Dirtiness 12 Meta (1 day before) vs Man Utd (Home) Fully healed 300 sample = Win 197, Draw 67, Loss 36, avg. 2.33-0.91
We see above that Dirtiness 8 > 12 seems to make no difference when the squad is set to 100% fitness.
Longshots Technique Passing Crossing 16 vs West Ham (Away) ~90% condition 300 sample = Win 92, Draw 98, Loss 110, avg. 1.35-1.49
Dirtiness 12 Longshots Technique Passing Crossing 16 vs West Ham (Away) ~90% condition 300 sample = Win 92, Draw 79, Loss 129, avg. 1.35-1.62
By contrast, when fitness is left to standard levels - around ~90% condition ~100% match fitness on average in this case - there does seem to be a significant difference.
This will require further testing to clarify. Other attributes aren't working this way, and are producing results in line with expectations.
I believe that the new method I'm using has fixed up my previous teamwork & technicals results, which should be disregarded as erroneous. I think it's mainly that I didn't adjust CA upwards in the realtime editor to match the increases, which I've done accurately now. I'll probably edit the previous post later to remove the old results to avoid confusion.
Control Meta (Knap 424 tactic) vs Man Utd (Home) Fully healed 1000 sample = Win 676 (67.6%), Draw 196 (19.6%), Loss 128 (12.8%), avg. 2.36-0.86
Control Meta (Knap 424 tactic) vs Man Utd (Home) Fully healed 300 sample = Win 217 (72.33%), Draw 44 (14.67%), Loss 39 (13.00%), avg. 2.35-0.83
Teamwork 14 Meta vs Man Utd (Home) Fully healed 300 sample = Win 217 (72.33%), Draw 52 (17.33%), Loss 31 (10.33%), avg. 2.47-0.88
So 8 > 14 Teamwork is now seemingly marginally beneficial. This is what I would have expected.
Pressure 1 Meta vs Man Utd (Home) Fully healed 300 sample = Win 146 (48.67%), Draw 73 (24.33%), Loss 81 (27.00%), avg. 1.60-1.00
Thought I'd try a very obvious one just to make sure things are on the right track and provide a yardstick to compare these lesser attributes to. Big drop from Pressure 13 > 1 as expected.
Longshots Technique Passing Crossing 16 Meta vs Man Utd (Home) Fully healed 300 sample = Win 236 (78.67%), Draw 48 (16.00%), Loss 16 (5.33%), avg. 2.88-0.84
Control Meta (Knap 424 tactic) vs Wolves (Away) Fully healed 300 sample = Win 256 (85.33%, Draw 31 (10.33%), Loss 13 (4.33%), avg. 2.75-0.83
Longshots Technique Passing Crossing 16 Meta vs Wolves (Away) Fully healed 300 sample = Win 274 (91.33%), Draw 21 (7.00%), Loss 5 (1.67%), avg. 3.09-0.82
Longshots Technique Passing Crossing 16 Meta vs West Ham (Away) ~90% condition 300 sample = Win 92, Draw 98, Loss 110, avg. 1.35-1.49
These attributes performed a quite a bit better than I expected. I did find in my testing that this particular combination yields significantly higher goal difference at very high performance levels, but this is showing significant benefits even at a lower performance level - albeit at a cost of +32 total attribute points and a good deal of CA to match.
I've added a GUI to the app. If you're already comfortable with using the command line then this won't make much of a difference, except it lets you swap teams without having to generate a new fixture in-game, and it also reads the next fixture from the game without you having to call the `get-next-fixture` command yourself (although all the old behaviour is still retained).
21 members of backroom staff are included in fixture objects that get sent to the match engine and the results only differ when changing one of them: the manager's.
This sounds obvious but the Instant Result button does say "Are you sure you want to leave this match to your staff?" Turns out the staff have nothing to do with the result. I'm running an experiment now to see which of your attributes (and I imagine, by extension, AI managers too*) actually matter when simming results.
* I suppose I could run a dedicated experiment later to change the AI managers too and see if they get better win rates.
Also, earlier, I fudged the fixture object to make the same team play itself 300 times. I don't know what I was expecting but the win-rate was basically the same for both (I think the away team edged it just slightly). I might try mapping the stadium and competition data in the object later so people can run tests on what happens if you change those.
EDIT: only six attributes do anything for the manager when simming a game but they barely do anything:
- man management
- motivating
- coaching: mental
- level of discipline
- working with youngsters
- tactics: attacking
To be clear, I played the same fixture over and over and changed _every_ coaching attribute of every member of staff one at a time. Only changing the above on the manager did anything.
The results didn't change a whole lot, but it occurs to me I'm already battering my opponent anyway so I'm going to repeat the experiment but against myself, so the baseline should be equal and there we'll really see deviations.
Question here. Is there a way to force certain seed to be processed in game? For example if one finds a seed that originally have made the team lose but then after tweaking some attributes the team won and one would like to see what has changed by checking match engine representation.
FREVKY said: Question here. Is there a way to force certain seed to be processed in game? For example if one finds a seed that originally have made the team lose but then after tweaking some attributes the team won and one would like to see what has changed by checking match engine representation.
Yep but only if you use the instant result button. See here for how https://fm-arena.com/find-comment/54572/
The reason you need to sim is because that's what my test does, so you hae to do it too to get the same outcome.
Hello guys, a couple questions regarding optimal FM play (and curious if they will carryover into FM27 but that is tbd).
What is the effect of collective competition bonuses?
What is the effect of the match preparation sessions done through the inbox?
Fussballspieler said: What is the effect of the match preparation sessions done through the inbox?
I had a quick look at this, but I've got much further to go. Here's what I've learned so far.
The Match Focus training slot provides no attribute training. It gives a +1 bump to cohesion and match sharpness, and a +2 bump to tactical familiarity. For comparison, Match Practice gives a +2 bump to all of those things. It has no condition, fatigue or injury cost and makes no difference to player happiness (Match Practice is +3 for happiness).
To explain what each tactical/set piece focus does, I need to briefly explain how attributes work during a match (and it's brief because I've not finished investigating this yet).
About half of the attributes are represented in game as random numbers between 0 and their actual value (some attributes are -1 or +2 to the upper limit and some have modifiers I've not tracked down yet which increase the ceiling by as high as 6). Every 15 match seconds, the values of each attribute are re-rolled to be somewhere in this range.
With that out of the way, here's what the match focus does:
- In possession: Off the Ball is rolled twice every 15 seconds (instead of just once) when your team has the ball and the higher of the two values is used.
- In Transition: Teamwork is re-rolled after possession change and the higher of the two values is kept.
- Out of Possession: Marking is rolled twice while the opponent has the ball and the higher of the two values is used.
- Attacking corners: Anticipation is rolled twice when you have a corner and the higher result is used
- Defending corners: same as above except when the opponent gets a corner
- Attacking free kicks: same as above
- Defending free kicks: same as above
- Throw-ins: Re-rolls anticipation or gives a +1 bonus to the original value if it was already better
- Penalties: a flat +1 bonus when taking a penalty
And by the way, I think that to get these bonuses, a player's current Concentration roll has to be >11, which means players with a low concentration attribute never see these buffs.
AI controlled teams randomly decide on these focuses before a match.
(No, before someone asks, this doesn't mean I've managed to dig in to the code and find where it says "Pace is worth 100, Acceleration is with 98, Stamina is worth blah". My investigation has been brief so far but it looks like the web of interconnectedness between attributes is massive. It might just be a hilarious coincidence that physicals render most of it unnecessary.)
EDIT: I looked in to this because @Dezhy asked the question in another thread and I never got back to him. Sorry mate.
EDIT 2: I might as well list what the 27 randomised attributes are:
- Teamwork
- Technique
- Work rate
- Vision
- Concentration
- Anticipation
- Decisions
- Off the Ball
- Marking
- Flair
- Positioning
- Composure
- Dribbling
- First Touch
- Pace
- Acceleration
- Heading
- Balance
- Agility
- Strength
- Reflexes
- Command of Area
- Aggression
- Eccentricity
- Bravery
- One on Ones
- Tackling
LightningFlik said: My investigation has been brief so far but it looks like the web of interconnectedness between attributes is massive.
That leaves me wondering why didn't @Zippo find any correlation between them: https://fm-arena.com/find-comment/40372/ in their testing. As I mentioned back then, those findings were extremely surprising.
Jolt said: That leaves me wondering why didn't @Zippo find any correlation between them: https://fm-arena.com/find-comment/40372/ in their testing. As I mentioned back then, those findings were extremely surprising.
Couldn't say. I'm not sure what they tweaked or what they were measuring. Maybe they lowered crossing and technique but their xG didn't change so they said it wasn't important, and they just never noticed that their crosses attempted/completed plummeted, forcing their players to find goals elsewhere.
**EDIT:** I'm about to spend 400 minutes running 40,000 matches seeing what happens when I take a normal player in a normal match and set his passing and technique to every combination of 1-20. I doubt it will have a big impact on the average W/L/D record but it'll probably have some impact on his passing stats.
I used the harness to brute force test almost all possible settings for my 'Variant B' of the Mars Attacks V7 tactic with my meta team.
I cumulatively improved it from W 132, D 36, L 32, avg. 2.02-0.94 to W 142, D 39, L 19, avg. 2.21-0.79.
For comparison, the Knap 424 scored W 135.2, D 39.2, L 25.6, avg. 2.36-0.86.
I then tested it with Rushall, finished 3rd. I thought a bit disappointing, but not too bad.
I tried Luton next. I got 1st with an unselected team straight off the bat. I thought bloody hell, this is it. To get 1st with Luton with a selected team is very rare. To get it with an unselected team is unprecedented. I think I was even a few points clear of 2nd.
But then I got 6th and 10th. And with Man City, the results were 1st but only 93pts and 100pts.
But today I see that I accidentally used what scored W 135, D 39, L 26, avg. 2.35-0.89 instead.
After fixing it up, I've still only got 7th as unselected Luton and 4th as selected Luton as the two initial samples.
So perhaps tactics are going to be somewhat safe from this brute-forcing method. This and the dirtiness results suggests to me that the non-linear nature of attributes and their interaction with external factors such % condition, means that we can't simply universalize a tactic that works perfectly for one team. Each team would have a slightly different optimum, and the method seemingly can't really tell you what tactic works best with most teams overall.
But I guess what I can try next is to brute force Luton rather than the meta team, and see what that produces.
LightningFlik said: With that out of the way, here's what the match focus does:
- In possession: Off the Ball is rolled twice every 15 seconds (instead of just once) when your team has the ball and the higher of the two values is used.
- In Transition: Teamwork is re-rolled after possession change and the higher of the two values is kept.
- Out of Possession: Marking is rolled twice while the opponent has the ball and the higher of the two values is used.
- Attacking corners: Anticipation is rolled twice when you have a corner and the higher result is used
- Defending corners: same as above except when the opponent gets a corner
- Attacking free kicks: same as above
- Defending free kicks: same as above
- Throw-ins: Re-rolls anticipation or gives a +1 bonus to the original value if it was already better
- Penalties: a flat +1 bonus when taking a penalty
And by the way, I think that to get these bonuses, a player's current Concentration roll has to be >11, which means players with a low concentration attribute never see these buffs.
So it sounds like match focus is kind of like a consistency modifier for those particular attributes you mention. Sounds like 'transition phrase' would be the one to pick, since teamwork actually does a little something.
Jolt said: That leaves me wondering why didn't @Zippo find any correlation between them: https://fm-arena.com/find-comment/40372/ in their testing. As I mentioned back then, those findings were extremely surprising.
I think I can shed some possible light on this. My own testing has shown that there are certain combinations of technical attributes (i.e. crossing + long shots + technique) that produce a significant difference, but this only came through when the team was dominating.
I think what this means is that when Man City play Barrow in the FA Cup, they're going to go above and beyond what Burton would do to Barrow, but only so long as their physicals mean they can keep it up - and in this case, they should have enough physical superiority to keep it up all game, whereas Burton might have to call it a day at 5-0 60 minutes in.
But if its say Man City vs. Luton, Man City should start with enough physical advantage to execute all that technical finesse for a while, but then I think the idea is that it ends up becoming a slogfest for much of the match where whoever makes the least serious mistakes (i.e. low dirtiness, high concentration) wins the battle.
IMO I think it's deliberate but probably unintentionally unbalanced, because it's common sense that if a player is worn out, he's not going to be pulling off clever technical skills.
With these combinations not showing up in tests, it would likely be because these tests are controlled where all players about equal in fitness all through the match due to near identical attributes, healing, etc.
Ultimately, one team dominating the other is not a measurable metric (what threshold - and what metrics define the trigger - qualifies as "dominating"? noone knows, thus it is not useful), so unless the reverse engineering allows insight into it, it is not a very useful scenario.
Having both teams equal, so with raising or lowering of single (or groups of) attributes, their impact can be extracted appropriately. What @LightningFlik mentioned was that certain groups of attributes may be made more effective by an external attribute threshold (Concentration >11). This is measurable and thus can be tested.
Ultimately, as I suggested to harvestgreen back in the day:
Jolt said: The only way of making it clear is tedious work: You (or someone else) needs to run tests in groups of three attributes, raising them or lowering them together. This way, we'll gain insight into every 3 attribute combination, and by doing the average of every 3 attribute combination that have the same 2 attributes, we can also gain insight into which 2 attribute combination is good.
If you are correct, then the 3 attribute combination of 'Acceleration'+'Pace'+'Jumping Reach' should perform much better than any other combination, including 'Acceleration'+'Pace'+'Strength', or 'Passing'+'Decisions'+'Work Rate'.
Of course, this is an utterly gargantuan amount of work. All visible attributes, plus the four hidden ones we know have an effect on performance (Consistency, Important Matches [this one would require editing match reputation or editing rivalry between the teams], Pressure, Dirtiness), and we're talking about close to 10,000 3-attribute combinations.
Even testing just 3 variations per attribute (7, 12, 17), and running 10 games per variation to get a sample size, and we're talking about hundreds of thousands of games.
So it is understandable that noone followed up on it.
Jolt said: Ultimately, one team dominating the other is not a measurable metric (what threshold - and what metrics define the trigger - qualifies as "dominating"? noone knows, thus it is not useful), so unless the reverse engineering allows insight into it, it is not a very useful scenario.
Dominating means winning almost all games with ease, which we measure with the points tally.
There is a high correlation with fitness and physical attributes. We don't know what it's specifically composed of, yes, but I wouldn't go so far as to say it's not measurable.
Jolt said: What @LightningFlik mentioned was that certain groups of attributes may be made more effective by an external attribute threshold (Concentration >11).
I think you've made an error of interpretation here. I think LightningFlik is saying that concentration >11 was necessary for certain miniscule match focus boosts, not for groups of attributes to kick in or be more effective.
It's possible similar things are going on with attributes, we don't have anything to suggest that yet.
One thing that suggests its not going to be like that is that I scaled down my template for lower leagues, and it seemed to work just as good. So I think it's about attributes relative to the opposition, rather than set thresholds for attributes to take effect.
Jolt said: Of course, this is an utterly gargantuan amount of work. All visible attributes, plus the four hidden ones we know have an effect on performance (Consistency, Important Matches [this one would require editing match reputation or editing rivalry between the teams], Pressure, Dirtiness), and we're talking about close to 10,000 3-attribute combinations.
Even testing just 3 variations per attribute (7, 12, 17), and running 10 games per variation to get a sample size, and we're talking about hundreds of thousands of games.
So it is understandable that noone followed up on it.
I think it's viable to do in a certain way
We can get rid of some attributes such as long throws, penalty taking, etc.
Then we can also discard some combinations such as vision + natural fitness + technique. And order of the combination doesn't matter either.
I did about 11,000 samples last night. Not sure on the exact numbers, but this means our processing capacity just increased at least 20-fold. Maybe applying AI to it can strip away the remaining problem of tedium.
I can't remember what I got this from, but I think combos can supposedly be up to 5 attributes.
What's holding me back right now is that it's super tedious to adjust CA in line with attribute changes in the realtime editor. Maybe the official realtime editor automatically does this, might see if I can use it later.
GeorgeFloydOverdosed said: What's holding me back right now is that it's super tedious to adjust CA in line with attribute changes in the realtime editor. Maybe the official realtime editor automatically does this, might see if I can use it later.
Why do you need to adjust CA? I don't think it affects a player's performance.
LightningFlik said: Why do you need to adjust CA? I don't think it affects a player's performance.
If I change an attribute that has CA weight, I have to change the player's CA to the new Recommended CA, otherwise it messes with the results.
I had my fingers crossed initially that the game would automatically just recalculate the CA for the player, but its not the case. It reduces every other attribute a little to make up for it.
When I added the correct CA, I got results that lined up with expectation (i.e. teamwork being marginally beneficial, rather than 8 > 14 teamwork producing significantly worse results)
GeorgeFloydOverdosed said: If I change an attribute that has CA weight, I have to change the player's CA to the new Recommended CA, otherwise it messes with the results.
I had my fingers crossed initially that the game would automatically just recalculate the CA for the player, but its not the case. It reduces every other attribute a little to make up for it.
When I added the correct CA, I got results that lined up with expectation (i.e. teamwork being marginally beneficial, rather than 8 > 14 teamwork producing significantly worse results)
Are you changing attributes in the game or the fixture object? If you edit the fixture JSON, changing the attributes should be all you need unless I'm very much mistaken.
(And, if I am, I can bake in the recalculation.)
LightningFlik said: Are you changing attributes in the game or the fixture object? If you edit the fixture JSON, changing the attributes should be all you need unless I'm very much mistaken.
(And, if I am, I can bake in the recalculation.)
I'm using a realtime editor to change the attributes in the game before exporting to JSON
I haven't taken the time yet to examine how your program works. So I don't know what you mean by 'fixture object'. Are you saying we can just edit the attribute data in the JSON file after export?
Let me know the process, because having to change the CA for each player is a real pain
GeorgeFloydOverdosed said: I haven't taken the time yet to examine how your program works. So I don't know what you mean by 'fixture object'. Are you saying we can just edit the attribute data in the JSON file after export?
Yes. The reason I added the much larger JSON type as an export is so people could modify it by hand. Open it and search for "home.player[0].ratings" (and [1] and [2] and so on). The first 15 values of that are positional ratings (1-20) then all the attributes. The name of the player is 15 lines up from this. I'm working to annotate more of these rows in the future but this should be enough to start.
EDIT: I'll try to make the fixture and players editable in the UI too, and improve the documentation.
= = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =
ATTRIBUTE-LEVEL RESPONSE IN FM24: DIJON VS LENS
EXPERIMENTAL SETUP
37 outfield attributes were tested at absolute in-game levels 4, 8, 12, 16, and 20.
In each condition, one attribute was set to the selected level for all 20 Dijon matchday players (starters and substitutes). The Lens, tactics, lineup, all other attributes, and fixture settings were held constant.
Each condition and the unchanged baseline used the same 500 paired seeds (1-500), for 93,000 simulated matches in total.
Excluded attributes: Left Foot, Right Foot, Corners, Penalty Taking, Long Throws, and Free Kicks.
The outcome is the change in Dijon's goal difference relative to the unchanged baseline. Positive values favor Dijon; negative values favor Lens. A positive change in goals conceded means Dijon conceded more.
MEAN RESPONSE ACROSS THE FIVE LEVELS
Attribute / Level 4 / Level 8 / Level 12 / Level 16 / Level 20 / Best Observed Level / Range (Max-Min)
Pace: -2.984 / -1.608 / -0.736 / -0.074 / +0.614 / Best: 20 / Range: 3.598
Acceleration: -2.496 / -1.430 / -0.522 / +0.214 / +0.726 / Best: 20 / Range: 3.222
Work Rate: -0.874 / -0.090 / +0.182 / +0.254 / +0.302 / Best: 20 / Range: 1.176
Agility: -0.538 / -0.246 / +0.054 / +0.164 / +0.388 / Best: 20 / Range: 0.926
Dribbling: -0.126 / +0.006 / +0.266 / +0.498 / +0.762 / Best: 20 / Range: 0.888
Jumping Reach: -0.292 / -0.256 / -0.028 / +0.250 / +0.596 / Best: 20 / Range: 0.888
Concentration: -0.304 / +0.008 / +0.190 / +0.282 / +0.476 / Best: 20 / Range: 0.780
Balance: -0.328 / -0.108 / +0.060 / -0.002 / +0.360 / Best: 20 / Range: 0.688
Stamina: -0.314 / -0.102 / -0.048 / +0.030 / +0.260 / Best: 20 / Range: 0.574
Anticipation: -0.278 / -0.074 / +0.068 / +0.216 / +0.268 / Best: 20 / Range: 0.546
Finishing: +0.104 / -0.142 / +0.036 / +0.360 / +0.392 / Best: 20 / Range: 0.534
Strength: -0.102 / +0.048 / +0.138 / +0.166 / +0.370 / Best: 20 / Range: 0.472
Important Matches: -0.054 / +0.034 / -0.028 / +0.294 / +0.396 / Best: 20 / Range: 0.450
Dirtiness: -0.018 / +0.114 / -0.092 / -0.096 / -0.294 / Best: 8 / Range: 0.408
Composure: -0.096 / +0.036 / +0.218 / +0.216 / +0.306 / Best: 20 / Range: 0.402
Determination: -0.182 / +0.034 / -0.060 / +0.198 / +0.104 / Best: 16 / Range: 0.380
Crossing: -0.080 / +0.076 / +0.160 / +0.288 / +0.244 / Best: 16 / Range: 0.368
Decisions: +0.190 / -0.096 / +0.054 / +0.254 / +0.178 / Best: 16 / Range: 0.350
Long Shots: -0.084 / +0.086 / +0.036 / +0.056 / +0.250 / Best: 20 / Range: 0.334
Consistency: +0.000 / +0.024 / +0.080 / +0.224 / +0.276 / Best: 20 / Range: 0.276
Heading: -0.014 / +0.128 / +0.034 / +0.144 / +0.238 / Best: 20 / Range: 0.252
Vision: -0.014 / -0.086 / +0.104 / +0.054 / +0.162 / Best: 20 / Range: 0.248
Teamwork: -0.062 / +0.042 / +0.060 / -0.026 / +0.150 / Best: 20 / Range: 0.212
Aggression: +0.094 / +0.056 / -0.044 / +0.042 / +0.148 / Best: 20 / Range: 0.192
Flair: +0.106 / +0.120 / +0.024 / -0.068 / +0.118 / Best: 8 / Range: 0.188
Marking: -0.008 / +0.088 / +0.118 / +0.132 / +0.178 / Best: 20 / Range: 0.186
Positioning: -0.014 / +0.032 / +0.024 / +0.058 / +0.156 / Best: 20 / Range: 0.170
Technique: +0.130 / +0.018 / +0.064 / +0.112 / +0.188 / Best: 20 / Range: 0.170
Leadership: -0.020 / +0.004 / +0.058 / -0.042 / +0.128 / Best: 20 / Range: 0.170
First Touch: -0.038 / +0.072 / +0.112 / +0.090 / -0.002 / Best: 12 / Range: 0.150
Injury Proneness: -0.010 / -0.006 / +0.088 / +0.114 / +0.010 / Best: 16 / Range: 0.124
Passing: +0.050 / +0.096 / +0.136 / +0.148 / +0.154 / Best: 20 / Range: 0.104
Bravery: +0.022 / +0.056 / +0.084 / +0.114 / +0.048 / Best: 16 / Range: 0.092
Off the Ball: +0.164 / +0.094 / +0.184 / +0.100 / +0.176 / Best: 12 / Range: 0.090
Tackling: +0.062 / +0.110 / +0.020 / +0.038 / +0.102 / Best: 8 / Range: 0.090
Versatility: -0.044 / +0.018 / -0.016 / +0.002 / +0.000 / Best: 8 / Range: 0.062
Natural Fitness: +0.000 / +0.000 / +0.000 / +0.000 / +0.000 / Best: 4 / Range: 0.000
This test shows that some attributes are indeed non-linear
@blackbird Love for this, can you possibly do this for GK as well🙏
blackbird said: ATTRIBUTE-LEVEL RESPONSE IN FM24: DIJON VS LENS
Love it! 93,000 matches though? Blimey. Some questions:
1. Did you write a script to perform these tests?
2. Did the game crash while running these matches?
3. How long did the entire thing take?
3. How large an increase do you consider to be significant and not just random noise? I notice some results dip with increases (Off the Ball) before rising again.
My passing/technique test finished. I changed a single player's passing and technique attributes to be every combination of 1-20 each and then played 100 matches. Here are the results: https://pastebin.com/D2iH45Vv
- Pass completion is 87% at the best and 85% at worst. Passing helps but it's a minimal impact.
- Passes attempted seems to lightly correlate with a higher passing ability
- Progressive passes is usually above 8 if passing and technique are double digits. It's usually below 7 when Passing is single figures (regardless of technique)
- High technique correlates with shots on target, but that's probably for reasons not related to passing
- Both correlate with key passes
- Dribbles correlates more with high passing than technique
- Possession lost correlates with low technique
Home field advantage test.
I've made Aston Villa play an exact copy of itself at Villa Park; same starting XI, same tactics, 1000 games:
home 351 / draw 307 / away 342
I repeated this at Ewood Park (to check whether your team owning the stadium makes a difference):
home 400 / draw 216 / away 384, avg 2.70-2.71
It's interesting that the home team still has a marginal advantage but draws plummet in this scenario.
Next I'm going to choose another neutral venue but alter the number of home and away fans. Ewood Park holds 31,363; here's what happens when they're split variously:
100% home fans: home 588 / draw 181 / away 231, avg 3.38-2.36
100% away fans: home 234 / draw 173 / away 593, avg 2.18-3.19
50%/50% fans: home 399 / draw 191 / away 410, avg 2.72-2.73
No fans: home 407 / draw 199 / away 394, avg 2.70-2.74
75/25 fans: home 389 / draw 195 / away 416, avg 2.72-2.79
75,000/25,000 fans: home 512 / draw 202 / away 286, avg 3.08-2.46
(These last two tests were to see if the absolute value of fans made the positive impact or the relative strength. Given the absolute number of fans makes a difference then there are performance gains in upgrading your stadium as much as possible, since every fan counts.)
These fields are labelled in JSON files as
- venue.attendance
- venue.home_fans
- venue.away_fans
- venue.capacity
You can change these yourselves if you want to experiment with this or try to create the most balanced fixture as a base.