I have been working pretty hard to break the age influence formula. I came close, but it was not a 100% success. I have found the mathematical workaround, which gives around 0.04-balls error for the team as a whole. That would be 33 players. I think that is pretty neat.

The graph can be found here:
https://imageshack.us/scaled/large/819/rsaigraph.png

The graph is based on the sample of 33 players of Wojskowi Zawiercie. As you can see, it shows that the age influence bends at least twice. What I could read from the graph and from the help, confirms the following findings:
1) The age influence for players up to ca. 21.5 years old is non-existant, meaning the skills are not impaired by age.
2) Around the age of 21.5 the age influence starts to show and is, more or less, a straight line up to 25.5 - 26 years old, where it is at ca. 92-93%. My findings indicate, that the age influence at that stage is between 1.733% - 1.944% per season/year.
3) After ca. 26 yo, the age influence increases and goes down more rapidly (the influence is stronger). This time, I was able to norrow it down to between 2.441% and 2.530% per season/age.

* I assume there is another break of the line where the age influences accelerates, yet again, but my players are not that old yet, and I was unable to verify it. Time will tell.

The values are usually rounded up to a number of digits after coma (probably no more than 2), which make is impossible to analyze it precisly.

This is my analysis. The further, more interesting findings, are for you to discover. The economical impact of this should be pretty clear - it is to me, but I leave it for you to discover. Hopefully, it will help you some.

If you have any questions, just go ahead.

thks ace

When you said it increases more rapidly, I immediately assumed it was a quadratic (it had a square in it, for the non-maths people) equation. This would speed up calculations a lot, as age influence could be done in one calculation (calculate it for all players, and cap that answer at 100%).

Using 50 non-U21 players of the top three in Japan as my sample - the 48 from the first teams, and 2 from my reserves, I got a very approximate formula of:

93.5 + 2 * (age) - 0.08 * (age)^2

By going to greater accuracy I could get a better fit, but I don't believe the formula is that complex - it doesn't strike me as Vincent's style to go to many decimal places.

I think age decline starts at the 21st birthday, but currently doesn't show until about 21.5 years. By 35, the age factor will be 65-66%, which on face value seems a big hit but by then managers will probably be looking to add specialist training.

I suspect that, in Rocking Rackets terms, all players are "100% age" players, and that once the game is settled and Delft are happy with how this "default" age curve works they will introduce players from "95%" (late bloomers) to "105%" (fast-growing youngsters who age quicker).

I also do not belive the formula is too complex. I think there are -if- conditions, which change the formula basing on age. The trick is to catch the age brackets and the formula.

Nice work katsudon & Ace,

With rounding, that formula fits almost all of my players. I have about 4 players that it doesn't work for. And they are out by less than .1%.

katsudon used square function, I used two linears. His one is probably closeer one.

A good post.Thank you for the contribution,ACE.I hope to there
are more and more good post in the fourm

Ace and katsudon: You have a lot of spare time to compute this formula, I totally respect you. ( I'm a math teacher, but don't care about solving this problem.)

Maybe like "Rackets" every player have individual "age factor".

Sima, no, for sure not. Our formulas, although different, are too close to be accidental. There is a formula, but there must be a trick somewhere there.

Very interesting post. Brings a better understanding, and help on training/transfer strategies.
Thank you !