Subpage under development, new version coming soon!
Asunto: NEWS Sokker- big changes are coming!
Because it only shows you his potential level of he pops every 4 weeks (talent)
Obviously it’s useless and harmful for new users who don’t know how YS works, it’s known since the day it was introduced
Obviously it’s useless and harmful for new users who don’t know how YS works, it’s known since the day it was introduced
Not only it is harmful for new users, but it show how the random system itself is absurd for every users. But since you didn’t vote (for just a minimal reform) it doesn’t matter to you, isn’t it ? :-p
I didn't vote because:
a. I knew they won't give a fuck about it
b. I didn't think your idea is a good solution
a. I knew they won't give a fuck about it
b. I didn't think your idea is a good solution
a. Almost true ;-) in fact we got a reply from Dev. In the end, we definitely agree.
b. it never claimed to solve a whole problem but removed some of the nonsense. In the meantime, lucky for you, we can still get in 18y and tragic entries each weeks ;-d
b. it never claimed to solve a whole problem but removed some of the nonsense. In the meantime, lucky for you, we can still get in 18y and tragic entries each weeks ;-d
It doesn’t matter, I don’t see a point in fixing problems halfway (because then it will stay that way for years) or with creating different, new problems.
Michal Káník 19 outstanding [12] outfield 1 = 36 to 39 skillsum | result: sumskill 32 or 34 with stamina
Richard Vavřička 18 excellent [10] GK 2 = 31 to 34 skillsum | result: sumskill 21 or 28 with stamina
Prokop Mestl 18 formidable [11] outfield 3 = 35 to 38 skillsum
Marcel Kudláček 18 formidable [11] outfield 3 = 35 to 38 skillsum
Arnošt Heřmánek 17 good [7] outfield 3 = 23 to 26 skillsum
We have fail #2, although its a goalkeeper, not sure if that counts.
result: sumskill 21 or 28 with stamina
(editado)
Richard Vavřička 18 excellent [10] GK 2 = 31 to 34 skillsum | result: sumskill 21 or 28 with stamina
Prokop Mestl 18 formidable [11] outfield 3 = 35 to 38 skillsum
Marcel Kudláček 18 formidable [11] outfield 3 = 35 to 38 skillsum
Arnošt Heřmánek 17 good [7] outfield 3 = 23 to 26 skillsum
We have fail #2, although its a goalkeeper, not sure if that counts.
result: sumskill 21 or 28 with stamina
(editado)
Its not terrible, but he will be for sale in the evening (if i dont foget):
Richard Vavřička, age: 18 , sum(21)
club: Ajax Trnava, country: Česká republika
value : 2 805 008 Kč
wage: 27 751 Kč
very good [9] form
tragic [0] tactical discipline
height: 180 cm, weight: 81.6 kg, BMI: 25.19
NTDB:
good [7] stamina good [7] keeper
weak [4] pace hopeless [1] defender
tragic [0] technique unsatisfactory [2] playmaker
good [7] passing tragic [0] striker
Richard Vavřička, age: 18 , sum(21)
club: Ajax Trnava, country: Česká republika
value : 2 805 008 Kč
wage: 27 751 Kč
very good [9] form
tragic [0] tactical discipline
height: 180 cm, weight: 81.6 kg, BMI: 25.19
NTDB:
good [7] stamina good [7] keeper
weak [4] pace hopeless [1] defender
tragic [0] technique unsatisfactory [2] playmaker
good [7] passing tragic [0] striker
Since stamina scale is not like other skills it could explain a sumskills/level lower than expected
And, once again, i note few zero skills when sumskills/level are lower than expected
And, once again, i note few zero skills when sumskills/level are lower than expected
First of all it is not possible to predict with 100, or even 95% accuracy.
It is still JUST estimation.
I've made "formula" which recounts level of junior (with decimal places) to a value. So in this place already, where input is also estimation - predicted sumskill can be wrong.
But still... When I take all juniors data I've collected (excluding GKs) an average estimation mistake is close to... 0 (-0,148 to be exact)! Which is nice result.
Here is graph - mistake vs sumskill, it doesn't show any trend.
And mistakes distribution - 65% of predictions is mistaken max by 1 point. Another 25% by 2 points. Just 10% is mistaken by 3 or more.
It is still JUST estimation.
I've made "formula" which recounts level of junior (with decimal places) to a value. So in this place already, where input is also estimation - predicted sumskill can be wrong.
But still... When I take all juniors data I've collected (excluding GKs) an average estimation mistake is close to... 0 (-0,148 to be exact)! Which is nice result.
Here is graph - mistake vs sumskill, it doesn't show any trend.
And mistakes distribution - 65% of predictions is mistaken max by 1 point. Another 25% by 2 points. Just 10% is mistaken by 3 or more.