Training

[sylvainmeteo]

Do you understand the basics of the theory of probability? Because that's what the trainings all about... Btw, I've been playing this game for almost 10 years, and I've seen almost everything about training probability. Twice missed >90% in a row and the opposite, twice taken <10% in a row, for example. Most interestingly, nobody ever seems to complain about the latter incident... And in my opinion, this training system is one thing that makes this game so special and interesting.

Personnally since I saw SM success 4 1% in a row...
Anyway, I am agree with FL, I don't know another game where I wait 4 times in a month 00h41 just to confirm that I am an unlucky man, I hate and like that at the same time. And because of that, we can't ride the same way, it is this which give us multiple possibility in the game.

Hum, luckiest think I had was to get 2/3/6% at the same time with Gidis (triple with 26 of age) 1 out of more than 1 million...
Also once 4 10% in a row (and not 1% poké ^^).
But sometimes I'm very unlucky, like with Szabo, Staponkus and all my pavé team...

[lennylenny]

here is my total training this month: Results | Trainingchances Mountain Flat Downhill TT Sprint Trainingluck(personal opinion): Unlucky bit unlucky average bit lucky lucky
Kyle Anderson: +4 Mountain +2 Sprint | 100% 0% 0% 0% 37%
Jyhan Artut: -2 Downhill | 2% -16% -17% 3% 0%
Paul Balto(only 3 trainings) +3 Flat | 0% 59% 0% 0% 0%
Stephen Bunting: -1 Downhill | 0% 1% -17% -18% -17%
Jeffrey deZwaan: +4 Mountain | 34% -12% -11% 4% 0%
Scott Kirchner: -1 Mountain -1 Flat -2 Downhill -1 TT -1 Sprint | -49% -36% -49% -49% -49%
Christian Kist: +1 Mountain -1 Downhill | 64% 6% -11% 0% 0%
Jamie Lewis: +1 Flat | 3% 8% 2% -14% 0%
John Part: -1 Mountain -1 TT | -1% 4% 0% -15% 0%
Gerwyn Price: +1 Flat -1 TT | 2% 6% -14% -12% 0%
Cristo Reyes: +1 Mountain +1 Flat | 22% 2% -12% 0% 0%
RowbyJohn Rodriguez: -1 Flat -2 Downhill -1 TT -1 Sprint | -14% -25% -25% -22% -25%
Sascha Stein: nothing | 0% 1% 1% 0% 0%
Mensur Suljovic: -2 TT | 0% 1% 0% -17% 0%
Dimitri vandenBergh: +2 Flat | 0% 32% 5% 0% 0%
Jermaine Wattimena: +4 Mointain +4 Flat | 100% 80% 0% 0% 0%
Dean Winstanley: +1 Mountain -1 TT | 14% 1% -15% -13%

[Coroncina2]

Do you understand the basics of the theory of probability? Because that's what the trainings all about... Btw, I've been playing this game for almost 10 years, and I've seen almost everything about training probability. Twice missed >90% in a row and the opposite, twice taken <10% in a row, for example. Most interestingly, nobody ever seems to complain about the latter incident... And in my opinion, this training system is one thing that makes this game so special and interesting.

I'm probably not one of best player here but in math I was quite good, at least good enough to know that probability shows only show almost nothing with few dates. Example trough a dice with 6 faces few times we can get 2 times a 6 or a 3 and a 5, really difficult to know the result. But with big amount of data we will see almost the same number of times the same faces. Teorema di Bernoulli
So here it's ok if twice I lost 90% and twice I takes 10% but I'm talking about all team in all year played. If system work for all at same way we wouldn't have same monsters in same teams.
Now I thought a way to misure the lucky coefficient of training:
The most point we have with the fewest percentage we training should show the lucky we have.
Example:
Xuri Zhang training with 8% 1% 1% 0% 1% and got 1 mountain
Average training 2,2% average point 0,2
Lucky coefficient 9.09 here I think I was lucky
Dainese (aux ttrider ) train 0% 0% 0% 81% 0% and normally ( or at least not so lucky as Xuri ) got tt point
Average training 16% average point 0,2
Lucky coefficient 1.23
If we use it for all team we will see how much lucky we are.
With last training session I got -0,2pt\-4,63% so 0,48 lucky coefficient.
You?

[Cerro Torre RT]

Please explain to me the theory of this, luck coefficient, best by quoting scientific papers, because my late night search of it as well as the the german translation which I thought to be adecuate just led to nothing. I'd be really interested to know what that really is, because just the number tells nothing.

[Coroncina2]

The coefficient is average of point get / average % training used to get them.
So in example xuri that get 1 in mountain with only 8% has 9, Dainese with 81% training a tt point have 1.23
Don't you think the more train we have with low % the more lucky we are?

[Cerro Torre RT]

Well, my first point to this kind of calculation is: Why to include the 0% trainings? They got nothing to do with chance or luck, their result is sure.

That leads to something that states well better for what you mean to calculate (at least I think that's what you want).
I would recommend to calculate the "expected value", which is a well established in mathematics. You can then either devide your awarded points by that expected value or, even better, subract both, because that subtracted value has 2 advanteges: It will not be influenced by 100% trainings and you can just sum it up over all your trainings without wronging it by the influence of number of riders, number of 100% or 0% trainings and so on.

That, still, leaves out one very important thing with riders over 24 years: If truly wanting to know who is lucky or not, it is of utter importance not only if he trains, but which of those possible values he traines, as there are more important or less important ones. For example, I defend downhill skill for my flat riders, with one training. it's usually around 1% chance. If that gets trained, numbers will state I'm very lucky. In fact, that doesn't change anything for me. But if he trains the 6% on flat, then I'm much more lucky, and it will show in the numbers, but not as much as the downhill training though having a far larger importance. Thus, training luck depends heavily on the value trained and the kind of rider and what the team wants to do with him. Don't know how to put that in a number. But just ignoring that for the sake of beeing able to put a number on it in order to justify will be misleading.

[Coroncina2]

All this just bècausè I sèè èvèr samè supèr ridèrs in samè tèams.
In all yèars could bè strangè.

[Coroncina2]

Well, my first point to this kind of calculation is: Why to include the 0% trainings? They got nothing to do with chance or luck, their result is sure.

considèr 0s or not doèsnt mattèr...

100%+0%+0%+0%+0% and +1 0 0 0 0
Average training 20% and Average points 0.2
lucky coefficient of training 0.2/20% = 1
100% and +1
Average training 100% and Average points 1
lucky coefficient of training 1/100% = 1
just bèttèr for mè put all in èxcèl calculator
Ok nicè quèstion considèr only kèy skills for cèrtan ridèrs

[Cerro Torre RT]

Ok now I see. I couldn't imaginge why to devide both by the number of trainings because that just eliminates itself.
(2/5)/(110%/5)=2/110%. As the result of every training is either 0 or 1, adding all training percentages equals the expected value.

So while you are right that your value might give you an idea of where you stand in terms of simple training mechanics, I have 2 more things to conisder in terms of impact to the game: Which rider trains, and timing. Training for captains is usually far more important then a cheap helper. With timing, I'd take a rider from my own team to explain: If Guðmundður Pálmason trains 5 times flat in in hte next 10 trainings, and nothing in the next 10, I'm extremly lucky. If he trains nothing for the next 10 trainings, and 5 times flat in the 10 following ones, it doesn't mean a thing, because Paris Roubaix is over.

[sylvainmeteo]

Training 5.2.2016:
Gorazd Batagelj Berg: -1
Gorazd Batagelj Flach: -1
Gorazd Batagelj Abfahrt: -1
Vojtech Kankovsky Zeit: -1
Vojtech Kankovsky Sprint: -1
Jonas Gregaard Berg: -1
Michael CarbelSvendgard Abfahrt: -1
Andreas Gylling Berg: -1
Valdis Ernestovskis Abfahrt: -1
Evaldas Sakalauskas Abfahrt: -1
Krisztian Szabo Berg: +1
Ernestas Staponkus Flach: +1
Ernestas Staponkus Abfahrt: -1
Myrkvi Kristofersson Berg: +1
Luka Korosec Flach: +1
Gottskalk Kolskeggursson Berg: +1
Gottskalk Kolskeggursson Flach: +1
Gasper Katrasnik Flach: +1
Bjornolfur Thorvaldsson Berg: +1
Raivis Deksnis Berg: +1
Raivis Deksnis Flach: +1
Raivis Deksnis Abfahrt: +1

Longuest ever
Gasper 84/67 now, back to TT
Staponkus after failing for 4 months got 2 flat in a row
rest is pretty good, except Thorvaldsson missing too much flat, and too much malus...

[Robyklebt]

Training 5.2.2016:
Sinclair Saddler Flach: -1
Jiri Topal Berg: -1
Jiri Topal Flach: -1
Jiri Topal Abfahrt: -1
Jiri Topal Zeit: -1
Jiri Topal Sprint: -1
Lakhdar Gaouaoui Flach: -1
Lakhdar Gaouaoui Abfahrt: -1
Lakhdar Gaouaoui Zeit: -1
Lakhdar Gaouaoui Sprint: -1
Cestmir Matuska Berg: -1
Cestmir Matuska Flach: -1
Cestmir Matuska Zeit: -1
Cestmir Matuska Sprint: -1
Manuel Vargas Flach: -1
Manuel Vargas Abfahrt: -1
Selwyn Cayetano Berg: +1
Preben Dahlerup Flach: -1
Cheik Faye Flach: +1
Teppei Umesao Sprint: -1
Joao Peixe Flach: +1
Indrek Rääts Flach: +1
Indrek Rääts Sprint: +1
Ravishankar Ganguly Flach: +1

Finally something slighlty different...
Cayetano mountain, Peixe flat... mountain would be so much nicer. Ganguly flat, instead of sprint. And Modesto refuses the risky 75-73 treatment. Rääts on the other hand gets a double, not sure why he was training sprint actually.

[Coroncina2]

ALLENAMENTO DEL 05/02/2016
Avilès -68%(-1) -81% (-1) -81% -81% -78%(-1)
Dèjano 8% 1% 2% 2% -14%
èsposito -13% -11% -14% -14% 0%
Fabbri -33% (-1) -31% -34% -34% -36%
jèntilè 3% 0% 0% 0% 0%
Nicolètti 2% 2% -14% -14% 13%
Puddu -46% (-1) -44% (-1) -47% -47% -49%
Quinzi 0% 0% -11% 0% 32% (+1)
Rinonj 0% 50% (0) 0% 0% 0%
Rocco -38% (-1) -47% -46% (-1) -46% -49% (-1)
Saldi 0% 65% (+1) 0% 0% 69% (0 )
Sali 78% (+1) 92% (+1) 0% 0% 0%
Spranjèn -8% -24% -25% -25% -25%
Tonjpanj -10% 31% (0) -12% -12% 0%
Van Noort -64% -55% -64% -64% -64%
Xiaotinj 9% 0% 1% (+1) 1% 0%
Yoon 18% (+1) 1% -14% -14% 0%
Zanj 5% 1% 0% 0% 0%

[team fl]

Lampe continues be a small light in training. But Megaranto makes up for it. First three skill training for ages at Team FL! Rest as expected. Zwiebach although could have keept at least the flat part for himself.

[Alkworld]

ALLENAMENTO DEL 05/02/2016
Avilès -68%(-1) -81% (-1) -81% -81% -78%(-1)
Dèjano 8% 1% 2% 2% -14%
èsposito -13% -11% -14% -14% 0%
Fabbri -33% (-1) -31% -34% -34% -36%
jèntilè 3% 0% 0% 0% 0%
Nicolètti 2% 2% -14% -14% 13%
Puddu -46% (-1) -44% (-1) -47% -47% -49%
Quinzi 0% 0% -11% 0% 32% (+1)
Rinonj 0% 50% (0) 0% 0% 0%
Rocco -38% (-1) -47% -46% (-1) -46% -49% (-1)
Saldi 0% 65% (+1) 0% 0% 69% (0 )
Sali 78% (+1) 92% (+1) 0% 0% 0%
Spranjèn -8% -24% -25% -25% -25%
Tonjpanj -10% 31% (0) -12% -12% 0%
Van Noort -64% -55% -64% -64% -64%
Xiaotinj 9% 0% 1% (+1) 1% 0%
Yoon 18% (+1) 1% -14% -14% 0%
Zanj 5% 1% 0% 0% 0%

The important question is: Do you think you were lucky? Or was the training system unfair to you?

[radekdeboktor]

This is a lucky guy...

Brendan Hooker Flach: -1
Brendan Hooker Abfahrt: -1
Brendan Hooker Zeit: -1
Brendan Hooker Sprint: -1

[Coroncina2]

The important question is: Do you think you were lucky? Or was the training system unfair to you?

Training system could bè fair if not samè tèams got samè supèr ridèrs with supèr training

[Alkworld]

The important question is: Do you think you were lucky? Or was the training system unfair to you?

Training system could bè fair if not samè tèams got samè supèr ridèrs with supèr training

That's not an answer to the question. But anyway, you had 6 positive trainings while the expected number was around 4,8. So you were lucky and should at least acknowledge that.

[Pokemon Club]

My training is way worst anyway, so be happy with what you have

[Coroncina2]

The important question is: Do you think you were lucky? Or was the training system unfair to you?

Training system could bè fair if not samè tèams got samè supèr ridèrs with supèr training

That's not an answer to the question. But anyway, you had 6 positive trainings while the expected number was around 4,8. So you were lucky and should at least acknowledge that.

Im normal lucky: nicè sprintèrs but usèlèss if my flat ridèrs training only till 87 86 and also sonè nicè classic likè Marcèl Avilès. For mè is a bit stangè that always samè tèams
got samè supèr ridèrs with supèr trainin : kaju, Alkorld, Ambition, Rockstar Inc, World Wildlifè No morè idols...

If you think I wrong why not writè your training in detail likè mè?

[lennylenny]

Im normal lucky: nicè sprintèrs but usèlèss if my flat ridèrs training only till 87 86 and also sonè nicè classic likè Marcèl Avilès. For mè is a bit stangè that always samè tèams
got samè supèr ridèrs with supèr trainin : kaju, Alkorld, Ambition, Rockstar Inc, World Wildlifè No morè idols...

If you think I wrong why not writè your training in detail likè mè?

i don't see the super riders of Rockstar Inc, World Wildlife
NMI only has one lucky rider
Ambi has two lucky flat guys with 17 riders in his team
Alk has one lucky rider with 20 riders in his team
and Kaju: Abramenko bought when he already was 73-86
Raivo had awesome starting skills
Tomi was bought when he already was 65-83
Rayan was bought as 65-85
Wolfgang was bought as 61-86
so Kaju also wasn't really lucky

[Coroncina2]

Im normal lucky: nicè sprintèrs but usèlèss if my flat ridèrs training only till 87 86 and also sonè nicè classic likè Marcèl Avilès. For mè is a bit stangè that always samè tèams
got samè supèr ridèrs with supèr trainin : kaju, Alkorld, Ambition, Rockstar Inc, World Wildlifè No morè idols...

If you think I wrong why not writè your training in detail likè mè?

i don't see the super riders of Rockstar Inc, World Wildlife
NMI only has one lucky rider
Ambi has two lucky flat guys with 17 riders in his team
Alk has one lucky rider with 20 riders in his team
and Kaju: Abramenko bought when he already was 73-86
Raivo had awesome starting skills
Tomi was bought when he already was 65-83
Rayan was bought as 65-85
Wolfgang was bought as 61-86
so Kaju also wasn't really lucky

aftèr this I stop

[lennylenny]

Im normal lucky: nicè sprintèrs but usèlèss if my flat ridèrs training only till 87 86 and also sonè nicè classic likè Marcèl Avilès. For mè is a bit stangè that always samè tèams
got samè supèr ridèrs with supèr trainin : kaju, Alkorld, Ambition, Rockstar Inc, World Wildlifè No morè idols...

If you think I wrong why not writè your training in detail likè mè?

and Kaju: Abramenko bought when he already was 73-86
Raivo had awesome starting skills
Tomi was bought when he already was 65-83
Rayan was bought as 65-85
Wolfgang was bought as 61-86
so Kaju also wasn't really lucky

aftèr this I stop

what is the funny part of Kaju also wasn't really lucky? he just bought the monsters from the market, so he didn't had training luck with them

[NicoVanarlo]

Ok, I just read that post, I guess you talk about fenna when you say world wildlife.

Fenna started with 56/74/66 sprint, her training was just horrible.

She failed 1 flat training at 90% and 2 at 50%where's the luck? She never took flat point after...
Berg? ok, 56=>74, a bit lucky but not incredible
and sprint, 66=>71, she just took one small trainig, all the others was "normal"

So ok, Fenna was strong, but only cause I bought her with high skill, not because of training.

[Pokemon Club]

Ok, I just read that post, I guess you talk about fenna when you say world wildlife.

Fenna started with 56/74/66 sprint, her training was just horrible.

She failed 1 flat training at 90% and 2 at 50%where's the luck? She never took flat point after...
Berg? ok, 56=>74, a bit lucky but not incredible
and sprint, 66=>71, she just took one small trainig, all the others was "normal"

So ok, Fenna was strong, but only cause I bought her with high skill, not because of training.

Fenna was a total shit. The only awesome panda I can remember was Tom VanAsbrock with his 73-85-7x 66 sprint.
And in fact, except SM who can have a 76-82 classic when he wants or successs 4 times 5% in a row, there is not people luckier than others.

Personally except Mikoto Suou who success 20/24 flat training before to win PR, I had excellent training but nothing crazy : Taiga only 88-57 in her best days, Kanako 69-56 87sp, Kinomoto 60-75 80sp, Koko 63-86, Nene 54-89, Akira 50-90, Sawachika 73-82-81, Sora 71-81 66sp...

[NicoVanarlo]

Tva got 63 sprint, not 66...