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1v1G because it trains HA quicker on the bigs and IS actually trains slower the taller the player is.

It's like you guys have never looked at the training simulator or coachParrot which use the coefficients estimated based on that data or just go by hearsay.
1. 1v1G and 1v1F train HA and DR exactly the same way
2. The training on HA and DR is unaffected by height

IS actually trains slower the taller the player is.

IS always trains faster for taller guys. Always. Both as primary (IS) and secondary training (ID, RB, JSF, 1v1F).

As I said before, JS has an elastic effect on IS but not vice versa so if you are planning to have the most TSP, then yes 1v1G will give you highest TSP. However since it always results in less IS it's usually not a good choice in terms of salary, cap and on-court efficiency, except for a few selected situations which I already listed.





Last edited by Lemonshine at 11/29/2019 6:24:46 PM

Is it possible that the IS training speed slowed down because of negative elastics. Was that factored in your old data? I got to agree with Lemonshine on this topic. I would have to say that coach Parrot is 90% accurate for me. Maybe you can share your data with us to compare.

This is the table used in coachParrot for elastics calculations:
https://imgur.com/smjYhlI

This is the table with the training coefficients:
https://imgur.com/gm8zvYB

And these are the other tables for height, age, trainer (cap coeff was most definitely changed by me):
https://imgur.com/2YbTZLX

I now see that rhyminsimon's tool had a decresing relationship, but he also has 1v1F being faster than 1v1G by over 10% for short players, which sounds highly unlikely. The distortion in the coefficients for HA and DR is likely due to skills that affect HA but not DR via elastics (OD). If you look through his coefficients (which were older than the ones Jozef Ka worked with) there are a lot of things that make little sense. If you look at IS training at 6'0'' he estimates the secondary training on JS to be 3% of primary training, while at 7'6'' he estimates a 30%.

I would need to see the dataset and run my own analysis, but at a glance I'd say rhyminsimons numbers defy common sense. According to that, JR and SB benefit from no elastic effect, so yeah.

Last edited by Lemonshine at 11/29/2019 10:07:18 PM

I think the math was what turned me off in training analysis when I was still a newbie. I have seen a couple of 150+ tsp guys (not a lot) in my 25+ seasons playing this game. It would be nice to see how their trainers pulled it off and what method they used. More recently, a Lithuanian manager broke down the table of Parrot which i thought made training very simple to understand even to a newbie with average intelligence. My mistake before was under estimating height in training speed its advantage and disadvantage. I constantly refer to that table to this day when I need to think about a training plan because it is convenient, and I do find it close to simulating training in this game that we all play.

Though both of you are in opposite views, its not productive to pursue because the conclusion was not based on the same data. So i am just going to go back to what works for me as a trainer. So unless i see more 150+ TSP hofs and atg on the transfer list i am not going to waste energy arguing 2-3 more skill points here and there.

So you believe that training IS for tall players you get 1 JS pop for every 3 IS pops and for a 6'0'' player you get 1 pop every 30? The difference is so large that this should be easy to test.

Crosstraining seems to be a % of the total training going to 1 or more random skills (and presumably the gym replicates that 1x-3x). Whether restrictions exist to this allocation could make a difference, because if it's completely random it could also fall to on the primary training skill. Crosstraining also creates noise in the data.

The PA and 1v1 connection to height is very bland even in rhyminsimon coefficients and it's certainly not as significant as it is for other skills. And since there a multiple variables involved here, I assume the relationship between all the variables was tested. For example, the PA slope may be entirely explained by elastic effect and the typical configuration of skills for players of different height. This could mean that the minimal connection with height is in fact no connection at all as it's driven by other variables highly correlated with height.

Using coachParrot I have not been getting pops in HA with 1 specific player (at least 3 several consecutive pops), while DR was on point. With each pop the margin of error should be decreasing, so something else was going on there. That player in particular had a fairly big imbalance between HA and DR. Using the other coefficients (the training simulator one) the estimate would have been even more off.

Last edited by Lemonshine at 11/29/2019 9:52:37 PM