I tried to perform similar analysis on EBFM's data to identify the best training session and ran into a problem.
The testing he performed involved training ONLY the specific training for a whole season. This resulted in any attribute which wasn't trained by the specific training degrading. This has the effect of:
-Messing with the measured CA growth of the training (since it will be the sum of the attributes increases and decreases) resulting in lower CA growth for more focused trainings. -Potentially changing the attribute growth measured as it there will be a different CA-PA delta than if there was no or minimal attribute degradation.
Since the goal here is to general identify which training sesssions are most efficient when combined with a generally (somewhat) balanced program, I think we need to gather another dataset in which each specific training session is tested alongside a balanced program (perhaps 1 attacking,defending,physical,match-practice) rather than in isolation.
something popped into my mind while watching EBFM's series that I guess might be relevant here. A lot of these tests look to maximise CA growth per time from training. Given that: -Attributes 'cost' different amounts of CA depending on position. -Players grow faster the larger the difference between their CA and PA. -We can estimate the quality of a player with the attribute weightings.
I think it might be better to maximise the value (weighted attribute) while minimising CA growth.
Another way of looking at is that we want to 'spend' our CA->PA growth in the most efficient way to maximise the weighted attribute values. My feeling is that broad/general training programs like EBFMs will tend to grow most attributes evenly. This is probably the fastest way to grow CA overall, but I think you will get higher quality players if you can focus the attribute growth on highly weighted attributes.
The testing he performed involved training ONLY the specific training for a whole season. This resulted in any attribute which wasn't trained by the specific training degrading. This has the effect of:
-Messing with the measured CA growth of the training (since it will be the sum of the attributes increases and decreases) resulting in lower CA growth for more focused trainings.
-Potentially changing the attribute growth measured as it there will be a different CA-PA delta than if there was no or minimal attribute degradation.
Since the goal here is to general identify which training sesssions are most efficient when combined with a generally (somewhat) balanced program, I think we need to gather another dataset in which each specific training session is tested alongside a balanced program (perhaps 1 attacking,defending,physical,match-practice) rather than in isolation.
-Attributes 'cost' different amounts of CA depending on position.
-Players grow faster the larger the difference between their CA and PA.
-We can estimate the quality of a player with the attribute weightings.
I think it might be better to maximise the value (weighted attribute) while minimising CA growth.
Another way of looking at is that we want to 'spend' our CA->PA growth in the most efficient way to maximise the weighted attribute values. My feeling is that broad/general training programs like EBFMs will tend to grow most attributes evenly. This is probably the fastest way to grow CA overall, but I think you will get higher quality players if you can focus the attribute growth on highly weighted attributes.