POWERPLAY 雜誌

Evaluating player qualities

The purpose of this article is to discuss how qualities and training ratios impact training.

Assumptions:

• Training has a linear relationship with the quality of the attribute being trained.

Imagine a situation where you have two players at a position and you are only going to train 4 attributes for each of those players.  The players are identical except for their qualities (same age, CL, etc.).  How do you determine which player has better qualities for that position?

For illustrative purposes, let's say that those players have qualities of 90/90/30/90 and 65/65/65/65 in the attributes to be trained for that position.  Is that enough information to decide which player has better qualities for that position?

Here's a word problem to think about (you may have seen this problem or a very similar problem before):

A car is going on a 60 mile journey.   The first 30 miles are up a mountain road and the last 30 miles are down a road on the other side of the mountain.  Going up the mountain, the car averages 30 miles per hour.  How fast does the car need to average going down the mountain to average 60 miles per hour for the entire journey? 

One way to tackle this problem is to think about the journey in aggregate.  If a car is going to average 60 miles per hour for a 60 mile journey, then the car needs to complete the journey in 1 hour.  How long did it take to drive the first 30 miles?  At 30 miles per hour for 30 miles, it took 1 hour. 

You can think of a player's qualities as the speed at which they train the associated attribute.  Continuing this analogy, the ratio you train the player at determines the distance you train each attribute.  What we want to determine is what the effective speed is based on the ratios we're using.

Using the two players mentioned above; what if we want our players at that position to be trained at a 2:1:1:1 ratio.  Note that since it is a ratio, it will be repeated as many times as training speed allows.  The 90/90/30/90 player is going to train attributes with a speed of 90 for a distance of 4 and an attribute with a speed of 30 for a distance of 1.  The time spent to train the 90 qualities is 4/90 and the time spent to train the 30 quality is 1/30.  The total time is 7/90 to cover a distance of 5.  Thus the aggregate speed is 5 / (7/90) = 64.29. 

The 65/65/65/65 player is going to train attributes with a speed of 65 for a distance of 5.  The total time spent is 5/65 and the aggregate speed is 5 / (5/65) = 65 (as you might expect).

If you're going to train those players at a 2:1:1:1 ratio, then the 65/65/65/65 player will train slightly faster than the 90/90/30/90 player (all else equal).    However, if you're comfortable changing your ratios for individual players, then there are many ratios where the 90/90/30/90 player will train faster (Example: 2:1:0:1). 

Some other examples of how you can apply these concepts are:

• How will adding another attribute to a training ratio impact average training?
• Based on positional ratios, which position would a player train fastest?

Some other things to consider:

• When training towards a new ratio, a player will train at one or more different ratios until their attributes reach the new ratio. 
• Switching primary attributes for a player results in an effective loss in OR.  When doing so, evaluate the catch-up point.  That is the point at which the effective OR at the new position will equal what the effective OR at the old position would be if the player did not switch positions.