PowerPlay Magazin

One of the great mysteries of PPM is the right balance of attributes. Scout the best hockey national teams, or simply the best teams, the balance of their players will always be around 100-50-50 (primary attribute - secondary attribute - secondary attribute).

However, here is what we can find in the PPM Hockey guide:

• Player with attributes 180 - 25 - 25 or 70 - 90 - 90 (where the first one is the primary main attribute and the last two are secondary main attributes) is not as good for the given position as a player with attributes 120 - 30 - 50. Similarly a player with attributes 130 - 80 - 30 or 80 - 80 - 80 is not as good as a player with attributes 100 - 80 - 50.

Is the optimal distribution 100-50-50 compatible with the guide?

Like Sherlock Holmes, we will list the clues and when comes the time, we'll make deductions and compare them with the facts.

We will start by drawing the outline of what the guide tells us:

1. Having a primary attribute too high compared to two secondary attributes is negative, it can be seen with 180 - 25 - 25 which is lower than 120 - 30 - 50.

2. Having a primary attribute too low compared to two secondary attributes is negative, as can be seen with 70 - 90 - 90, which is lower than 120 - 30 - 50, or 80 - 80 - 80, which is lower than 100 - 80 - 50.

3. Too great an imbalance between two secondary attributes is negative, can be seen with 130 - 80 - 30 which is lower than 100 - 80 - 50.

4. The explanation says: there are three attributes, the main and two secondary. And not a primary, secondary and tertiary one: so in the example 120 - 30 - 50 it might well have been 120 - 50 - 30 and 100 - 80 - 50 might well have been 100 - 50 - 80 etc. The two sides are equal and that's good because if there was a tertiary, 120 - 30 - 50 would become inexplicable.

Clues were not complicated to extract from the guide. Now it's time for us to use a little bit of our gray matter.

1) We deduce that the secondary attributes are a constraint on the primary attribute: if too high compared to them, after a moment, it does not matter. Same thing for 2): if the primary attribute is too low, the secondary attributes beyond a certain threshold will no longer count. From 3) and 4) we understand that there may be a reasonable gap between the two secondaries, but not too excessive, either.

Taking 100-50-50 as a known working distribution, we will try to formulate a theory with deductions based on 1) and 2). We assume that if PA> SA 1+SA 2, then PA = SA 1+SA 2 (PA = Primary Attribute, SA = Secondary Attribute). Similarly, if SA> PA / 2, then SA = PA / 2. Check this:

1. 180-25-25 changes to 50-25-25 while 120-30-50 becomes 80-30-50. 50-25-25 is actually lower than 80-30-50. So it's OK.

2. 70-90-90 changes to 70-35-35 while 120-30-50 becomes 80-30-50 . 70-35-35 is actually lower than 80-30-50. So it's OK.

3. 130-80-30 changes to 130-65-30 and then to 95-65-30 while 100-80-50 becomes 100-50-50. 95-65-30 is actually lower 100-50-50. So it's OK.

4. Let's finish with 80-80-80 which changes to 80-40-40 while 100-80-50 becomes 100-50-50. 80-40-40 is actually lower 100-50-50. So it is also OK with that data.

Thus within a few lines, a theory and method have been proven to be in agreement with the guide. Be careful though, it is possible to make other theories compatible with the figures from the guide! I have explained it just to show that 100-50-50 is consistent with the guide.

Well, I hope you liked it. If you want clarification, explanations, etc. on hockey or football, please leave a comment or request a new topic.