This model shows that each element has a deterministic win and loss among all the decks.
We can have a tournament in this model and it would be very similar to the first tournament described earlier.
I shall add another layer of complexity to the model. Because there are 10 elements in the model, we need to add a relation between each element. For example:
- 1 beats 2, 4, 6, 8 and loses to 3, 5, 7, 9, 10
- 2 beats 3, 4, 5, 6 and loses to 1, 7, 8, 9, 10
Again, when a number plays against itself it’s a draw.
At this point we can take a look at a few aspects. First, how many decks does each element/deck win against and lose to? If there is one deck that wins against 9 decks, it’s clearly going to be the strongest. This is what we would now call match-ups, so how each deck fares against another deck. In the cardgame jargon, we’d talk about how many good and bad match-ups a deck has, so against how many decks it wins and to how many it loses.
Let’s assume that the match-up distribution is uneven and some decks have 3 winning match-ups, some 5, some 7 etc.
We hold a tournament and we get results. Now the metagaming process gets substantially more creative and interesting. Let’s say deck 4 won the whole thing and it wins and loses to exactly 5/5 decks. What will you bring to the next tournament?
Option 0 – bring 4
Option 1 – you can bring a deck that beats 4
Option 2 – you can bring a deck that beats the deck that beats 4, so a deck that beats Option 1
Option 3 – you can bring a deck that beats the deck that beats the deck that beats 4, so a deck that beats the deck that beats Option 1, so a deck that beats Option 2
Option 4 – you can bring a deck that beats….
And this could go on. Usually, we’d stop at level 3 and not go deeper. Bear in mind, that Option 2 might not beat 4 but beat the deck that beats 4. The relation is not transitive and the match-ups might not line-up the way you’d think they would.
However, that’s not all. So far, we’ve talked about matches being deterministic and having clear win-lose. If games were like this, there would be no point in us even showing up to play, we’d already know who’s going to win. Where’s the fun in that?
So now we changed the deterministic relations to probabilistic relations.
- 1 beats 2 - 55% of the time, 4 - 53%, 6 – 60%, 8 – 57% and loses to 3 – 55%, 5 – 59%, 7 – 67%, 9 – 51%, 10 – 53,5%
The whole model has got its match-ups defined probabilistically. Now if we use the option system described above all the ‘beats’ will change to ‘beats n% of the time’ which makes it much more difficult to know the actual final outcome.
To make it even more annoying, the percentages can be skewed by players’ preparation or technical level of play. An excellent player who in theory has 40% to win can demolish a bad player who would be 60% to win. Naturally, this model would reward skill and plenty of other factors, not only the deck choice.
But but but it’s not all. Decks do not have one specific build card-for-card. If 1 loses to 3 55%, the 1 player might want to change their deck a bit to win more often. But it comes at a cost, as they might incidentally make other match-ups worse by making this one match-up better.