1.2 Modelling Competitor + Strategy Probabilities in Trading Card Games

How does the conditional probability of one player and deck change when facing another unique player/deck combination?

In the previous post, we discussed our decision to use Bayesian Inference as the preferred method to compute the win probabilities for different players and decks.

We will now expand on how Bayes’ Theorem can help us do this.

We’re going to take for granted that the probabilities here are examples only and that, given this demonstration, we’re also taking for granted that someway, somehow, all the probabilities have been provided to us.

In later parts of this project, we’ll tackle actually getting to the point where we can create such matchups with our own computed proabilities.

The Competitor + Strategy Matrix

Assume we have 8 Competitors (C1…C8) and 8 Strategies (S1…S8).

We might have a table that looks like this:

C1C2C3C4C5C6C7C8
S10.3310.5290.4500.6260.1830.6490.2570.151
S20.5070.2710.5370.2000.2580.5130.1190.330
S30.5120.2420.2870.2530.6130.3930.1790.574
S40.6770.5400.6940.6100.4060.1380.2090.395
S50.6540.2220.4920.6300.5690.1190.1440.397
S60.2970.5440.5340.2620.2430.4320.6960.352
S70.3510.6000.7050.1190.3650.7190.3990.518
S80.4150.3420.4770.1100.2050.4320.4930.134

This table tells us how probable a given Competitor, using a given Strategy is to win, in general. This would be a probability against a disembodied opponent: the probability isn’t conditioned on facing another person or deck. It’s most likely the long term average for the Competitors and their Strategies (wins/losses) expressed as a ratio.

Reading the table, above, we can see that Competitor 2 using Strategy 4 has a .540 probability of winning any game.

But what is this Competitor using this Strategy’s probability of winning against another Competitor and Strategy combination?

Computing Win Probabilities

Last time, we reviewed Bayes’ Theorem in the abstract:

[math] P(A|B)=\frac{P(A)P(B|A)}{P(B)} [/math]

We decided that the win probability for Player A against Player B was a conditional probability, or that this probability was conditioned on the circumstances of these two opponents facing one another.

It wouldn’t make sense for us simply to give a probability of Player A (or Player B) winning in isolation. This is what our table, above, does.

It doesn’t, however, give us a probability of a Competitor + Strategy combination against another Competitor + Strategy combination, which is what we want.

Let’s take the case of Competitor 3 using Strategy 5 (C3+S5) playing against Competitor 7 using Strategy 2 (C7+S2).

How do we calculate the probability that C3+S5 win a match against C7+S2?

We set up our equation thusly:

[math] P(C3+S5|C7+S3)=\frac{P(C3+S5)P(C7+ S3|C3+S5)}{C7+ S5} [/math]

Let’s examine each of the variables in turn.

P(C3+S5 | C7 + S3)

This is the win probability of C3+S5 against C7+S3, which is what we’re looking for.

We won’t know this until the end!

P(C3+S5)

Referring to our table above, we find that the probability of C3+S5 winning any match is 0.492.

Thus, the value of P(C3+S5) is 0.492 in our formula.

P(C7+S3|C3+S5)

This one is a little trickier. This is the probability that C7+S3 win against C3+S5.

And if we think about it, is the opposite of what we’re looking for, P(C3+S5|C7+S3)!

If we knew this now, we’d be done, because to find the other probability, we simply subtract it from 1. This is something we’ll return to shortly.

For now, let’s leave this as a mystery.

P(C7+S3)

This is simply the probability that C7+S3 win any match.

Consulting our table, we see that this is 0.179.

So we’ll use that.

Missing Information

So far, our revised formula looks like this:

[math] P(C3+S5| C7+ S3)= \frac{0.492*P(C7+ S3|C3+S5)}{P(0.179)}[/math]

But what about that pesky P(C7+S3|C3+S5)?

If we could calculate that, we’d know P(C3+S5 | C7+ S3), our desired answer, too.

Let’s find an easier way to make this calculation.

Using Excel and Basic Algebra

Let’s take another look at our Competitor and Strategy table:

We know that C3+S5 has an overall win probability of 0.492 and that C7+S3 has an overall win probability of 0.179.

To make our life easier, we will constrain the probability space to the universe that contains only these two match ups. That is, to say, that 1 (or 100%) will be the combined total probabilities for these two Competitors + Strategies.

We can think of this as a ratio:

[math] Win Probability= \frac{P(C3+C5)}{P(C3+C5) + P(C7+ S3)}[/math]

This pits the probability of C3+C5 against the combined probability of C3+C5 and C7+ S3.

We get the following:

[math]  \frac{0.492}{0.492+0.179}=0.733[/math]

This works because the probability that C3+C5 win any match is a fraction of the combined probabilities that C3+C5 win any match and C7+S3 win any match.

In fact, we can put all of this back into Bayes’ Theorem to prove our case.

Remember, that if the P(C3+C5|P(C7 + S3)) = 0.733, then the probability that the other player win is exactly 1 – this amount, or P(C7= + S3|C3+C5) = 1 – 0.733 = 0.267.

This is because all probabilities must sum to 1 (absolute certainty). All potential outcomes represent the whole of possible outcomes. 

[math]P(C3+S5| C7+ S3)=\frac{0.492*0.267}{0.179} =0.733[/math]

All we have to do is divide Cn+Sn by Cn+Sn combined with Ck+Sk.

This is much easier.

What we’ve done here is normalize the probabilities of the two competitors such that their conditional probabilities sum to 1, like this:

The Expanded Cn+Sn Matrix

To carry this theory into action, we pit each and every combination of Competitors + Strategies (Cn+Sn) against each other in the same way as we did above for C3+C5 against C7+S3.

Our previous win probability table has 8 rows and 8 columns. This one has 64 rows and 64 columns.

Wins? ↓C1+S1C1+S2C1+S3C1+S4C1+S5C1+S6C1+S7C1+S8C2+S1C2+S2C2+S3C2+S4C2+S5C2+S6C2+S7C2+S8C3+S1C3+S2C3+S3C3+S4C3+S5C3+S6C3+S7C3+S8C4+S1C4+S2C4+S3C4+S4C4+S5C4+S6C4+S7C4+S8C5+S1C5+S2C5+S3C5+S4C5+S5C5+S6C5+S7C5+S8C6+S1C6+S2C6+S3C6+S4C6+S5C6+S6C6+S7C6+S8C7+S1C7+S2C7+S3C7+S4C7+S5C7+S6C7+S7C7+S8C8+S1C8+S2C8+S3C8+S4C8+S5C8+S6C8+S7C8+S8
C1+S10.50.3949880670.3926453140.3283730160.3360406090.5270700640.4853372430.4436997320.3848837210.5498338870.5776614310.3800229620.5985533450.3782857140.3555316860.4918276370.4238156210.3813364060.5355987060.3229268290.402187120.382658960.3194980690.4096534650.3458725180.6233521660.5667808220.3517534540.3444328820.5581787520.7355555560.7505668930.6439688720.561969440.3506355930.4491180460.3677777780.5766550520.4755747130.6175373130.3377551020.3921800950.457182320.705756930.7355555560.4338138930.3152380950.4338138930.562925170.7355555560.6490196080.6129629630.6968421050.3222979550.4534246580.4016990290.6867219920.500756430.3657458560.4559228650.454670330.4846266470.3898704360.711827957
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C8+S60.5153733530.4097788130.4074074070.3420796890.3499005960.3499005960.5007112380.45893090.399545970.5650080260.5925925930.3946188340.6132404180.3928571430.3697478990.5072046110.4389027430.3959505060.550860720.3365200760.4170616110.3972911960.3330179750.4246079610.35991820.6376811590.5818181820.3659043660.3584521380.5732899020.7473460720.7619047620.6579439250.577049180.3647668390.4643799470.3821932680.5915966390.4909344490.6319569120.3516483520.4069364160.4724832210.7183673470.7473460720.4489795920.3286647990.4489795920.5779967160.7473460720.6629001880.627450980.7096774190.3358778630.4687083890.4165680470.6998011930.5161290320.380129590.4712182060.4699599470.50.4045977010.724279835
C8+S70.6101295640.5053658540.5029126210.4334728030.4419795220.4419795220.5960874570.5551982850.4947468960.656527250.6815789470.4896030250.70.4877589450.463327370.6023255810.5351239670.4909952610.6434782610.4273927390.5128712870.4923954370.4235486510.5206030150.4527972030.7214484680.6718547340.4592198580.4512195120.6641025640.8131868130.8248407640.7389443650.6675257730.4580017680.5606060610.4765409380.6806833110.5866364670.7164591980.4438731790.502424830.5686059280.7896341460.8131868130.5452631580.4187550530.5452631580.6683870970.8131868130.7431850790.7125171940.7824773410.4266886330.5648854960.5123639960.7742899850.6108490570.4743589740.567360350.5661202190.5954022990.50.794478528
C8+S80.2881720430.2090483620.2074303410.1652281130.1700507610.1700507610.276288660.2440801460.2021116140.3308641980.3563829790.1988130560.3764044940.1976401180.1825613080.2815126050.2294520550.1997019370.3182897860.1618357490.2140575080.2005988020.1597139450.2193126020.1763157890.4011976050.346253230.1801075270.175392670.3383838380.5296442690.5491803280.4227129340.3418367350.1793842030.2481481480.1906116640.3554376660.2685370740.3952802360.1711366540.2071097370.254269450.4926470590.5296442690.2367491170.1570926140.2367491170.3427109970.5296442690.4281150160.3906705540.4820143880.1614457830.2514071290.2137161080.4701754390.2887931030.1892655370.2533081290.2523540490.2757201650.2055214720.5

You’ll notice that starting with Row 1, Column 1 (position [1,1]) and following along a perfect diagonal (positions [2,2], [3,3], [4,4], etc.) all the probabilities are 0.5, or dead even. This is because due to the way the table is set up, each Cn+Sn is pitted against itself exactly once.

There are also 448 other instances of the same Competitor facing itself with a different Strategy.

These 512 matchups notwithstanding, our table does give us the win probabilities for every real combination of Cn+Sn.

This is Bayes’ Theorem at work.

We’re on to a start.

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