HCT Fall Americas Playoffs (Part 1 of 3)

Hi

Welcome to my first post of analysis. It will be about the 2018 HCT Fall Americas Playoffs. I intend to write a few posts about this event as it is too much to cover everything in just one post.

The analysis will mainly be about the ban decisions made by players. I will explain a little about the event in case you are not clear about the event. In this first part, I will give an introduction of the event and how I got my data to do my analysis.

The Event

The 2018 HCT Fall Americas Playoffs is basically a tournament for Hearthstone player to compete for a chance to earn a place in the Hearthstone Fall Championship. It is part of a series of tournaments organised by Blizzard to allow pro-players to compete. A similar event is also organised for the region of Europe, China and Asia-Pacific. 4 players from each regions then compete in the Fall Championship.

The overall tournament format goes as follows:

There were a total of 76 participants. As you can see only 4 players (5% of the total participants) will eventually be able to participate in the Fall Championship from this playoffs. This is definitely a tough process. 😱 Congratulations to justsaiyan, lnguagehackr, Tincho and bloodyface for emerging from this playoffs to participate in the Fall Championship.

The matches are played using the Hearthstone competitive format “Conquest” – Best-of-5. Below is a simple illustration of the format:

Do watch this video illustration of the Hearthstone “Conquest” format.

Focus of Analysis

From the illustration above, you might be able to tell that there are certain decisions that players have to make during each match. Each players have to choose which deck they want to ban from the opponent lineup and also which deck to use in each round. I will focus on the ban decisions. I will discuss whether their decisions have been the most optimal ban given the situation they are in.

But before I dive in to the analysis, I will have to first introduce the tedious and hard work of the analysis – getting the data.

The Data

The data that are required for this analysis will be the following:

1. Each player’s 4-decks line-up
2. Each player’s ban decision in each match
3. The relative win-rate of all the decks used in the tournament

For 1. I got my data from Hearthstone Top Decks. For 2. I got them from Battlefy. Lastly, I got the relative win-rate of decks from HSReplay.  These websites are great resources to get data about almost everything about Hearthstone.

A screenshot from Hearthstone Top Decks. You can see that Activelee has brought along Token Druid, Odd Rogue, Secret Hunter and Zoo Warlock to this tournament.

From the above image from Battlefy, you can see that justsaiyan’s Druid deck was banned and Purple’s Warrior deck was banned. This means that justsaiyan has chosen to ban Purple’s Warrior deck while Purple has chosen to ban justsaiyan’s Druid deck.

The way to interpret this screenshot from HSReplay is: Zoo Warlock has a win-rate of 42.87% against Odd Rogue and 56.04% against Tempo Mage. An easy way to interpret is that if you are playing a Zoo Warlock deck against an Odd Rogue deck, you have a 42.87% chance of winning, meaning an unfavourable match for you.

I spent a while to collect this data manually from these websites, unfortunately. If anyone out there knows of faster and easier way to collect such data, do let me know about it 😁. I have all these data stored in a .csv format.

The Pre-processing

The next step of the analysis was to do some calculation of win-rate of line-ups. As shown above, I have a win-rate table of deck vs deck. Now, I want to calculate the win-rate of a line-up vs another line-up. This will involve some calculation of probability. The calculation is pretty tedious to do my pen and paper, so I have it all done using Python. Please read this Reddit post on how to calculate the probability of line-up. It gave me a good direction on how to do my calculation. Briefly, I did my calculation using a bottom-up approach. For e.g. I want to know the win-rate of a line-up consisting {} vs a line-up consisting {}. I first calculate the win-rate of a line-up consisting of 2 decks vs 1 deck, then extended it to 3 decks vs 1 deck, 2 decks vs 2 decks, 3 decks vs 2 decks and then finally 3 decks vs 3 decks. There was no need to calculate a 4v3 and 4v4 win-rate as players are going to have one of their decks banned.

Here is what a 3 decks vs 3 decks win-rate table looks like:

What this table tells me is that, in a match where Player 1 used {Taunt Druid, Deathrattle Hunter, Odd Warrior} vs Player 2 used {Malygos Druid, Control Priest, Odd Rogue}, if the first match is Taunt Druid vs Malygos Druid, Player 1 will win the entire 3v3 match with a probability of 62.05%, which is pretty high.

After getting all the necessary 3v3 tables, I extended them to 4v4 tables. A possible scenario that makes use of the above 3v3 table is a match between {Taunt Druid, Deathrattle Hunter, Even Warlock, Odd Warrior} and {Malygos Druid, Control Priest, Odd Rogue, Even Warlock}. Here is what a 4v4 table will look like:

Here is how to interpret the table: for e.g. if Player 1 bans Malygos Druid from Player 2 line-up and Player 2 bans Deathrattle Hunter from Player 1 line-up, Player 1 will be the winner of the match with a probability of 57.40%. Essentially, Player 1 uses {Taunt Druid, Deathrattle Hunter, Even Warlock, Odd Warrior}, while Player 2 uses {Malygos Druid, Control Priest, Odd Rogue, Even Warlock}. Try not to get confused 😅.

As you can see from this table, the ban decision affected your win-rate as a whole in a significant manner. From the table above, the lowest win-rate is 49.69% while the highest win-rate is 61.72%. The ban decision determines whether the match will be unfavourable or favourable to a certain extent.

The Question

So the question is: What is the optimal ban decision for both players? And did each player actually ban optimally? That will answered in the following blog post.

If there are any questions that you have, do contact me so that I can clarify for you.

Cheers