Blackjack 21 Methods To Determine Who Wins Champ Select.

Gen G famously determined a successful draft is reaching a certain CC score, calculated by how much lockdown potential each champ had in their kit. Using a card-counting algorithms, we'll do the same.

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Inspiration

I want to put together a project that helps to objectively decide which team wins the draft component of a League of Legends game. I'm a card player at heart, and there's a process of card counting in Blackjack that I think could be applied by applying a metric value to each champion based on their CC (or potentially DPS) ability, and seeing how close each team can get towards the desired score in their drafting strategies. With a draft too one side meaning the composition is lacking lockdown potential and a composition too far on the other side of the spectrum meaning the composition is lacking other aspects that would define a successful draft. I remember IWillDominate mentioning Gen G utilised a CC score for each champ as a strategy in the past, I want to see if it can scale and be measured through data science processes. Because just like a blackjack shoe (deck), there is a finite number of resources available (just like a champion draft) and then as each phase of play resolves and fewer resources are available, it becomes easier to predict which moves to make that will be in your favour.

How does crowd control score work in LoL? The crowd control score (CCS) is a stat that calculates the amount of crowd control a player creates during a game. The crowd control score was introduced in 2017 and remains fairly unknown among players. It’s calculated based on the amount of time a player impaired an enemy throughout a game. It’s worth noting that it varies based on the type of crowd control used. For example, hard crowd control such as stuns weighs higher than slows, blinds, and other milder types of crowd control.

What it does

The methodology applies a numerical value to each champion, with negative values applied to higher priority CC champions and positive values applied to lower CC priority champions.

How we built it

Using AWS Amplify and Lambda for backend data structures, Figma for UI, React to integrate the processes using plain old math for the calculation of Hi-Lo and Omega 2 values. Bayes provided the data to test the methods.

Challenges we ran into

It was the first time using AWS and react. So processes and codes I natively know when to use where, had to be relearnt to make sure it makes sense in the structures provided.

Accomplishments that we're proud of

Getting further along in the development process than I expected.

What we learned

How to apply mathematical equations to other faucets of League E-sports. That a higher CC score doesn't guarantee victory (if it was that clear cut I would question my own equations). And an intro to AWS and the data systems available in League of Legends.

What's next for Blackjack 21 Card Counting To See Who Wins Champ Select.

Continue developing and iterating on algorithms and testing processes to create a broader understanding to Esports in general.

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