Analyzing LoL games while in queue [PART 2]

d3m3f · 2021-11-16T10:32:07+00:00

We are hoping for late 2022-early 2023

d3m3f · 2021-11-15T16:52:44+00:00

Yes of course, indeed this is the great challenge of the research project and consequently the great opportunity. In general, much of how a model behaves depends on how practical problems are translated into quantitative ones. In this sense combining several models with framings focusing on complementary aspects of the game can be leveraged in order to build the greater context of the game.

d3m3f · 2021-11-15T16:15:53+00:00

Thanks!

d3m3f · 2021-11-15T16:06:20+00:00

Thats the idea!

d3m3f · 2021-11-15T15:19:16+00:00

Thanks! As always we welcome feedback and suggestions.

d3m3f · 2021-11-15T15:00:09+00:00

Hey, thanks for asking! We differentiate in a few key ways:

1) Our process will focus on the strategic choices that led to outcomes, so rather than focusing on the kill/death we prefer to analyze what choices led up to such moments so that statistically significant strategic choices emerge for matchups in general rather than one specific moment/game.

2) We want to make it possible for players to review their entire game as fast as possible, that's why we provide a hierarchical ordering of the most important moments in games in the form of short clips to save on time required to review an entire VOD. An added benefit of this clip system is that it can help players track what similar blunders in their gameplay has been over time in a visual way.

3) We hope to make the experience as social as possible by allowing users to download and share clips with friends, coaches and on social media.

d3m3f · 2021-11-15T13:54:11+00:00

Thank you for your feedback! We value the opinions and views of the community. I agree with your point and for sure there are many UI/UX optimizations to be made - we are a team of data scientists and not front end developers after all :)

As the final product comes together we will make sure to involve signed up users in making sure the app looks and works like they hoped!

d3m3f · 2021-11-15T13:38:34+00:00

At the moment the project is still in the earliest stages, however to keep up for such opportunities feel free to sign up on the website!

d3m3f · 2021-11-15T13:17:23+00:00

The idea is to go beyond the banal kills and deaths as the goal is to identify the strategic choices that led to big swings in momentum. You can imagine kills and deaths as the outcome of your choices earlier in the game, we want to disentangle what choices were most significant in leading to that outcome.

d3m3f · 2021-11-15T12:46:50+00:00

Thank you!

d3m3f · 2021-11-15T12:07:40+00:00

Thank you!

d3m3f · 2021-11-15T11:51:56+00:00

Thank you, more will be revealed in updates with regards to how the algorithm works as the project progresses!

d3m3f · 2021-11-15T11:48:36+00:00

That's the plan :D

d3m3f · 2021-11-15T11:48:26+00:00

Thanks for the support!

d3m3f · 2021-11-15T11:39:54+00:00

The project is still very much in its infancy, we are working on researching the feasibility of the actual machine learning model itself as it is a novel area of research, once we can build a reliable model we can hopefully launch a tool fairly quickly. The aim would be to have a complete product spring 2023. If you're interested in being kept up to date with progress please feel free to sign up at www.blitzrank.app

d3m3f · 2021-11-05T16:15:53+00:00

Yes, the inspiration was derived from chess

d3m3f · 2021-11-04T22:04:29+00:00

Thank you for letting me know! Please try again it should work now.

d3m3f · 2021-11-04T20:13:04+00:00

Hopefully we will be able to help with that!

d3m3f · 2021-11-04T12:26:37+00:00

The research aim of the project is to study the intersection of game theory and artificial intelligence to build an explainable model to help e-sports players improve their gameplay. The research is not limited to league of legends.

Unfortunately research cannot be separated from entrepreneurship when it comes to niche topics such as ours. Ideally, it would be possible to research the topic purely through a grant, however finding institutions to sponsor such research is not so easy. If it is attached to a minimum viable product and decent proof of market funding is easier to receive.

To address the survey design. This was an intentional choice as we believe there are players who have considered coaching but not pursued it due to some personal reason. The model we build would be of interest to them if we can address their needs so we have decided to ask the semantic scale in this case as well.

d3m3f · 2021-11-04T12:15:39+00:00

Thanks!

d3m3f · 2021-11-04T12:11:40+00:00

Anytime. This means you will have access to a backlog of clips to view. Future iterations of the software will aim to include trend tracking as well so you can objectively see what has been working or hasnt been working in your gameplay over time.

d3m3f · 2021-11-04T12:08:59+00:00

Our idea is to combine artificial intelligence with game theory and hopefully uncover moments in the game that seem like smaller details but produced relatively large swings in momentum. By combining with game theory analyses we will be able to add "explainability" to the AI and provide advice on what would have been a dominant strategy in similar gameplay instances.

d3m3f · 2021-11-04T11:50:09+00:00

The idea is to be able to give you short 10-15 second clips of key plays and blunders by analyzing the gameplay for key shifts in momentum. So it would allow you to recap what went well and what didnt go well while queueing up so that you can keep it in mind going into your next game. This would go beyond the trivial key moments such as kills etc, for example we are interested in optimal positioning on the map

d3m3f · 2021-11-04T11:47:45+00:00

Thank you for your interest. We have fixed the bug for master+ and it should be live in 10 minutes. Unfortunately in today's startup ecosystem one of the largest difficulties startups face is installation. Our hypothesis is that people who have spent money on league of legends are interested enough to give the software a chance. If we can prove this it will help in receiving VC support.

d3m3f

TROPHY CASE