Overview

Know thy self, know thy enemy. A thousand battles, a thousand victories.

-- Sun Tzu

I am going to go over the different sections of the site and will give a brief explanation of what they are and what they are trying to perceive.

The Radar Chart

The chart is an overall snapshot of who you are as a League of Legends summoner. With one glance you can see if you are more attack style prone or defense style prone. You can see where your warding skills are at and where your cs levels are at. The list goes on from there. The main point is this: snapshot. This chart is based off all the relevant games in the system. What does this mean? Let's go over the sections of the chart itself.

• Attack: I understand that the attack level of a support game can't be compared to the attack level of the other lanes. Due to this all support games are disregarded when analyzing the attack data of a summoner. (sorry support mains)
• Defense: This is universal across the lanes so I pull defensive stats from all games.
• Warding: Again, the same for all lanes so is pulled for all games.
• CS: Creep Score is naturally significantly lower for supports and junglers. So for the CS analysis all support and jungling games are left out. This is to ensure the clearest picture of CS skill is shown. (sorry support and jungle mains)
• KDA: This is where I do something strange. I get the average amount of kills for all games, excluding support games. I then get the average amount of assists for all games and the average amount of deaths for all games. From there I calculate the average KDA and from there calculate a score for it.
• Gold/Minute: Again, I understand that support players' GPM will not be as high as the other lanes. Due to this I drop out support games for this analysis. (again, support mains...)
• Jungle Creeps: This score analysis is only based off of jungle games in the system. (makes sense right?)
• Jungle Games In Enemy Jungle: Again, only based off of jungle games in the system.
• Damage To Champions: Naturally, for this analysis and score I don't include support games. (support mains.........)

Now with this said I would like to add some clarification on the games in the system. Whenever you run your summoner name through my system I go back and grab your most recent 20 ranked games and granted they meet all the criteria, I log them into my system. So the first time you search your summoner name, I will log the most recent 20 ranked games that meet my criteria. If you go and play 5 games after the fact and search again, then I will log those 5 games as long as they meet my criteria (what is meant by criteria is whether or not Riot gives me all the information I need).

Now with this all said, these levels are compared to the community average. So using the same process as described above for you, I do the same thing but for all games in the system for all summoners in the system. This way I am able to not only portray your current scores and levels, but how you stack up against the community and where you can change things up to improve.

Averages & LOA Score

There isn't a whole bunch that has to be said here. Using the same methodology as the radar chart, I pull the averages for each section in the averages section. This gives a different view from the radar chart (instead of seeing a score based off the averages, you get to see the actual averages).

The LOA score is exactly as it sounds, it is your overall League of Analytix score. Take it as an overall Radar Chart score.

History Charts

These charts show the raw data from all the games in the system. The reason these exists it to show you how consistent you are at these vital 4 game points. The goal is to have a good score in these sections and for the history charts to be as straight as possible.

Player Information

Right now I am just displaying your most played champion in our system, as well as some cool information about that champion. Eventually (release 2.0), this section will display your play style and more information about you as a player.

Thanks for reading!

Justin "TytonDon" Werner