Creating a Solo-Laner Index: Higher-Order Statistics & League of Legends Esports

Kevin Haube
Feb 18 · 6 min read
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Image for post
Berlin, Germany — January 24: — — during the 2019 League of Legends European Championship Series Week 1 at the LEC Studio on January 24, 2020 in Berlin Germany (Photo by Michal Konkol/Riot Games)

In the world of League of Legends esports, there’s no hotter debate than that of “who is the better mid-laner”. In an effort to silence the multitude of biased, opinion-driven drivel I heard on a weekly basis during my tenure as a photographer for Team Liquid and the LCS, I dove into data provided by Tim Sevenhuysen over at EsportsOne and (This link open in new tab) (This link open in new tab) Oracle’s Elixir to find out exactly what qualifies a successful solo-laner (yes, both mid and top), and how could we quantify it in an effective and easily digestible way.

What is a solo-laner?

Summoner’s Rift
Summoner’s Rift

Though the top and bottom lanes are equal in length, and mirror images of one another, the top is a solo lane while the bottom is a lane that is occupied by a primary damage dealer, the ADC, and a Support player who plays somewhat of a healing or peeling role. We will only be looking into Top- and Mid-laners during this exploration as the variables are much more explainable through individual stats.

What does a good solo-lane performance look like?

At the end of the Early Game or Laning Phase, laners often switch lanes, or form 1–3–1 or 1–4 compositions, or group as 5. This phase ends right around 12–15 minutes. This is around the time you start to see the fruits of your early game labors in the form of snowballing, whether that’s through exponential increases in kills, objectives taken, or both, resulting in growing gold leads. This can skew endgame statistics, so Riot has blessed us statisticians and data scientists with some key metrics tracked at 10 and 15 minutes. These are the versions of these metrics we will be utilizing for this Solo Laner analysis. This will give us a clearer image of how they perform on their own, rather than as a part of the team.


Calculations!

That brings us to our next key metric, gold earned outside of CS. Since we already have the amount earned through CS at 10, we can subtract it from our Gold at 10 stat to get this number. We average this amount from all of their games, and through custom (i.e. proprietary) methods of weighting derived from weeks of analysis, we are able to accurately provide our second statistic, what I’ve named the Gold Difference Rating, not to be confused with the player’s Gold Difference at 10/15 minute stat that is tracked and given via the Riot API. Instead, this is an example of a statistic we can use to assume a player’s aggression, team play, and more when compared with other statistics like Kills Per Minute, Baron Kill Rate, etc. This is a major step in a positive direction when it comes to accounting for the “human factors” in professional play!

Results

As far as the Top 10 Top- and Mid-laners:

Top

  1. Doran (LCK) — 76.5
  2. Orome (LEC) — 73.0
  3. Ruin (LCS) — 73.0
  4. Kiin (LCK) — 72.5
  5. Alphari (LEC) — 71.2
  6. Impact (LCS) — 69.1
  7. Rascal (LCK) — 68.5
  8. Bwipo (LEC) — 65.3
  9. Hauntzer (LCS) — 64.0

Middle

  1. Nemesis (LEC) — 81.4
  2. Febiven (LEC) — 80.0
  3. Jenax (LEC) — 79.2
  4. ShowMaker (LCK) — 79.2
  5. Chovy (LCK) — 79.0
  6. Kuro (LCK) — 79.0
  7. PowerOfEvil (LCS) — 78.3
  8. Bdd (LCK) — 78.0
  9. Fly (LCK) — 77.5

There’s a notable gap in scores between Top and Mid, which may be a result of the position of the lane itself. As mentioned in the introduction, the middle lane slices the map in half, therefor the Jungler has to cross the lane multiple times while pathing, opening the lane up to a higher gank probability. In the current meta, as of writing this (patch 10.3), Junglers begin ganking as soon as Level 2 or 3. A large contributing factor to the success of all Solo-laners was their ability to earn gold outside of CS-ing, but there is a notable difference in the CS-ing proficiency of the Top-laners, which may help support my Jungle gank theory when paired with Kills/Assists data. When a gank is successful, it opens the lane up to uncontested wave clearing; something Top-laners may not have as often, putting them at a more average CS proficiency rating.


Conclusion

The next exploration is a secret, for now, but rest assured there’s more coming! I’ll have to set up some type of email subscription system to notify you all. I don’t have any social media plugs, but feel free to contact me via Discord (KMH#6227) if you ever want to chat about LoL esports, statistics, or data science!

Visit my website for more written data science and esports content, https://kevinmhaube.com/

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Kevin Haube

Written by

Software Engineer, Data Scientist, Statistician, and Photographer — Life’s too short to do the same thing every day.

The Startup

Medium's largest active publication, followed by +709K people. Follow to join our community.

Kevin Haube

Written by

Software Engineer, Data Scientist, Statistician, and Photographer — Life’s too short to do the same thing every day.

The Startup

Medium's largest active publication, followed by +709K people. Follow to join our community.

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