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LoL: How to win your Solo Queue Draft - a statistical analysis of 1M+ games

Games are both won and lost in Champ Select, here’s a statistical overview of the biggest contributors to that result

Published on: 8th September, 2022

So, we finished off our series on drafting in professional games (you can find them all here if you’re late to the party). The question remains: can we translate any of our lessons to solo queue?

If you follow me elsewhere, you know I actually answered this question a while ago and used it to launch our first product: The iTero drafting coach.

This article is a quantifying those results, visualising what we found from studying 1M+ solo queue games in order to train the AI.

“Master Yourself, Master the Enemy”

It should come at no great surprise to anyone that by far the most impactful of all is the level of Mastery you have on a Champion.

lol-mastery-vs-win-rate

How Champion Mastery Impacts Win Rate

It’s not even that you need hundreds of games before a Champion becomes an acceptable pick — it’s just that so many players insist in mid-elo insist on first timing in ranked! The drop off is considerable, with sub-5k Mastery players averaging a win rate of just 41.5%, compared to the 54.5% enjoyed by 500k+ players.

The increase is fast, but diminishing returns hits fast. The gain from 75k to 175k is not even a single percentage point, whilst going from 2.5k to 7.5k is almost 8%.

There is a simple lesson here: don’t first pick in Ranked.

Champion Win Rate

There’s not so much point graphing the correlation between a Champion’s win rate and how many games that player ends up winning — since they are practically the same thing (apart from some sampling randomness). So, instead — let’s look at the sort of values we tend to see in our ranked games.

lol-champion-win-rate-distribution

The Distribution of Champion Win Rates

A grand majority of Champions sit between the 46–54% range, with few outliers. Of course, all else being equal it’s better to sit at the top of this scale where you can, but there’s not a huge range.

Counters & Pairs

If you play ranked, the chances are that you will occasionally look at the top lane counters to face up against your opponent. However, how often do we consider any other lane. Sure, your mid laner counters their mid laner, but how does it fair against their jungler? Or the synergy with your support?

The game is only isolated to the 1v1 for a relatively short period of the game when compared to all those skirmishes and team fights that will happen post-laning phase. No doubt, your 1v1 lane match-up will have the most significant impact — but that doesn’t mean you should ignore everything else!

lol-champion-win-rate-counters-per-lane

The impact of each lane win rate counters

This grid shows you the impact each lane counter has, where the darker the colour the more it sways the final outcome. For instance, the mid vs. mid match-up is by far the most important for those playing that lane, whilst for ADC’s is far more diverse with an almost equal impact across the board.

Econ & “Snowballatility”

This one’s not so straight forward, so bare with me. I’ve written an entire article dedicated just to this statistic, if wanted to really get into the details then head over there.

The simple summary is that certain Champions have a better chance of winning a game than others even if their laning phase went the same. If an Ornn goes 0–5 by 12 minutes, his win chances go down BUT not nearly as badly as if it was 0–5 Irelia. We call this a Champion’s econ.

The flipside is what I call “snowballatility”. A 5–0 Irelia can do more with that lead than a 5–0 Ornn, for instance.

To visualise this I’ve selected around 17,000 games where the lanes gone badly. The players all have around 70–75% of the average Gold @ 12 minutes for that lane. I.e. they’re about half an item behind schedule.

I’ve then split these in to two categories depending on whether that Champion has a good or bad econ rating. Here’s some examples of the Champions in each:

BAD Econ: Irelia, Tristana, Renekton, Aatrox, Riven

GOOD Econ: Ornn, Malphite, Galio, Singed, Malzahar

lol-champion-econ-win-rate

How a Champion's Econ Rating Impacts Win Rate's for a Losing Lane

For all players, things are bad — averaging around 27.5% win rate if they’re set this far behind so early in the game. However, the BAD econ Champions have win rates almost 5% lower than the GOOD ones.

Obviously, the question is: “well how do you know how the laning phase will go?”. The technical answer is I have a separate model which first predicts the Gold @ 12 minutes before then going on to predict the final result.

In practice however, it’s very difficult. As a general rule it makes sense to consider this when blind picking or counter picking a Champion.

Know who you’re against and reckon you can face-roll the lane and get ahead? High “snowballatility” champions will maximise your win chance.

Blind picking and worried about getting countered yourself? High econ Champions will be good damage mitigation.

Team Composition

Obviously, you have far less control over the totality of your compositions strengths and weaknesses. However, there’s one that is worth considering: the AD Ratio.

By building a composition that is too one dimensional in their damage type you offer the enemy the opportunity to efficiently buy defensive items. Their tanks can use all 6 slots to build items effective against your entire team, when usually they would be forced to balance the two.

lol-ad-ratio-win-rate

How Damage Type of Team Composition Impacts Win Rate

If AD accounts for around 20–80% of your team’s damage — you’ll be sitting bang average, just above the 50% mark. However, for those few games that fall on either side of this green zone there is a very real decrease in their win chance.

For some reason, <20% AD seems to be more painful than >80%. In other words, if you had to choose between the two you’d choose a full AD team over a full AP team. Potentially as there tends to be a greater selection of AD Champions with high and consistent true damage that can still handle the tanks.

And More…

There is of course many more elements to the draft, each bringing their own nuances that require consideration as you move through the drafting phase.

However, in the 30 seconds we have to make our decision there is simply too much to weigh up. Hence, I’d recommend sticking to these key points based on the findings above if you want to make that final climb:

  • Don’t first time. Above anything, it’s consistently the worst offender in mid-elos. You want a minimum of 5 games on a Champion before you take it to ranked. That’s a minimum.
  • There’s a reason Champions with high win rates have high win rates. This doesn’t supersede the first point. Of your highest played Champions, pick the ones with the highest win rates.
  • Lane Counters are super effective. Just remember non-lane counters are ALSO effective. We keep track of all matchup win rates in Silver/Elo on iTero.gg if you’re unsure.
  • Blind picking? Consider high econ Champions. Got them on the ropes? High snowballatility.
  • Diversify your damage. If you’re Jungling and the mid locks in Zed, consider an AP Champion. Last picking support and your team is 4 AP Champions? Consider Pyke or Pantheon.

Remember, this list is also in order of priority. If you’ve never played an AD support then avoid them, even if it locks you in to 90% AP damage. Quinn is a Garen counter — once you’ve played the matchup a handful of times AND she’s in a strong position in the meta.

If you aren’t an android from the future, chances are it’ll be extremely to consider all this in the moment. It’s why we made our drafting tool, afterall:

The iTero AI Drafting Tool

Simply type in your Summoner name, Region and the draft so far (you can even leave it empty if you want to see your best first picks) then hit “GET RECOMMENDATIONS”. The AI will calculate all the above and more, to give you recommendations based on your account. From our initial tests we’ve found that players using our recommendations consistently win more game— it’s as simple as that.


Thanks for Reading!

You got to the end of the article! My name is Jack J and I’m a professional Data Scientist applying AI to competitive gaming & esports. I’m the founder of AI & Esports start-up iTero.GG and the analytics site jung.gg. You can follow me on Twitter, join the iTero Discord or drop me an e-mail at jack@itero.gg. See you at the next one.

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