Original Post — Direct link

I'm a Data Scientist, League player and writer for TowardsDataScience. I often combine these to research and publish articles about LoL and I thought Reddit might appreciate my latest, which is all about tilt!

The full article & link to the code can be found here:

https://towardsdatascience.com/analyzing-tilt-to-win-more-games-league-of-legends-347de832a5b1

I was interested in seeing whether I could prove the existence of tilt (I went on a 13 game losing streak, this was a coping mechanism). The first step was to see whether players who have lost, are more likely to lose their next game:

Win Rates of players based on the results of their previous games.

But, this is obvious when you think about it. Players that have recently lost 2 games are statistically more likely to be worse players than players who have recently won 2 games! The players might not be “tilted”, they might just be worse than the average.

So instead, I looked at like-for-like players (Gold players on a 2 game losing streak) and compared their win rates based on how long they waited before re-queueing:

The win rate of Gold ranked players on a losing streak, depending on how long they waited before playing the next game.

Those who take no break after losing two consecutive games have by far the lowest win rate! Interestingly, those who only take a short break not only have improved win rates but win more than 3% of their games compared to the average! I suggested this could be because they've "warmed up", but it's a question for future research.

I re-ran the experiment for Diamond I players and the results were interesting..

he win rate of Diamond I ranked players on a losing streak, depending on how long they waited before playing the next game.

High elo players actually see their win rate considerably decrease if they take regular short-breaks following a loss, whilst players who play immediately see a small improvement compared to the average player! My best guess is that players who don't take breaks after a loss simply play more games & have learnt to cope with the tilt (otherwise they wouldn't be high elo, right?), but this is again up for debate

I'd love to hear your opinions and maybe suggestions on future research!

I will also shamelessly promote my LoL analytics website, jung.gg - it's the only site that can provide the most common jungle paths taken by high elo players (currently awaiting a redesign!).

Thanks!

Jack

External link →
over 4 years ago - /u/RiotSouthKorea - Direct link

Hey Jack, this is awesome to see! I love seeing fellow data nerds on here. The analysis that you did was thorough and you were thoughtful of potential biases.

A question I have for my own curiosity: for Gold and Diamond players respectively, what was the size distribution like for each of those "Time before Requeueing" Bars? In other words, what is the % breakdown of how many games are in the "Immediate," "Short break," and "Long break" bars for Gold players and Diamond players?

Again, thanks for providing such an interesting read!

over 4 years ago - /u/RiotSouthKorea - Direct link

Originally posted by Tekparif

hey data nerd, would you pls share some ball park data about the amount of players over d4+ and d3+ ? i seperated d4 because it is elo hell, prob you know so i believe there should be lots of stuck people there. we kinda see data around like %2ish of the players are diamond+ but what it stands for?

thanks

Hey Tekparif - I'm not sure if I fully understood your question (sorry if I misunderstood!), but I think what you're asking is, "how many people are in d4+, d3+, d3+, etc.?" or in other words, what is the distribution of players by rank?

I'm going to cheat a little and use op.gg as they already provide this data in a readily consumable format. Seems like for NA, D4 is about the top 2% of players, and D3 is about the top 1%.

Hope that helps!