The NA server starts 150K+ new games per hour during perk periods almost every day

How popular is League of Legends?

Below is a graph for NA server activity in terms of (estimated) number of games per hour on April 14th, 2015.

As you can see, during 2AM to 8AM (keep in mind this is west coast time - PDT), there are less than 50k games starting per hour. In fact, according to my rough estimate, during these non-peak hours there are still around 500 games starting every minute on the NA server - which is very impressive considering a game of League of Legends will usually last more than 20-30 minutes and usually involves 10 players.

On the other hand, during peak periods of the day there can easily be more than 150K new games starting in an hour. This usually happens around 7PM to 10PM. For example, on April 14th (which is a Tuesday, in case you are interested), about 270K games started on the NA server on 8PM to 9PM. That's a lot of games!

There does seem to be some variations on server activity. Here's a similar but bigger plot for most of April:

It seems that there are some day-to-day variations. Since Patch 5.7 was launched on April 8th, it is not too surprising that we see some spikes of activities right after the new patch. It would also be interesting to know whether or not the server activity correlates to LCS/LCK/LPL in any shape or form. If you are interested to analyze this, I have provided the raw data here.


A note on how number of games per hour is estimated: each game on the server has a unique Match ID associated to it which is assumed to autoincrement with time. Therefore, if I can sample, say, 10000 games that started in an hour and find the smallest and the largest Match ID in this sample, then I can estimate the total number of games that started during this hour.

This blog, goldper10, reddit bot, and the future

Dear reader,

Since earlier this year, I have also started writing for goldper10.com. Here are the articles I've posted for goldper10 so far:

Nemesis Draft - Win Rates and Analysis

How Win Rate Lies To You

Analysis on Teleport and Smite Top Lane in Solo Queue

I have also made a reddit bot which garnered some attention. This bot reads in historical record of a player and suggests some other champions the player should play - using association rules mining. I've had some great fun making it and I definitely plan to make other kinds of bots in the future that also uses neat machine learning / data mining techniques on League of Legends data. Additionally, I want to thank everyone who provided me with great feedback (especially on the ARAM issue) and I am very happy that many people enjoyed using the bot.

I want to thank all my supporters so far (especially /u/x-o). It has been a bumpy road.

As far as the future is concerned, I will keep writing for goldper10.com as well as producing more contents right here - ideally on a weekly basis. I also want this blog to consolidate all of my contents such that everything is self-contained right here.

Thank you again,

Sufficiency

How I mine and analyze Riot's data

I seem to get this question a lot, so I figure I should write a short piece about this.

Disclaimer: I am not a computer scientist, software developer, or a DBA. I just try to make things work with my hacky ways.

First of all, the Riot API provides a way to download game data from Riot's server. You need to read their documentations to figure out what kind of data are available, but the short answer is that there are a LOT of data available and it does take some bandwidth if you mine continuously just below Riot's threshold.

All of Riot's data are in the JSON format. There are a lot of tools which will allow you to parse JSON, but I am currently using a Java package called ulti. With this and my Java code, I request data from Riot's servers while staying barely below the threshold they set out.

On a side note, I am sure other programming languages like Python can do the job just as well, if not better. It just happened that I have fairly strong roots in C/C++ and I enjoy coding in C-like languages.

Once the data is collected, I save the data using a PostgreSQL database. If you are also mining data from Riot, I highly recommend that you set up a database for storage as the data can easily get out of hands if they are stored in plain text. Currently, my database is about 400GB in size; if entirely exported to CSV files, I will not be surprised if they will take more than 4TB of storage.

My disk usage from the data I mined since January, 2015

Databases also make retrieval easier, since SQL queries are fairly efficient.

When it comes to actual data analysis, I use a combination of SQL (simple loading and aggregation),  R, and Excel. Loading data and doing simple aggregations is simple; just write some SQL queries.

R, on the other hand, is slower but it is packed with more statistical tools that allows more insightful analysis.

Excel is another tool that I often use for quick-and-dirty plotting. Not every plot I make is sophisticated; sometimes I just want a quick bar chart and Excel does the job really well.

So there you have it. I hope it helps!

Flash on D or F - revisited

Let us revisit this question. Flash on D or on F? It seems that Flash on D is slightly more popular. But perhaps more interestingly, it is possible that players who put Flash on D have different lane preference compared to players who put Flash on F. I will also give my theory on this phenomenon near the end of this blog entry.

I analyzed about 370k ranked solo queue games played on Patch 5.6 and found about 600k unique players. Here's the frequency table for these players:

	Flash on D	Flash on F	Not Identifiable
# of Players	301269	290322	10947
Percentage	50.0%	48.2%	1.8%

In summary, there are slightly more players who use D for Flash than F. Note that the "not identifiable" 1.8% of the players are due to a combination of inconsistent use of D and F for Flash and/or running no Flash at all.

More interestingly, it is possible that players who use D for Flash have slightly different lane of preference compared to players who use F for Flash - although the difference in preference is fairly small. As you can see below, players with Flash on D chooses to play mid lane 19.8% of the time; for players with Flash on F, it's 20.5%.

Player \ Chance to play	BOTTOM	JUNGLE	MIDDLE	TOP
Players with Flash on D	40.2%	20.6%	19.8%	19.5%
Players with Flash on F	40.0%	19.8%	20.5%	19.6%

Again, the difference is small, but a chi-squared test of independence shows that the difference is significant so the choice of D or F for Flash and choice of lane are probably not independent[1]. 

I have a theory for the result above.

First of all, why do some players choose D and some players do F for Flash? Well, when you play your first game on a level 1 account, the game places Heal on D and Ghost on F by default. Flash is not available for a level 1 account; but over time you will gain the option to use Flash and you will be faced with the dilemma of assigning it to D or F.

I think at this point, the decision to use D or F for Flash depends on if the player thinks Flash is similar to Ghost or not. If the player treats Flash like a movement spell similar to Ghost, I think it is natural to keep Flash on F. On the other hand, if the player treats Flash differently from Ghost, it makes more sense to bind it on a different button.

Therefore, the choice of using D or F for Flash may be demonstrating different modes of thinking for players - and perhaps the button choice is a reflection of the player's personality. Because of this, we also see that players who choose D for Flash have slightly different lane choices compared to players who choose F for Flash.

Then again, this is just my theory. It would be cool to be able to do some personality tests on players and see how personality correlates with lane choices though!

Footnote

[1] Feel free to rip me apart on how I inappropriately used chi-squared test here.

URF Win Rate 2015

95k games collected via Riot API.
Sorted by win rate highest to lowest.
For win rates, mirror match-ups are filtered out.

Champion	Popularity	Win Rate
Sona	27.04%	69.03%
Galio	5.27%	64.13%
Wukong	14.29%	58.76%
Malzahar	12.14%	56.79%
Skarner	2.92%	56.79%
Jax	6.67%	56.69%
Maokai	12.43%	55.58%
Diana	6.31%	55.36%
Evelynn	11.62%	55.23%
Karthus	11.36%	55.16%
Fiora	6.47%	55.02%
Hecarim	23.67%	54.97%
Zed	32.42%	54.97%
Orianna	4.67%	54.72%
Xin Zhao	9.03%	54.72%
Poppy	5.26%	54.49%
Vladimir	10.77%	54.12%
Shaco	27.51%	54.09%
Ahri	14.07%	53.65%
Zyra	4.17%	53.41%
Sivir	5.64%	53.32%
Master Yi	17.00%	53.29%
Soraka	8.08%	53.24%
Ezreal	39.49%	53.13%
Nami	7.57%	53.06%
Morgana	13.67%	53.03%
Jayce	11.85%	52.95%
Nocturne	1.65%	52.80%
Karma	8.43%	52.66%
Fizz	15.71%	52.63%
Tryndamere	5.72%	52.62%
Shen	6.40%	52.55%
Lulu	5.77%	52.48%
Kog'Maw	5.76%	52.29%
Urgot	7.59%	51.96%
Kennen	3.87%	51.85%
Syndra	6.49%	51.75%
Annie	9.86%	51.71%
Talon	7.36%	51.70%
Malphite	10.73%	51.56%
Kayle	5.05%	51.47%
Cho'Gath	8.81%	51.47%
Sejuani	2.85%	51.41%
Janna	2.99%	51.18%
Rumble	3.13%	51.07%
Twisted Fate	7.28%	50.83%
Yorick	3.85%	50.83%
Blitzcrank	23.23%	50.72%
Amumu	7.76%	50.60%
Irelia	2.82%	50.40%
Swain	6.20%	50.37%
Mordekaiser	4.80%	50.12%
Graves	4.10%	50.08%
Dr. Mundo	5.03%	50.02%
Nautilus	5.36%	50.02%
Gangplank	11.51%	50.00%
Katarina	17.83%	49.89%
Varus	3.60%	49.81%
Alistar	12.67%	49.80%
Azir	8.99%	49.65%
Darius	5.21%	49.65%
Lux	20.67%	49.40%
Nasus	13.10%	49.22%
Jarvan IV	3.04%	49.08%
Nidalee	30.39%	48.98%
Quinn	1.70%	48.60%
Warwick	4.10%	48.45%
Veigar	8.41%	48.36%
Heimerdinger	5.26%	48.29%
Zac	1.53%	48.15%
Taric	3.03%	48.07%
Draven	1.87%	48.05%
Teemo	19.35%	48.01%
Riven	14.79%	47.93%
Rammus	2.59%	47.82%
Ziggs	10.12%	47.76%
Ashe	10.57%	47.31%
Ryze	10.71%	46.44%
Volibear	1.92%	46.38%
Vi	6.12%	46.35%
Aatrox	1.53%	46.31%
Vel'Koz	8.72%	46.31%
Leona	8.39%	45.95%
Udyr	5.59%	45.77%
Rengar	5.01%	45.66%
Kha'Zix	4.40%	45.60%
Renekton	1.68%	45.45%
Shyvana	0.96%	45.42%
Lucian	13.78%	45.29%
Trundle	1.21%	45.19%
Pantheon	6.21%	44.96%
Lissandra	3.98%	44.95%
Miss Fortune	2.50%	44.93%
Fiddlesticks	6.01%	44.92%
Xerath	6.07%	44.65%
Braum	1.55%	44.59%
Caitlyn	3.22%	44.54%
Sion	4.49%	44.52%
Jinx	15.27%	44.35%
Brand	6.16%	44.17%
Singed	2.96%	44.07%
Viktor	3.91%	44.05%
Gragas	3.17%	43.35%
Olaf	1.87%	43.27%
Twitch	2.27%	43.21%
Tristana	3.26%	42.81%
Vayne	5.22%	42.78%
Kassadin	12.51%	42.61%
LeBlanc	20.49%	42.41%
Garen	3.64%	42.01%
Nunu	4.16%	41.96%
Akali	6.91%	41.71%
Rek'Sai	3.29%	41.59%
Lee Sin	7.02%	41.52%
Anivia	3.36%	41.43%
Corki	3.24%	40.90%
Elise	0.97%	39.96%
Yasuo	6.30%	39.44%
Cassiopeia	3.30%	38.82%
Zilean	4.50%	38.66%
Gnar	2.51%	38.66%
Kalista	2.08%	36.82%
Thresh	5.44%	34.32%
Bard	11.81%	30.75%

Blue side win rate: 52%

I think Riot deserves some praise here. They spent a lot of time tuning many champions individually so they do not need to be disabled. In URF 2014, 9 champions were either initially or later disabled due to their gross power on URF ( Kassadin, Ryze, Sona, Hecarim, Kayle, Soraka, Nidalee, and Alistar). This year, no champions were disabled (yet) after global healing/shield adjustments and some individual adjustments.

The URF mode win rate from last year can be found here.

Legend of the Poro King Mode Statistics!

75k games collected.

Mirror matchups are filtered out for the computation of champion win rate (not for popularity).

Champion	Win %	Popularity
Sona	65.57%	22.45%
Heimerdinger	61.14%	11.18%
Janna	60.49%	7.49%
Kog'Maw	60.43%	5.99%
Sion	60.40%	7.68%
Swain	58.74%	4.16%
Galio	58.61%	9.20%
Maokai	58.12%	7.54%
Ziggs	57.64%	28.80%
Zyra	57.12%	4.49%
Vladimir	57.02%	4.25%
Karthus	56.95%	7.14%
Rammus	56.91%	3.17%
Wukong	56.27%	13.88%
Leona	55.96%	6.08%
Caitlyn	55.92%	12.59%
Alistar	55.90%	13.67%
Singed	55.80%	8.22%
Lux	55.75%	25.45%
Taric	55.61%	1.84%
Varus	55.58%	9.45%
Fiddlesticks	55.28%	16.28%
Talon	54.73%	7.98%
Sivir	54.53%	14.19%
Amumu	53.87%	10.60%
Garen	53.65%	10.65%
Malzahar	53.45%	13.51%
Vel'Koz	53.30%	10.50%
Miss Fortune	53.23%	9.77%
Teemo	53.21%	15.51%
Master Yi	53.13%	13.99%
Jinx	53.09%	17.70%
Katarina	52.84%	11.63%
Soraka	51.94%	8.37%
Fiora	51.91%	8.23%
Ashe	51.91%	14.17%
Poppy	51.74%	3.34%
Yorick	51.73%	1.28%
Draven	51.36%	3.47%
Trundle	51.28%	1.28%
Gangplank	51.27%	9.00%
Brand	51.24%	6.71%
Mordekaiser	50.95%	2.26%
Sejuani	50.94%	13.93%
Graves	50.43%	3.65%
Morgana	50.34%	14.96%
Jarvan IV	50.28%	5.65%
Jayce	50.26%	14.81%
Nautilus	50.24%	3.82%
Anivia	50.14%	3.02%
Dr. Mundo	50.00%	4.40%
Annie	49.47%	7.53%
Darius	49.43%	9.43%
Warwick	49.40%	2.65%
Diana	49.28%	2.87%
Nami	49.21%	4.44%
Blitzcrank	49.20%	18.67%
Viktor	49.16%	3.34%
Ezreal	49.15%	27.94%
Nasus	48.89%	2.89%
Xin Zhao	48.76%	4.63%
Karma	48.59%	5.53%
Twisted Fate	48.33%	6.61%
Olaf	48.19%	1.97%
Pantheon	48.15%	7.16%
Irelia	48.07%	2.78%
Gnar	47.79%	10.32%
Malphite	47.46%	10.22%
Volibear	47.36%	4.28%
Shen	47.35%	2.70%
Azir	47.18%	10.35%
Ryze	47.11%	2.86%
Xerath	47.11%	21.07%
Renekton	47.03%	2.10%
Cho'Gath	46.97%	8.27%
Cassiopeia	46.95%	2.77%
Ahri	46.93%	8.53%
Jax	46.87%	5.26%
Hecarim	46.76%	2.34%
Fizz	46.53%	11.60%
Corki	46.20%	4.11%
Quinn	45.92%	2.02%
RekSai	45.76%	13.38%
Rumble	45.76%	3.46%
Yasuo	45.54%	17.67%
Braum	45.52%	6.19%
Shyvana	44.98%	2.36%
Vi	44.74%	7.41%
Lissandra	44.71%	5.88%
Vayne	44.52%	5.95%
Veigar	44.52%	10.81%
Skarner	44.42%	3.92%
Nidalee	44.42%	24.56%
Orianna	44.39%	23.58%
Lucian	44.35%	15.40%
Kayle	44.18%	3.24%
Twitch	44.14%	3.69%
Shaco	43.97%	6.61%
Riven	43.76%	9.55%
Nunu	43.41%	4.27%
Kennen	43.25%	4.33%
Urgot	43.23%	0.94%
Nocturne	43.21%	0.76%
Zed	42.86%	14.02%
Lulu	42.71%	11.87%
Tryndamere	42.63%	3.47%
Gragas	42.50%	4.47%
Udyr	42.25%	1.66%
Akali	42.11%	7.43%
Rengar	41.85%	2.43%
Tristana	41.74%	7.59%
Kassadin	41.67%	2.60%
Kalista	40.83%	12.42%
Thresh	40.69%	6.53%
Syndra	40.62%	4.22%
Zac	40.45%	6.82%
Zilean	40.20%	8.58%
Aatrox	39.86%	1.40%
Lee Sin	39.72%	10.08%
Kha'Zix	39.27%	2.87%
Elise	38.97%	0.61%
LeBlanc	36.72%	9.80%
Evelynn	34.34%	0.52%

Side	Win Rate
Blue Side	58.47%
Red Side	41.53%

I wish I made up the 58.47% win rate on blue side. Unfortunately, it's real.