(Edited)

# Presentation

Hello everyone, my name is Felype and currently finishing my computer engineering degree. My area of ​​expertise is in Generative Adversarial Networks(GANs), but I also work with data analysis.

First of all I would like to thank , for making the API available.

Initially I built a dataframe with the current top 100

First we can see that the top 1 and 2 have very high win rates (89%>), amazing!

## Boxplot

After seeing that, I analised the distribution via boxplot of the top 25,

With this we can see that the median is around 73%, with its third quartile around 80% and its first quartile around 65%. We can see that the lowest win rate in the top 25 has around 60% and the highest (top 1) has above 90%!

But do we need to play a lot to reach the top? The answer is... no!

As we can see in the graphic above, the ranking one has about 200 games, while top 19 has more than 900 games!

I analyzed the guilds to top 100, many don't have guild, but I found 3 guilds that have 2 members in the top 100!

The above information give us that The Cobras and \$GAME- CREDITS have, on average, approximately more than 75% win rate!

Next, we will analyze the amount of cards of each player:

Top 1 has less than 100 cards (a nice optimization maybe?)
Top 15 doesnt have cards, maybe he ranked up only renting cards.

Last but not least, I analyzed the win rate of the top 25 players with the 6 elements. More than 600 games were analyzed, to facilitate the analysis, I discarded games that the result was surrender or draw!

The ranking elements by choice is earth with 192 games and fire with 149 games!
Analyzing by the win rate, water and death was not a good choice, while dragon and fire has incredible 80%+ victory!

Well, this is all for now.
If you liked it, please don't forget to comment!
If you see any error in the analysis, please don't forget to let me know, growing and always learning! :)

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Thanks for the analysis, contains some valuable insights! You've been upvoted by solaito.
Your post has been manually curated by @monster-curator Team!

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(Edited)

Thank you for your analysis. One hint: Sometimes you can just look at your subjects rather than make assumptions based on pure data. I.e. if you look at schwarszchild's collection one will pretty fast recognize that he owns only a handful of competitive cards in his collection. So he must have rented a lot of cards for his climb to #1 too. And if you look at his match/rent history you will determine exactly this.

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Hey Rikkon, thanks for your feedback! Next time I'll try to see in action the subjects of my analysis. I believe there really was a lack of manual verification.

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