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Commit b29ce52

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add analytics and graphs
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‎analytics/analytics.md‎

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# LeetCode Contest Analytics
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The following observations are conducted with **_Amazon Redshift_**.
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## Data Overview
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- The collected data are from the top **50,000** users of _LeetCode_ global contest ranking and **2334** problems
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of **585** contests.
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- The range of ratings in the data collected is **1788 - 3700**.
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## Duplicated Users
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- There are roughly **60** users that appear more than once in the collected data, not including _NULL_ or deleted users.
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- Their accounts exist in both US and CN data regions.
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## Countries by Number of Users
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![](./images/user_distribution.png)
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- Approximately **15,600** users are from unknown countries.
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- China and India are the two largest populations in the world. The markets in these countries are probably more
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competitive as well.
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- These top countries are likely more competitive and developed in the tech market.
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## Average Ranking by Rating Bracket
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![](./images/avg_bracket_ranking.jpeg)
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- _Average ranking_ is the average placement of all contests a user attended.
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- Overall, the average ranking distribution is pretty diverse, except for the top contestants.
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- The lower the rating, the more diverse the average ranking.
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- It can be inferred from the data that users with higher ratings usually perform more consistently across contests.
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- Contestants can predict their future growth and potential rating bracket based on this graph. Eg: A person with a
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current rating of 2000 and an average ranking of 100 can possibly reach 3250+ without difficulty.
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## Topics by Number of Appearances
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![](./images/topics.jpeg)
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- These topic tags show up most frequently during contests.
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- The topic tags in _LeetCode_ might be insufficient. Eg: A problem uses the line sweep technique, but it's not tagged line sweep.
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- Practicing these topics accordingly might improve your average contest placement.
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‎analytics/images/topics.jpeg‎

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