Data
League-of-Legend-High-Elo-Team-Comp--Game-Length

League-of-Legend-High-Elo-Team-Comp--Game-Length

active ARFF Database: Open Database, Contents: Database Contents Visibility: public Uploaded 24-03-2022 by Elif Ceren Gok
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Context I was exploring League of Legends datasets to play around but since Riot allows limited calls to their API, I've collected the data from OP.GG. Few goals of mine were to find out the best team compositions and to predict Victory/Loss given a team comp and game length. The most recent games on the dataset are on 2020 October 16th. Content The dataset consists of ranked matches from Korea(WWW), North America(NA), Eastern Europe(EUNE), and Western Europe(EUW) servers. It has which team won the match, the total time of the match, blue team composition and red team composition. Note that only the high elo games were added this includes Challenger, Grand Master, Master and sometimes even High Diamonds. Note that there are 151 total unique champions with 'Samira' as the latest addition. You may find my blog post useful. (On quick data cleaning and analysis) https://leejaeka.github.io/jaekangai/fastpages/jupyter/2020/10/28/lolpredict.html

6 features

game_lengthstring292 unique values
0 missing
mmr (ignore)numeric0 unique values
4028 missing
resultstring2 unique values
0 missing
serverstring4 unique values
0 missing
team_1string2334 unique values
0 missing
team_2string2328 unique values
0 missing
timestampstring2333 unique values
0 missing

19 properties

4028
Number of instances (rows) of the dataset.
6
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
0
Number of numeric attributes.
0
Number of nominal attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
Average class difference between consecutive instances.
0
Percentage of numeric attributes.
0
Number of attributes divided by the number of instances.
0
Percentage of nominal attributes.
Percentage of instances belonging to the most frequent class.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.

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