LoLAnalysis (PDF)

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Analyzing League of Legends Winners
Ricardo Rivera, Billy Bibbs, Zachary Shoemaker,
Taelor Rivera, Mark Preedy, and Kathy Macropol

Arcadia University Computer Science and Mathematics Department

Online gaming is a billion dollar
industry and is growing larger
each day [1]. For example, more
people tuned in to watch the
League of Legends world final (32
million) than the NBA finals (26.3
million) [2]. Electronic sports (or
eSports) have become popular
and are extremely competitive.
Tournaments in the eSports
community range from smaller
tournaments with minimal cash
prizes to larger tournaments held
in stadiums with million dollar

Methods & Data

Dataset: A list of in-game, individual performances for each player in their team.
The attributes included, for each player:


kill participation

kill/death ratio

creep score per minute

total kills

total creep score

total deaths

minutes played

total assists

win/loss ratio
In total we included data from 246 matches, obtained from Riot (the
game developer).


To analyze this dataset we created multiple Decision Tree classifiers
(using WEKA, an open source data mining toolkit) from various subsets
of the data, each predicting how likely a team was to win based on their
players’ individual in-game statistics. One of the resulting Decision Trees
is shown in Figure 1.

In this project, our goal was to
analyze eSport data and matches
using data mining techniques.
Specifically, we chose the game
“League of Legends,” a popular
online team-based game where
two teams of five players
compete to see which team can
destroy a target first.
We hoped, from our analysis,
to see what in-game factors
tended to influence the outcomes
for these matches, and whether it
was possible to predict match
outcome from these attributes.

Figure 1: Decision Tree Classifier

Overall, our Decision Tree
classifiers were able to predict match
outcome with an over 97% accuracy
rate, emphasizing the connection
between the in-game attributes
collected and final match result.
Analyzing the splits within the
obtained Decision Tree, it can be
seen that the number of player kills /
deaths contributed most toward
determination of team result,
followed after by creep score and


From our study, it can be seen
that several of the collected
attributes within our dataset did
indeed strongly contribute to the
prediction of League of Legend match
results. In fact, the factors contributing most were the number of
player kills / deaths, over other
factors such as creep score or player
Our results help indicate that
further analysis and mining of ingame data could provide useful and
interesting information, both for
players hoping to improve their
game, as well as companies hoping to
improve in-game experiences and
fairness for their players.

References: [1] [2]

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