League of Legends Dataset

Introduction

League of Legends is a MOBA video games, where 2 teams of 5 players compete against each other to destroy the opposing base. Its a team-based strategy game, where the players have over 140 champions to choose from to make plays, secure kills, and destroy enemy towers as you make your way towards the opponent’s base on the Summoner’s Rift. Because it is a team-based strategy game, it is important to pick and choose the correct champions that fit your team composition. Depending on the team compositions, the game length can vary, from passive late game compositions to aggressive early game compositions.

The League of Legends Dataset consists of League of Legends competitive matches between 2015-2017. The matches include the NALCS, EULCS, LCK, LMS, and CBLoL leagues as well as the World Championship and Mid-Season Invitational tournaments. In our project we used the following variables:

  • Year - Year the match took place in
  • blueTeamTag - Blue Team’s name
  • bResult - Result of the match for Blue Team - 1 is a win, 0 is a loss
  • redTeamTag - Red Team’s name
  • rResult - Result of the match for Red Team - 1 is a win, 0 is a loss
  • blueTopChamp - Name of Blue Team’s champion in the top position
  • blueMiddleChamp - Name of Blue Team’s champion in the middle position
  • blueJungleChamp - Name of Blue Team’s champion in the jungle position
  • blueADCChamp - Name of Blue Team’s champion in the ADC position
  • blueSupportChamp - Name of Blue Team’s champion in the support position
  • redTopChamp - Name of Red Team’s champion in the top position
  • redMiddleChamp - Name of Red Team’s champion in the middle position
  • redJungleChamp - Name of Red Team’s champion in the jungle position
  • redADCChamp - Name of Red Team’s champion in the ADC position
  • redSupportChamp - Name of Red Team’s champion in the support position
  • gamelength - Game length in minutes
  • League - League or Tournament the match took place in
  • Team - Which team (blue or red) killed the victim
  • x_pos - x-coordinate for every death
  • y_pos - y-coordinate for every death

Research Questions

  1. How have the trends in the game changed over time in terms of teams and popular champions? 2.How do the different regions vary in game length and champion picks?
  2. What factors affect games and their outcomes?

Question 2: How do the different regions vary in gamelength and champion picks?

In order to answer this question, we focused on the variables each of the champions picked for both blue team and red team in each roles (blueTopChamp, blueJungleChamp, blueMiddleChamp, blueADCChamp, blueSupportChamp, redTopChamp, redJungleChamp, redMiddleChamp, redADCChamp, redSupportChamp), the length of the game (gamelength), and the different regions (League).

For the champions picked, we combined all ten champion variables to account for every champion picked in competitive games. "

We then found the top 10 most picked champions by regions. We only focused on the Top 4 regions, NALCS, EULCS, LCK and LPL. However, the data is missing LPL, hence we will only focus on the first three regions. From our results, EULCS picked Gragas, Thresh, Reksai, Braum, Sivir, Lucian, Elise, Corki, Gnar, and Alistar as their top 10 most picked champions, respectively, LCK picked Alistar, RekSai, Elise, Maokai, Gragas, Corki, Sivir, Braum, Ashe, and Thresh as their top 10 most picked champions, respectively, and NALCS picked Gragas, RekSai, Braum, Karma, Maokai, Corki, Sivir, Ashe, Lucian, and Elise as their top 10 most picked champions, respectively. The three leagues have many champions in common, such as Gragas, Alistar, RekSai, Braum, Sivir, and Elise appearing in all three regions’ top 10 most picked champions. Thresh only appeared in the Top 10 most picked champions for EULCS and LCK. Lucian only appeared in the Top 10 most picked champions for EULCS and NALCS. Ashe and Maokai only appeared in the Top 10 most picked champions for LCK and NALCS. Karma only appeared in the Top 10 most picked champions for NALCS. Gnar only appeared in the Top 10 most picked champions for EULCS.

One thing to note before taking the graph into consideration is the fact that there are more LCK games recorded than there are of other leagues.

To better see the differences in picked champions, we will make a faceted bar graph of the most commonly picked champions, which are Gragas, Thresh, RekSai, Braum, Elise, Alistar, Maokai, Corki, Lucian and Sivir."

Graph 5

From our faceted bar graph of Top 10 champions by the top 3 regions, we can better examine the difference between pick rates of the Top 10 most popular champions. On the x-axis, the Top 10 champions are shown. On the y-axis, the number of times the champion was picked is shown. From the graph, we can determine that Gragas is most picked for EULCS and NALCS, while Alistar is most picked for NALCS.

The overall distribution from the graph makes sense, as champions such as Alistar, Elise, Maokai, Sivir and Thresh are relatively old champions. It also makes sense that Lucian, Corki and Gragas are on the list because these three champions have been used in multiple roles.

Since we know the Top 3 leagues share the Top 10 most picked champions, we will compare gamelength by all regions to determine whether each regions vary in gamelength.

Graph 6

In the histogram, the x-axis represents game length of each games, and the y-axis represents the number of games. For all regions, we can examine that the graph is uni-modal and right-skewed, with the peak around 30 to 40 minutes. This is more evident in the Top 3 regions, where more games are played and recorded. Most games last between 20 minutes and 60 minutes, with few outliers of games lasting beyond 60 minutes in EULCS, LCK, LJL, LMS, NALCS, TCL and at the Worlds Championship.

Question 3: What factors affect the games and their outcomes?

We wanted to explore statistical insights into the game and additionally what factors affected the outcomes of games. With our data available we wanted to see if any information could help inform players of the game on things to watch out for when playing themselves. We first started by plotting all of the kills recorded within our data onto a heat map given the kills x and y coordinates and which team did the killing. We chose a hexagonal heat map as it was a good balance between a continuous one and a tile one given the nature of our map containing solid terrain with varied edges.

Graph 7

From our graph we can clearly see increased areas of kill activity going down the 3 lanes (top, mid, bot) especially the mid lane, as well as within the river (diagonally top left to bottom right) for both kills made by teams starting on the blue side and red side. Clear hexagons indicate no kills were recorded there, and from a image of the map one can see that it is because of terrain occupying that area. League map We also see the highest gradient hexagon for blue side kills within the Baron pit, a semi-circle within which a neutral boss can be fought, the killer of which receives a big gold boost and temporary buff to all their teammates. Knowing that these areas have a high kill amount, players should be wary when in these areas especially if they are playing on the red side. It seems that the red side has a unique disadvantage given that the walls of the objective only face them. Further investigating this disparity between sides we decided to run a binomial test against the win-rates given side across all competitive games. Players should be wary of this statistical difference in sides, but as the sides are decided randomly they cannot do anything about it.

## 
##  Exact binomial test
## 
## data:  wins
## number of successes = 4146, number of trials = 7620, p-value =
## 1.452e-14
## alternative hypothesis: true probability of success is not equal to 0.5
## 95 percent confidence interval:
##  0.5328311 0.5553241
## sample estimates:
## probability of success 
##              0.5440945

With a p-value of close to 0, we reject the null that the side one team gets is independent from their outcome, noting therefore there is some relationship between side and winning. We see from our code that those teams that are on the blue side had a 54.40945% win rate, thus our previous investigation into the map shape and kill locations may have played a role in deciding the outcome of games given that the difference in sides is statistically significant.

Conclusion

Through our research questions and investigation into this League of Legends dataset we hope to have provided insights into the nature of League of Legend’s competitive scene and the ways in which games are won and lost. We noted the dominance of SKT, as other teams were trying to catch up to and try to improve their skills while SKT had over 90 games won since 2015. We also saw that each year there is a variety of top 5 teams that get the most teams while 1 or 2 teams stay consistently in the top 5. Furthermore, we concluded a greater trend about this game that each year teams are improving to win more and more games as the game gets more competitive because the amount of games that teams win increases. We can also conclude that there are more games being held in League of Legends probably because the popularity is growing year by year. Moreover, we made the inference that the most popular champion are the strongest champions as they are picked the most often. It is rare to see champions being the most popular in consecutive years, which suggests that the game developers change the champions’ stats (abilities, powers) when they get too strong as they do not want a couple champions to dominate the game. We can infer that game developers like to increase the power of weaker champion and decrease the power of stronger champions to add variety to the game. We also noted that between regions the most popular champions differed, indicating that different leagues have different competitive scenes and strategies to win. Additionally we found that most games last between 20 minutes and 60 minutes, with few outliers of games lasting beyond 60 minutes in EULCS, LCK, LJL, LMS, NALCS, TCL and at the Worlds Championship. Finally, newcomers to League of Legends will find of particular interest after the choices made within the competitive scene for champion picks the inherent benefit of playing on the blue side vs. red. We hope that those interested in the competitive scene itself will find the insights into popular teams and their performances across the years potentially useful in correlation to their favorite players and how such player‘s may or may not have contributed to their favorite team’s success over time, and we hope our insights will be beneficial and informative to all who view them.