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Description

Hello! This is a simple analysis of Doublelift’s performance during the 2018 NALCS Spring Season. Doublelift is the ingame name of a League of Legends player who plays ADC position for Team Liquid.

The dataset used this analysis is taken from https://oracleselixir.com/match-data/.

For those we do not play League of Legends, here is a quick description:

Developed by Riot Games, League of legends has become one of the most popular multiplayer video games in the world. In League of Legends, players compete in matches, lasting anywhere from 20 to 60 minutes on average. In each game mode teams work together to achieve a victory condition, typically destroying the core building (called the Nexus) in the enemy team’s base after bypassing a line of defensive structures called turrets, or towers.

Summoner’s Rift is the most popular game mode in League of Legends. The objective of the blue team, for example, is to destroy the red team’s nexus (located in top right corner). To do this, players must control characters called champions and work together with other players to defeat the enemy players and ultimately their base. It is a fast-paced game that involves communication, teamwork and coordination between players on the same team.

Code for slicing the data: 
XLS_FILENAME <- '2018-spring-match-data-OraclesElixir-2018-05-20.xlsx'
springData <- read_excel(XLS_FILENAME)
# slicedByPlayer is a function that slices the springData set by player position and handle
doubleLift <- sliceByPlayer("ADC", "Doublelift")

Data Preview

Here is a preview of the dataset we are analyzing. 
# A tibble: 6 x 98
  gameid  url       league split   date week   game patchno playerid side 
  <chr>   <chr>     <chr>  <chr>  <dbl> <chr> <dbl>   <dbl>    <dbl> <chr>
1 100244… https://… NALCS  2018… 43121. 1         1    8.01        4 Blue 
2 100244… https://… NALCS  2018… 43122. 1         2    8.01        9 Red  
3 100244… https://… NALCS  2018… 43128. 2         1    8.01        9 Red  
4 100244… https://… NALCS  2018… 43129. 2         2    8.01        4 Blue 
5 100245… https://… NALCS  2018… 43135. 3         1    8.02        9 Red  
6 100245… https://… NALCS  2018… 43136. 3         2    8.02        9 Red  
# ... with 88 more variables: position <chr>, player <chr>, team <chr>,
#   champion <chr>, ban1 <chr>, ban2 <chr>, ban3 <chr>, ban4 <chr>,
#   ban5 <chr>, gamelength <dbl>, result <dbl>, k <dbl>, d <dbl>, a <dbl>,
#   teamkills <dbl>, teamdeaths <dbl>, doubles <dbl>, triples <dbl>,
#   quadras <dbl>, pentas <dbl>, fb <dbl>, fbassist <dbl>, fbvictim <dbl>,
#   fbtime <dbl>, kpm <dbl>, okpm <dbl>, ckpm <dbl>, fd <dbl>,
#   fdtime <dbl>, teamdragkills <dbl>, oppdragkills <dbl>,
#   elementals <dbl>, oppelementals <dbl>, firedrakes <dbl>,
#   waterdrakes <dbl>, earthdrakes <dbl>, airdrakes <dbl>, elders <dbl>,
#   oppelders <dbl>, herald <dbl>, heraldtime <dbl>, ft <dbl>,
#   fttime <dbl>, firstmidouter <dbl>, firsttothreetowers <dbl>,
#   teamtowerkills <dbl>, opptowerkills <dbl>, fbaron <dbl>,
#   fbarontime <dbl>, teambaronkills <dbl>, oppbaronkills <dbl>,
#   dmgtochamps <dbl>, dmgtochampsperminute <dbl>, dmgshare <dbl>,
#   earnedgoldshare <dbl>, wards <dbl>, wpm <dbl>, wardshare <dbl>,
#   wardkills <dbl>, wcpm <dbl>, visionwards <dbl>, visionwardbuys <dbl>,
#   visiblewardclearrate <dbl>, invisiblewardclearrate <dbl>,
#   totalgold <dbl>, earnedgpm <dbl>, goldspent <dbl>, gspd <dbl>,
#   minionkills <dbl>, monsterkills <dbl>, monsterkillsownjungle <dbl>,
#   monsterkillsenemyjungle <dbl>, cspm <dbl>, goldat10 <dbl>,
#   oppgoldat10 <dbl>, gdat10 <dbl>, goldat15 <dbl>, oppgoldat15 <dbl>,
#   gdat15 <dbl>, xpat10 <dbl>, oppxpat10 <dbl>, xpdat10 <dbl>,
#   csat10 <dbl>, oppcsat10 <dbl>, csdat10 <dbl>, csat15 <dbl>,
#   oppcsat15 <dbl>, csdat15 <dbl>

Slicing

Here we extract the data we want. 
dmgPerChamp <- doubleLift %>% 
  select(champion, dmgtochamps, dmgtochampsperminute) %>%
  group_by(champion) %>%
  summarise(mean = mean(dmgtochamps),
            DmgPerMinute = mean(dmgtochampsperminute)) %>% 
  arrange(-mean) %>% 
  ungroup() %>% 
  data.frame()
dmgPerChamp$champion <- factor(dmgPerChamp$champion, levels = dmgPerChamp$champion[order(-dmgPerChamp$mean)])
dmgPerChamp
  champion     mean DmgPerMinute
1     Jhin 30885.40     866.0483
2  Kog'Maw 27985.00     694.5192
3  Caitlyn 24623.60     704.7594
4     Ashe 23597.00     794.6013
5   Ezreal 20752.50     574.0597
6 Tristana 13507.86     421.1571
7  Kalista 13365.00     363.1793
8    Xayah 13205.00     423.6898
9    Varus 10842.25     343.2878

Damage Per Champion Graph

More

There will be more content in the future. I promise.

Thanks! - Kai