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Big Data Programming-Final Project
- 1. League of Legends Matches Analyses Big Data Programing Project 2, December 2018 Project 2 Group 5 Ammar Khan, Feng Zhang, Shuhui Wu, Thi Tran-Nguyen and Jiawei Wang
- 2. Outline 1. Players performance 2. Champions performance 3. Does the number of minions killed affect winning outcome? 4. Do ward positions affect winning outcome?
- 3. Distribution of total matches and win ratio per player A B C D
- 4. The 10 most active players Player win lose Total matches Win ratio [unknown] 50 86 136 0.37 noelleis 48 82 130 0.37 jantrance 57 70 127 0.45 Prophecy Beetle 55 66 121 0.45 Deusald 73 42 115 0.63 èuler 57 54 111 0.51 FrozenxxH ero 54 43 97 0.56 Qeudrito 60 36 96 0.63 Sabbé 48 45 93 0.52
- 5. Top 10 best players (>=50 games cut off) player win lose Total matches Win ratio Klacky 39 15 54 0.72 iiaann 38 15 53 0.71 YOLO SWAG 40 18 58 0.69 starhunterz 48 24 72 0.67 CuteKingK ongDong 34 17 51 0.67 Boe 49 25 74 0.66 RUZ3 Aryze 46 24 70 0.66 Quality 44 23 67 0.66 Pándá 52 28 80 0.65 GetJecht 33 18 51 0.65
- 6. win ratio distribution among active players
- 8. The 10 best champions (>=50 matches) Champion name championID win lose sum win ratio League of Graph Warwick 19 73 48 121 0.6 0.51 Talon 91 43 31 74 0.58 0.49 Fiddlestick s 9 82 69 151 0.54 0.51 Vladimir 8 39 35 74 0.53 0.5 Janna 40 199 179 378 0.53 0.52 Blitzcrank 53 129 4 1166 2460 0.53 0.51 Amumu 32 160 1 1448 3049 0.53 0.51 Kog 'Maw 96 331 305 636 0.52 0.52 Tristana 18 82 76 158 0.52 0.47 Riven 92 142 133 275 0.52 0.5
- 9. Win ratio across 9 regions A B C D
- 10. Minions killing and win outcome 1st 2nd 3rd 4th 5th 6th 7th 8th 9th 10th 10 15 20 25 Lost Games Won games Does number of minions killed affect winnning? normalized time interval minionskilled(x105)
- 11. Do ward positions affect winning outcome? Initial Phase
- 12. Do ward positions affect winning outcome? Mid Phase
- 13. Do ward positions affect winning outcome? Last Phase
- 15. Conclusions 1. Active players (>=50 games) are more likely to win than lose (win ratio ~ 0.6) 2. Champion win ratio is remarkably well-balanced (win ratio ~ 0.5) 3. Killing more minions may hurt the chance to win the game 4. Ward positions: Towards the end of the games, more wards are concentrated in the middle lane. No special patterns were found to distinguish between win and lose teams.
Editor's Notes
- In this project, we sought out to analyze the LOL dataset to gain more insights into (1) players performance, (2) champions performance. We also investigated certain aspects of the game such as (3) minion killing, (4) buff duration and (5) ward positions to see if they affect winning.
- the majority (87.5%) of players only played one game. Similarly, we plotted the CDF distribution of total won games per player (Figure 1B), and it showed that 50% of users didn’t win any game, and about 90% didn’t win more than 4 games. This isn’t surprising considering most players only played one game. This explains the somewhat peculiar findings in the distribution of win ratio (Figure 1C and 1D). 50% of users win 0% of the time whereas another 50% win 100% of the time. Since most players only play one game, they can either win or lose in that game and therefore, this result is expected.
- Our initial goal was to find the best players in this dataset, as defined by players with the best win ratio. However, we quickly realized that we cannot simply output the best win ratio as 1 (100% win rate) for players who only played one game because it is unfair for other players who play more games. We then checked the most active players in this data set, as defined by players with the most matches (Table 1A). As shown in Table 1A, the most active player [unknown] has a total of 136 games, with win-ratio of 0.37, which is rather discouraging. In fact, the top 4 active players had win rate less than 0.5.
- We then define a different set of criteria for best players, which is that they have to play frequently, with >= 50 games in total. Among those active players, we will select the ones with best win ratio (Table 1B). We also checked the distribution of win ratio for such active players (Figure 1E). The distribution is centered around 0.6 win rate, which means that these frequent players win more often than they lose.
- We then define a different set of criteria for best players, which is that they have to play frequently, with >= 50 games in total. Among those active players, we will select the ones with best win ratio (Table 1B). We also checked the distribution of win ratio for such active players (Figure 1E). The distribution is centered around 0.6 win rate, which means that these frequent players win more often than they lose.
- As shown in figure 2, the majority of champion were summoned in fewer than 500 games, which correspond to their win/lose occurrences (less than 250 win or lose counts). The average win ratio per champion is about 0.5, with a large number of champions win or lose 100% of the time, probably due to the fact that some only were summoned a few times.
- In terms of champion’s win-ratio, it is remarkably well-balanced with win ratio about 0.5 on average for each of the 86 champions. The best performed champion in our dataset is Warwick with win ratio of 0.6. We also compared our result with that of League of Graph, and in general, there weren’t anything particularly divergent.
- Region euw (European West, or geographically Amsterdam and Netherlands), and na (North America, or specifically Chicago, America) stand out from other regions in terms of total win, lose or total games, probably because they have more players (we verified this by checking the player data by region). However, in terms of win ratio, these popular regions perform only averagely with win ratio hovering around 0.5, or even less than that of North America. Lan (Latin America North, more specifically Miami, America) region perform the worst, with win ratio about 0.25 on average. Tr (Turkey) region perform the best in terms of win ratio.
- when we looked across each phase of the game, we found a consistent trend that the losing group always killed more minions than winning group (Figure 3). At first, this came as a surprise to us. However, on second thought, it is possible that focusing on killing the minions isn’t a good strategy to win the game because minions are created recurrently from the nexus anyhow. Minions need to be destroyed during the game, but they may distract the players from advancing to destroy the nexus more so than they are harmful.
- According to leagueoflegends.wikia, a ward is “a deployable unit that removes the fog of war over the surrounding area” [9]. Therefore, we believe that strategic placement of ward in specific locations in the LOL map will help players win the game. We sought to test out this hypothesis by dividing the dataset into won and lost teams, and extracting the coordinates of the ward positions. We also partitioned the dataset into 3 temporal phases (namely initial phase, mid-phase and end-phase) which are normalized according to the length of each game. We plotted the X and Y coordinate of ward positions for each phase using ggplot2’s geom_point() function. To address the over-plotting issue, we also use another 2D kernel density graph function in ggplot2 called stat_density_2d(), which will also show the density of the overlapped points In general, in the initial phase, there are still gaps in the maps where the wards are not filled. As the games progress, we see more wards being placed which filled almost the entire map. We also see another temporal pattern that in the beginning, more wards are being placed at the top left and bottom right corner but later at the end of the game, wards are being placed more often in the middle lane (see Figure 4D for the map of the game). However, there weren’t any distinct patterns to help distinguish the won teams from the lost teams.
- In general, in the initial phase, there are still gaps in the maps where the wards are not filled. As the games progress, we see more wards being placed which filled almost the entire map. We also see another temporal pattern that in the beginning, more wards are being placed at the top left and bottom right corner but later at the end of the game, wards are being placed more often in the middle lane (see Figure 4D for the map of the game). However, there weren’t any distinct patterns to help distinguish the won teams from the lost teams.
- In general, in the initial phase, there are still gaps in the maps where the wards are not filled. As the games progress, we see more wards being placed which filled almost the entire map. We also see another temporal pattern that in the beginning, more wards are being placed at the top left and bottom right corner but later at the end of the game, wards are being placed more often in the middle lane (see Figure 4D for the map of the game). However, there weren’t any distinct patterns to help distinguish the won teams from the lost teams.
- In general, in the initial phase, there are still gaps in the maps where the wards are not filled. As the games progress, we see more wards being placed which filled almost the entire map. We also see another temporal pattern that in the beginning, more wards are being placed at the top left and bottom right corner but later at the end of the game, wards are being placed more often in the middle lane (see Figure 4D for the map of the game). However, there weren’t any distinct patterns to help distinguish the won teams from the lost teams.