ŽAIDIMO "LEAGUE OF LEGENDS" DUOMENŲ ANALIZĖ
League of legends yra MOBA(multiplayer online battle arena) tipo žaidimas, žaidžiamas 10 žaidėju realiu laiku.
Žaidėjai pasiskirsto į komandas po 5. Kiekvienas žaidėjas pasirenka vieną iš 156 esamų personažų ir jį valdo vieno žaidimo metu.
Kiekvienas personažas turi specialią galią ir dalyvauja vienoje iš 5 rolių.(ADC,SUPPORT,MID,TOP,JUNGLE)
Žaidimą laimi komanda pirma sugriovusi kitos komandos 'Inhibitor' bokštą (bazę), bet iki to seka trys 'Towers'(bokšteliai) kiekvienos linijos (yra 3) kuriuos reikia nugriauti.
Kiekviena komanda gauna specialių galių arba papildomai pinigų nukovojusius 'Dragon', 'Baron', 'Rift Herald' ir kt pabaisas.
Taip pat gavę 'first blood' arba nuvertę 'first tower'
ANALIZĖS APRAŠYMAS
Mano analizę sudarys 3 dalys:
Duomenų sukėlimas ("Diamond" lyga)
import numpy as np
import pandas as pd
import seaborn as sns
import json
import matplotlib
import matplotlib.pyplot as plt
champ_info=pd.read_json('C:\\Users\\migle\\Desktop\\baigiamasis\\champs and bans\\champion_info.json')
champ_info= pd.read_json((champ_info['data']).to_json(),orient='index')
champ_info
title | id | key | name | |
---|---|---|---|---|
1 | the Dark Child | 1 | Annie | Annie |
10 | The Judicator | 10 | Kayle | Kayle |
101 | the Magus Ascendant | 101 | Xerath | Xerath |
102 | the Half-Dragon | 102 | Shyvana | Shyvana |
103 | the Nine-Tailed Fox | 103 | Ahri | Ahri |
... | ... | ... | ... | ... |
91 | the Blade's Shadow | 91 | Talon | Talon |
92 | the Exile | 92 | Riven | Riven |
96 | the Mouth of the Abyss | 96 | KogMaw | Kog'Maw |
98 | the Eye of Twilight | 98 | Shen | Shen |
99 | the Lady of Luminosity | 99 | Lux | Lux |
138 rows × 4 columns
champ_info2=pd.read_json('C:\\Users\\migle\\Desktop\\baigiamasis\\champs and bans\\champion_info_2.json')
champ_info2 = pd.read_json((champ_info2['data']).to_json(),orient='index')
champ_info2
tags | title | id | key | name | |
---|---|---|---|---|---|
Aatrox | [Fighter, Tank] | the Darkin Blade | 266 | Aatrox | Aatrox |
Ahri | [Mage, Assassin] | the Nine-Tailed Fox | 103 | Ahri | Ahri |
Akali | [Assassin] | the Fist of Shadow | 84 | Akali | Akali |
Alistar | [Tank, Support] | the Minotaur | 12 | Alistar | Alistar |
Amumu | [Tank, Mage] | the Sad Mummy | 32 | Amumu | Amumu |
... | ... | ... | ... | ... | ... |
Zac | [Tank, Fighter] | the Secret Weapon | 154 | Zac | Zac |
Zed | [Assassin, Fighter] | the Master of Shadows | 238 | Zed | Zed |
Ziggs | [Mage] | the Hexplosives Expert | 115 | Ziggs | Ziggs |
Zilean | [Support, Mage] | the Chronokeeper | 26 | Zilean | Zilean |
Zyra | [Mage, Support] | Rise of the Thorns | 143 | Zyra | Zyra |
139 rows × 5 columns
summoner_spell=pd.read_json('C:\\Users\\migle\\Desktop\\baigiamasis\\champs and bans\\summoner_spell_info.json')
summoner_spell= pd.read_json((summoner_spell['data']).to_json(),orient='index')
summoner_spell
id | summonerLevel | name | key | description | |
---|---|---|---|---|---|
1 | 1 | 6 | Cleanse | SummonerBoost | Removes all disables (excluding suppression an... |
11 | 11 | 10 | Smite | SummonerSmite | Deals 390-1000 true damage (depending on champ... |
12 | 12 | 6 | Teleport | SummonerTeleport | After channeling for 4.5 seconds, teleports yo... |
13 | 13 | 1 | Clarity | SummonerMana | Restores 50% of your champion's maximum Mana. ... |
14 | 14 | 10 | Ignite | SummonerDot | Ignites target enemy champion, dealing 70-410 ... |
21 | 21 | 4 | Barrier | SummonerBarrier | Shields your champion from 115-455 damage (dep... |
3 | 3 | 4 | Exhaust | SummonerExhaust | Exhausts target enemy champion, reducing their... |
30 | 30 | 1 | To the King! | SummonerPoroRecall | Quickly travel to the Poro King's side. |
31 | 31 | 1 | Poro Toss | SummonerPoroThrow | Toss a Poro at your enemies. If it hits, you c... |
32 | 32 | 1 | Mark | SummonerSnowball | Throw a snowball in a straight line at your en... |
33 | 33 | 1 | Nexus Siege: Siege Weapon Slot | SummonerSiegeChampSelect1 | In Nexus Siege, Summoner Spells are replaced w... |
34 | 34 | 1 | Nexus Siege: Siege Weapon Slot | SummonerSiegeChampSelect2 | In Nexus Siege, Summoner Spells are replaced w... |
35 | 35 | 1 | Disabled Summoner Spells | SummonerDarkStarChampSelect1 | Summoner spells are disabled in this mode. |
36 | 36 | 1 | Disabled Summoner Spells | SummonerDarkStarChampSelect2 | Summoner spells are disabled in this mode. |
4 | 4 | 8 | Flash | SummonerFlash | Teleports your champion a short distance towar... |
6 | 6 | 1 | Ghost | SummonerHaste | Your champion gains increased Movement Speed a... |
7 | 7 | 1 | Heal | SummonerHeal | Restores 90-345 Health (depending on champion ... |
ranked_games=pd.read_csv('C:\\Users\\migle\\Desktop\\baigiamasis\\champs and bans\\games.csv')
ranked_games
gameId | creationTime | gameDuration | seasonId | winner | firstBlood | firstTower | firstInhibitor | firstBaron | firstDragon | ... | t2_towerKills | t2_inhibitorKills | t2_baronKills | t2_dragonKills | t2_riftHeraldKills | t2_ban1 | t2_ban2 | t2_ban3 | t2_ban4 | t2_ban5 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 3326086514 | 1504279457970 | 1949 | 9 | 1 | 2 | 1 | 1 | 1 | 1 | ... | 5 | 0 | 0 | 1 | 1 | 114 | 67 | 43 | 16 | 51 |
1 | 3229566029 | 1497848803862 | 1851 | 9 | 1 | 1 | 1 | 1 | 0 | 1 | ... | 2 | 0 | 0 | 0 | 0 | 11 | 67 | 238 | 51 | 420 |
2 | 3327363504 | 1504360103310 | 1493 | 9 | 1 | 2 | 1 | 1 | 1 | 2 | ... | 2 | 0 | 0 | 1 | 0 | 157 | 238 | 121 | 57 | 28 |
3 | 3326856598 | 1504348503996 | 1758 | 9 | 1 | 1 | 1 | 1 | 1 | 1 | ... | 0 | 0 | 0 | 0 | 0 | 164 | 18 | 141 | 40 | 51 |
4 | 3330080762 | 1504554410899 | 2094 | 9 | 1 | 2 | 1 | 1 | 1 | 1 | ... | 3 | 0 | 0 | 1 | 0 | 86 | 11 | 201 | 122 | 18 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
51485 | 3308904636 | 1503076540231 | 1944 | 9 | 2 | 1 | 2 | 2 | 0 | 2 | ... | 10 | 2 | 0 | 4 | 0 | 55 | -1 | 90 | 238 | 157 |
51486 | 3215685759 | 1496957179355 | 3304 | 9 | 2 | 1 | 1 | 2 | 2 | 2 | ... | 11 | 7 | 4 | 4 | 1 | 157 | 55 | 119 | 154 | 105 |
51487 | 3322765040 | 1504029863961 | 2156 | 9 | 2 | 2 | 2 | 2 | 0 | 1 | ... | 10 | 2 | 0 | 2 | 0 | 113 | 122 | 53 | 11 | 157 |
51488 | 3256675373 | 1499562036246 | 1475 | 9 | 2 | 2 | 2 | 2 | 0 | 2 | ... | 11 | 3 | 0 | 1 | 0 | 154 | 39 | 51 | 90 | 114 |
51489 | 3317333020 | 1503612754059 | 1445 | 9 | 1 | 1 | 1 | 1 | 1 | 2 | ... | 1 | 0 | 0 | 1 | 0 | 11 | 157 | 141 | 31 | 18 |
51490 rows × 61 columns
a1 = ranked_games['gameDuration'].min()
b1 = ranked_games['gameDuration'].max()
c1 = ranked_games['gameDuration'].mean()
print(a1)
print(b1)
print(c1)
190 4728 1832.3628083122937
fig_1 = sns.displot(ranked_games['gameDuration'], bins=90)
plt.xlabel('Time(s)')
plt.ylabel('How many matches')
plt.title('Game time', fontsize = 18)
plt.show(fig_1)
Minimalios reikšmės pvz 190s nusako jog žaidime galejo būti trukdžių, ne visi žaidėjai galėjo prisijungti, iš kart nubalsuotas pasidavimas, bug'as išsijungė žaidimas.
Maksimalios reikšmės pvz 4728s, nusako įtemptą žaidimą, lygias komandų jėgas, panašias strategijas, counter pick'us.
Pagal grafiką galim įžvelgti jog dažniausiai pasitaikantis laiko tarpas per kurį vyksta vienas žaidimas, atitinka vidutinę (mean) žaidimo trukmę.
komandu_pav_pakeitimas = ranked_games.replace([0,1,2],['Nei viena','Blue','Red'])
komandu_pav_pakeitimas
gameId | creationTime | gameDuration | seasonId | winner | firstBlood | firstTower | firstInhibitor | firstBaron | firstDragon | ... | t2_towerKills | t2_inhibitorKills | t2_baronKills | t2_dragonKills | t2_riftHeraldKills | t2_ban1 | t2_ban2 | t2_ban3 | t2_ban4 | t2_ban5 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 3326086514 | 1504279457970 | 1949 | 9 | Blue | Red | Blue | Blue | Blue | Blue | ... | 5 | Nei viena | Nei viena | Blue | Blue | 114 | 67 | 43 | 16 | 51 |
1 | 3229566029 | 1497848803862 | 1851 | 9 | Blue | Blue | Blue | Blue | Nei viena | Blue | ... | Red | Nei viena | Nei viena | Nei viena | Nei viena | 11 | 67 | 238 | 51 | 420 |
2 | 3327363504 | 1504360103310 | 1493 | 9 | Blue | Red | Blue | Blue | Blue | Red | ... | Red | Nei viena | Nei viena | Blue | Nei viena | 157 | 238 | 121 | 57 | 28 |
3 | 3326856598 | 1504348503996 | 1758 | 9 | Blue | Blue | Blue | Blue | Blue | Blue | ... | Nei viena | Nei viena | Nei viena | Nei viena | Nei viena | 164 | 18 | 141 | 40 | 51 |
4 | 3330080762 | 1504554410899 | 2094 | 9 | Blue | Red | Blue | Blue | Blue | Blue | ... | 3 | Nei viena | Nei viena | Blue | Nei viena | 86 | 11 | 201 | 122 | 18 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
51485 | 3308904636 | 1503076540231 | 1944 | 9 | Red | Blue | Red | Red | Nei viena | Red | ... | 10 | Red | Nei viena | 4 | Nei viena | 55 | -1 | 90 | 238 | 157 |
51486 | 3215685759 | 1496957179355 | 3304 | 9 | Red | Blue | Blue | Red | Red | Red | ... | 11 | 7 | 4 | 4 | Blue | 157 | 55 | 119 | 154 | 105 |
51487 | 3322765040 | 1504029863961 | 2156 | 9 | Red | Red | Red | Red | Nei viena | Blue | ... | 10 | Red | Nei viena | Red | Nei viena | 113 | 122 | 53 | 11 | 157 |
51488 | 3256675373 | 1499562036246 | 1475 | 9 | Red | Red | Red | Red | Nei viena | Red | ... | 11 | 3 | Nei viena | Blue | Nei viena | 154 | 39 | 51 | 90 | 114 |
51489 | 3317333020 | 1503612754059 | 1445 | 9 | Blue | Blue | Blue | Blue | Blue | Red | ... | Blue | Nei viena | Nei viena | Blue | Nei viena | 11 | 157 | 141 | 31 | 18 |
51490 rows × 61 columns
Kiek iš viso kartų komandai pavyko, paimti firstBlood, firstTower, firstInhibitor, firstBaron, firstDragon, firstRiftHerald:
Pirmieji_veiksmai = ['firstBlood','firstTower', 'firstInhibitor', 'firstBaron', 'firstDragon', 'firstRiftHerald']
Pirmieji_veiksmai_viso = komandu_pav_pakeitimas[Pirmieji_veiksmai].apply(pd.value_counts)
Nauji_pav = ['Nei viena','Blue','Red']
Pirmieji_veiksmai_viso
firstBlood | firstTower | firstInhibitor | firstBaron | firstDragon | firstRiftHerald | |
---|---|---|---|---|---|---|
Blue | 26113 | 25861 | 23054 | 14758 | 24690 | 12948 |
Nei viena | 555 | 1213 | 6276 | 20258 | 2000 | 26179 |
Red | 24822 | 24416 | 22160 | 16474 | 24800 | 12363 |
Detalus kiekvienas matmuo ir jo grafikas:
ranked_games['firstBlood'].value_counts()
1 26113 2 24822 0 555 Name: firstBlood, dtype: int64
sns.countplot(x = 'firstBlood', data = ranked_games)
plt.xlabel('First blood')
plt.ylabel('How many times')
plt.title('First blood chart', fontsize = 18)
Text(0.5, 1.0, 'First blood chart')
Mėlyna (Blue) komanda daugiau kartų pasiimė 'first blood', skirtumas nežymus.
ranked_games['firstTower'].value_counts()
1 25861 2 24416 0 1213 Name: firstTower, dtype: int64
sns.countplot(x = 'firstTower', data = ranked_games)
plt.xlabel('First tower')
plt.ylabel('How many times')
plt.title('First tower chart', fontsize = 18)
plt.show
<function matplotlib.pyplot.show(close=None, block=None)>
Mėlyna (Blue) komanda daugiau kartų pasiimė 'first tower', skirtumas nežymus.
ranked_games['firstInhibitor'].value_counts()
1 23054 2 22160 0 6276 Name: firstInhibitor, dtype: int64
sns.countplot(x = 'firstInhibitor', data = ranked_games)
plt.xlabel('First inhibitor')
plt.ylabel('How many times')
plt.title('First inhibitor chart', fontsize = 18)
Text(0.5, 1.0, 'First inhibitor chart')
Mėlyna (Blue) komanda daugiau kartų žaidimuose 'best of 3', nuvertė 'inhibitor' bokštą, skirtumas nežymus.
ranked_games['firstBaron'].value_counts()
0 20258 2 16474 1 14758 Name: firstBaron, dtype: int64
sns.countplot(x = 'firstBaron', data = ranked_games)
plt.xlabel('First Baron')
plt.ylabel('How many times')
plt.title('First Baron chart', fontsize = 18)
Text(0.5, 1.0, 'First Baron chart')
Mėlyna (Blue) komanda daugiau kartų pasiimė 'first baron', nors ir skirtumas nėra didelis, stebina jog abi komandos daugiausia kartų nepasinaudojo 'boost' kurį duoda 'baron' pabaisa.
ranked_games['firstDragon'].value_counts()
2 24800 1 24690 0 2000 Name: firstDragon, dtype: int64
sns.countplot(x = 'firstDragon', data = ranked_games)
plt.xlabel('First Dragon')
plt.ylabel('How many times')
plt.title('First Dragon chart', fontsize = 18)
Text(0.5, 1.0, 'First Dragon chart')
Galima teigti jog 'First dragon' komandos pasidalijo pusiau, skirtumas visiškai nedaro įtakos.
ranked_games['firstRiftHerald'].value_counts()
0 26179 1 12948 2 12363 Name: firstRiftHerald, dtype: int64
sns.countplot(x = 'firstRiftHerald', data = ranked_games)
plt.xlabel('First Rift Herald')
plt.ylabel('How many times')
plt.title('First Rift Herald chart', fontsize = 18)
Text(0.5, 1.0, 'First Rift Herald chart')
Mėlyna (Blue) komanda daugiau kartų pasiimė 'First Rift Herald', taip pat kaip ir su 'Baron' situacija, stebina tai jog daugiau kartų ši pabaisa liko nepaliesta.
ranked_games['winner'].value_counts()
1 26077 2 25413 Name: winner, dtype: int64
sns.countplot(x = 'winner', data = ranked_games)
plt.xlabel('Winner')
plt.ylabel('How many times')
plt.title('Which team wins most of the games', fontsize = 18)
Text(0.5, 1.0, 'Which team wins most of the games')
Taip pat nežymiai daugiau kartų laimėjo mėlyną (Blue) komanda.
Galima teigti jog komandai įtakos turėjo jos įsiveržimas 'First blood', 'First turrent', 'First inhibitor' kategorijoje.
'First dragon' kategorija neturėjo įtakos.
Raudonajai (Red) komandai laimėti padėtų 'First Baron' ir 'First Rift Herald', kurie sustiprintų komandą mėlynos (blue) komandos atžvilgiu ir padėtų įgauti pranašumą.
ranked_games.columns
Index(['gameId', 'creationTime', 'gameDuration', 'seasonId', 'winner', 'firstBlood', 'firstTower', 'firstInhibitor', 'firstBaron', 'firstDragon', 'firstRiftHerald', 't1_champ1id', 't1_champ1_sum1', 't1_champ1_sum2', 't1_champ2id', 't1_champ2_sum1', 't1_champ2_sum2', 't1_champ3id', 't1_champ3_sum1', 't1_champ3_sum2', 't1_champ4id', 't1_champ4_sum1', 't1_champ4_sum2', 't1_champ5id', 't1_champ5_sum1', 't1_champ5_sum2', 't1_towerKills', 't1_inhibitorKills', 't1_baronKills', 't1_dragonKills', 't1_riftHeraldKills', 't1_ban1', 't1_ban2', 't1_ban3', 't1_ban4', 't1_ban5', 't2_champ1id', 't2_champ1_sum1', 't2_champ1_sum2', 't2_champ2id', 't2_champ2_sum1', 't2_champ2_sum2', 't2_champ3id', 't2_champ3_sum1', 't2_champ3_sum2', 't2_champ4id', 't2_champ4_sum1', 't2_champ4_sum2', 't2_champ5id', 't2_champ5_sum1', 't2_champ5_sum2', 't2_towerKills', 't2_inhibitorKills', 't2_baronKills', 't2_dragonKills', 't2_riftHeraldKills', 't2_ban1', 't2_ban2', 't2_ban3', 't2_ban4', 't2_ban5'], dtype='object')
champions_picked = ['t1_champ1id', 't1_champ2id', 't1_champ3id', 't1_champ4id', 't1_champ5id',
't2_champ1id', 't2_champ2id', 't2_champ3id', 't2_champ4id', 't2_champ5id']
champions_picked
['t1_champ1id', 't1_champ2id', 't1_champ3id', 't1_champ4id', 't1_champ5id', 't2_champ1id', 't2_champ2id', 't2_champ3id', 't2_champ4id', 't2_champ5id']
Champions_banned = ['t1_ban1', 't1_ban2', 't1_ban3', 't1_ban4', 't1_ban5',
't2_ban1', 't2_ban2', 't2_ban3', 't2_ban4', 't2_ban5']
Champions_banned
['t1_ban1', 't1_ban2', 't1_ban3', 't1_ban4', 't1_ban5', 't2_ban1', 't2_ban2', 't2_ban3', 't2_ban4', 't2_ban5']
champions = champ_info2[['id', 'name']]
champ_dictionary = dict(zip(champions['id'], champions['name']))
for champion in champions_picked:
pick = ranked_games[champion].replace(champ_dictionary, inplace=True)
for champion_bans in Champions_banned:
ban = ranked_games[champion_bans].replace(champ_dictionary, inplace=True)
champ_dictionary
{266: 'Aatrox', 103: 'Ahri', 84: 'Akali', 12: 'Alistar', 32: 'Amumu', 34: 'Anivia', 1: 'Annie', 22: 'Ashe', 136: 'Aurelion Sol', 268: 'Azir', 432: 'Bard', 53: 'Blitzcrank', 63: 'Brand', 201: 'Braum', 51: 'Caitlyn', 164: 'Camille', 69: 'Cassiopeia', 31: "Cho'Gath", 42: 'Corki', 122: 'Darius', 131: 'Diana', 36: 'Dr. Mundo', 119: 'Draven', 245: 'Ekko', 60: 'Elise', 28: 'Evelynn', 81: 'Ezreal', 9: 'Fiddlesticks', 114: 'Fiora', 105: 'Fizz', 3: 'Galio', 41: 'Gangplank', 86: 'Garen', 150: 'Gnar', 79: 'Gragas', 104: 'Graves', 120: 'Hecarim', 74: 'Heimerdinger', 420: 'Illaoi', 39: 'Irelia', 427: 'Ivern', 40: 'Janna', 59: 'Jarvan IV', 24: 'Jax', 126: 'Jayce', 202: 'Jhin', 222: 'Jinx', 429: 'Kalista', 43: 'Karma', 30: 'Karthus', 38: 'Kassadin', 55: 'Katarina', 10: 'Kayle', 141: 'Kayn', 85: 'Kennen', 121: "Kha'Zix", 203: 'Kindred', 240: 'Kled', 96: "Kog'Maw", 7: 'LeBlanc', 64: 'Lee Sin', 89: 'Leona', 127: 'Lissandra', 236: 'Lucian', 117: 'Lulu', 99: 'Lux', 54: 'Malphite', 90: 'Malzahar', 57: 'Maokai', 11: 'Master Yi', 21: 'Miss Fortune', 62: 'Wukong', 82: 'Mordekaiser', 25: 'Morgana', 267: 'Nami', 75: 'Nasus', 111: 'Nautilus', 76: 'Nidalee', 56: 'Nocturne', -1: 'None', 20: 'Nunu', 2: 'Olaf', 61: 'Orianna', 516: 'Ornn', 80: 'Pantheon', 78: 'Poppy', 133: 'Quinn', 497: 'Rakan', 33: 'Rammus', 421: "Rek'Sai", 58: 'Renekton', 107: 'Rengar', 92: 'Riven', 68: 'Rumble', 13: 'Ryze', 113: 'Sejuani', 35: 'Shaco', 98: 'Shen', 102: 'Shyvana', 27: 'Singed', 14: 'Sion', 15: 'Sivir', 72: 'Skarner', 37: 'Sona', 16: 'Soraka', 50: 'Swain', 134: 'Syndra', 223: 'Tahm Kench', 163: 'Taliyah', 91: 'Talon', 44: 'Taric', 17: 'Teemo', 412: 'Thresh', 18: 'Tristana', 48: 'Trundle', 23: 'Tryndamere', 4: 'Twisted Fate', 29: 'Twitch', 77: 'Udyr', 6: 'Urgot', 110: 'Varus', 67: 'Vayne', 45: 'Veigar', 161: "Vel'Koz", 254: 'Vi', 112: 'Viktor', 8: 'Vladimir', 106: 'Volibear', 19: 'Warwick', 498: 'Xayah', 101: 'Xerath', 5: 'Xin Zhao', 157: 'Yasuo', 83: 'Yorick', 154: 'Zac', 238: 'Zed', 115: 'Ziggs', 26: 'Zilean', 143: 'Zyra'}
Kokius personažus žaidėjai rinkosi:
player_champ_pick = pd.concat(
[ranked_games['t1_champ1id'], ranked_games['t1_champ2id'], ranked_games['t1_champ3id'],
ranked_games['t1_champ4id'], ranked_games['t1_champ5id'],
ranked_games['t2_champ1id'], ranked_games['t2_champ2id'], ranked_games['t2_champ3id'],
ranked_games['t2_champ4id'], ranked_games['t2_champ5id']])
print(player_champ_pick)
0 Vladimir 1 Draven 2 Tristana 3 Maokai 4 Warwick ... 51485 Gragas 51486 Veigar 51487 Lux 51488 Master Yi 51489 Renekton Length: 514900, dtype: object
player_champ_ban = pd.concat(
[ranked_games['t1_ban1'], ranked_games['t1_ban2'], ranked_games['t1_ban3'],
ranked_games['t1_ban4'], ranked_games['t1_ban5'],
ranked_games['t2_ban1'], ranked_games['t2_ban2'], ranked_games['t2_ban3'],
ranked_games['t2_ban4'],ranked_games['t2_ban5']])
print(player_champ_ban)
0 Riven 1 Caitlyn 2 Lulu 3 Zed 4 Malzahar ... 51485 Yasuo 51486 Fizz 51487 Yasuo 51488 Fiora 51489 Tristana Length: 514900, dtype: object
fig, (table1, table2) = plt.subplots(1, 2, sharey=False, figsize=(15,30))
plt.xticks(rotation=90)
sns.countplot(y=player_champ_pick, ax=table1, order=player_champ_pick.value_counts().index, data=ranked_games )
sns.countplot(y=player_champ_ban, ax=table2, order=player_champ_ban.value_counts().index, data=ranked_games )
table1.set_title('Player champion Picks')
table2.set_title('Player champion Bans')
plt.show()
Dažniausiai pasirenkami personažai yra Tresh, Tristana, Vayne.
Dažniausiai užblokuojami personažai yra Yasou, Zed, Cho'ghat.
Galima teigti jog dažniausia renkamasi ADC (Attack Damage Carry) personažai,blokuojami assasin ir top damage tipo personažai.
Įdomu tai jog didžiausią tikimybė nužudyti ADC tipo personažą turi būtet dažniausiai užblokuojami personažai.
ranked_games.columns
Index(['gameId', 'creationTime', 'gameDuration', 'seasonId', 'winner', 'firstBlood', 'firstTower', 'firstInhibitor', 'firstBaron', 'firstDragon', 'firstRiftHerald', 't1_champ1id', 't1_champ1_sum1', 't1_champ1_sum2', 't1_champ2id', 't1_champ2_sum1', 't1_champ2_sum2', 't1_champ3id', 't1_champ3_sum1', 't1_champ3_sum2', 't1_champ4id', 't1_champ4_sum1', 't1_champ4_sum2', 't1_champ5id', 't1_champ5_sum1', 't1_champ5_sum2', 't1_towerKills', 't1_inhibitorKills', 't1_baronKills', 't1_dragonKills', 't1_riftHeraldKills', 't1_ban1', 't1_ban2', 't1_ban3', 't1_ban4', 't1_ban5', 't2_champ1id', 't2_champ1_sum1', 't2_champ1_sum2', 't2_champ2id', 't2_champ2_sum1', 't2_champ2_sum2', 't2_champ3id', 't2_champ3_sum1', 't2_champ3_sum2', 't2_champ4id', 't2_champ4_sum1', 't2_champ4_sum2', 't2_champ5id', 't2_champ5_sum1', 't2_champ5_sum2', 't2_towerKills', 't2_inhibitorKills', 't2_baronKills', 't2_dragonKills', 't2_riftHeraldKills', 't2_ban1', 't2_ban2', 't2_ban3', 't2_ban4', 't2_ban5'], dtype='object')
ranked_games['t1_champ1_sum1'].dtypes
dtype('int64')
Summoner_spell_columns = ['t1_champ1_sum1', 't1_champ1_sum2', 't1_champ2_sum1', 't1_champ2_sum2',
't1_champ3_sum1', 't1_champ3_sum2', 't1_champ4_sum1', 't1_champ4_sum2',
't1_champ5_sum1', 't1_champ5_sum2',
't2_champ1_sum1', 't2_champ1_sum2', 't2_champ2_sum1', 't2_champ2_sum2',
't2_champ3_sum1', 't2_champ3_sum2', 't2_champ4_sum1', 't2_champ4_sum2',
't2_champ5_sum1','t2_champ5_sum2']
Summoner_spell_columns
['t1_champ1_sum1', 't1_champ1_sum2', 't1_champ2_sum1', 't1_champ2_sum2', 't1_champ3_sum1', 't1_champ3_sum2', 't1_champ4_sum1', 't1_champ4_sum2', 't1_champ5_sum1', 't1_champ5_sum2', 't2_champ1_sum1', 't2_champ1_sum2', 't2_champ2_sum1', 't2_champ2_sum2', 't2_champ3_sum1', 't2_champ3_sum2', 't2_champ4_sum1', 't2_champ4_sum2', 't2_champ5_sum1', 't2_champ5_sum2']
How_many_spells_used = ranked_games[Summoner_spell_columns].apply(pd.value_counts)
How_many_spells_used ['count']= How_many_spells_used[Summoner_spell_columns].sum
How_many_spells_used
t1_champ1_sum1 | t1_champ1_sum2 | t1_champ2_sum1 | t1_champ2_sum2 | t1_champ3_sum1 | t1_champ3_sum2 | t1_champ4_sum1 | t1_champ4_sum2 | t1_champ5_sum1 | t1_champ5_sum2 | ... | t2_champ1_sum2 | t2_champ2_sum1 | t2_champ2_sum2 | t2_champ3_sum1 | t2_champ3_sum2 | t2_champ4_sum1 | t2_champ4_sum2 | t2_champ5_sum1 | t2_champ5_sum2 | count | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 160 | 199 | 151 | 153 | 135 | 168 | 136 | 134 | 125 | 182 | ... | 190 | 129 | 159 | 136 | 182 | 154 | 181 | 149 | 184 | <bound method NDFrame._add_numeric_operations.... |
3 | 3640 | 4394 | 3904 | 4614 | 3928 | 4738 | 3992 | 4723 | 3763 | 4557 | ... | 4459 | 3912 | 4596 | 3928 | 4704 | 3855 | 4717 | 3885 | 4511 | <bound method NDFrame._add_numeric_operations.... |
4 | 28164 | 22216 | 27998 | 22490 | 28019 | 22397 | 27988 | 22393 | 27966 | 22377 | ... | 22204 | 28037 | 22427 | 28170 | 22255 | 28089 | 22322 | 27838 | 22589 | <bound method NDFrame._add_numeric_operations.... |
6 | 744 | 798 | 678 | 706 | 647 | 750 | 664 | 689 | 720 | 800 | ... | 761 | 715 | 706 | 690 | 712 | 672 | 737 | 665 | 711 | <bound method NDFrame._add_numeric_operations.... |
7 | 4581 | 5758 | 4922 | 6309 | 4902 | 6180 | 4971 | 6136 | 4500 | 5826 | ... | 5934 | 4854 | 6197 | 4834 | 6173 | 4896 | 6276 | 4571 | 5746 | <bound method NDFrame._add_numeric_operations.... |
11 | 4711 | 5635 | 4780 | 5520 | 4789 | 5511 | 4681 | 5579 | 4768 | 5550 | ... | 5656 | 4690 | 5558 | 4621 | 5594 | 4894 | 5594 | 4777 | 5563 | <bound method NDFrame._add_numeric_operations.... |
12 | 4968 | 6395 | 4576 | 5954 | 4664 | 6000 | 4651 | 6020 | 5083 | 6291 | ... | 6358 | 4693 | 6085 | 4736 | 6067 | 4581 | 5926 | 5024 | 6350 | <bound method NDFrame._add_numeric_operations.... |
14 | 3820 | 5205 | 3832 | 4930 | 3786 | 4894 | 3777 | 4982 | 3917 | 5093 | ... | 5047 | 3823 | 4875 | 3758 | 4980 | 3715 | 4899 | 3956 | 5032 | <bound method NDFrame._add_numeric_operations.... |
21 | 702 | 890 | 649 | 814 | 620 | 852 | 630 | 834 | 648 | 814 | ... | 881 | 637 | 887 | 617 | 823 | 634 | 838 | 625 | 804 | <bound method NDFrame._add_numeric_operations.... |
9 rows × 21 columns
summ_spell = summoner_spell[['id', 'name']]
spell_dict= dict(zip(summ_spell['id'],summ_spell['name']))
for spell in summoner_spell:
spell = ranked_games[summoner_spell].replace(summoner_spell, inplace=True)
spell_dict
{1: 'Cleanse', 11: 'Smite', 12: 'Teleport', 13: 'Clarity', 14: 'Ignite', 21: 'Barrier', 3: 'Exhaust', 30: 'To the King!', 31: 'Poro Toss', 32: 'Mark', 33: 'Nexus Siege: Siege Weapon Slot', 34: 'Nexus Siege: Siege Weapon Slot', 35: 'Disabled Summoner Spells', 36: 'Disabled Summoner Spells', 4: 'Flash', 6: 'Ghost', 7: 'Heal'}
Pirmas: Abiejų komandų dažniausias 'Spell' yra 'Flash'
Antras: Turime 'Smite' arba 'Teleport'
Duomenų sukėlimas ("League of legends championship 2021")
import mysql.connector
import pandas as pd
mydb = mysql.connector.connect(
host="localhost",
port="3317",
user="root",
password="MRspaikIS899",
)
cursor = mydb.cursor()
cursor.execute('USE lol_worlds_2021')
champions_2021 = pd.read_sql('SELECT * FROM champions', con=mydb)
champions_2021
Champion | Pos | GP | P% | B% | P+B% | W% | CTR% | K | D | ... | GD10 | XPD10 | CSD10 | CSPM | CS%P15 | DPM | DMG% | GOLD% | WPM | WCPM | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | Aatrox | Middle | 1 | 1.2% | 1.2% | 4.8% | 100% | 100% | 2 | 2 | ... | 46 | 467 | -2.0 | 8.8 | 25.1% | 377 | 22.1% | 20.9% | 0.21 | 0.38 |
1 | Aatrox | Top | 2 | 2.4% | 1.2% | 4.8% | 50% | 100% | 6 | 2 | ... | 14 | 184 | -10.0 | 7.9 | 25.0% | 260 | 17.5% | 24.0% | 0.36 | 0.22 |
2 | Alistar | Support | 5 | 6.0% | 0.0% | 6.0% | 40% | 80% | 6 | 19 | ... | -44 | -77 | 2.4 | 0.9 | 1.9% | 121 | 7.1% | 8.4% | 1.96 | 0.34 |
3 | Amumu | Support | 1 | 1.2% | 7.2% | 8.4% | 100% | 0% | 2 | 4 | ... | -39 | 446 | 2.0 | 0.8 | 1.8% | 155 | 6.6% | 8.8% | 1.69 | 0.34 |
4 | Annie | Middle | 2 | 2.4% | 1.2% | 3.6% | 50% | 100% | 6 | 6 | ... | -345 | -457 | -14.0 | 5.6 | 16.4% | 396 | 20.1% | 16.6% | 0.60 | 0.11 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
87 | Yone | Middle | 1 | 1.2% | 0.0% | 1.2% | 0% | 100% | 2 | 3 | ... | 58 | 47 | 1.0 | 8.3 | 24.8% | 245 | 19.7% | 25.5% | 0.20 | 0.40 |
88 | Yuumi | Support | 15 | 18.1% | 78.3% | 96.4% | 53% | 0% | 21 | 21 | ... | 117 | 51 | -8.4 | 0.2 | 1.0% | 323 | 17.6% | 9.6% | 1.51 | 0.17 |
89 | Ziggs | ADC | 9 | 10.8% | 7.2% | 18.1% | 33% | 78% | 21 | 21 | ... | -331 | 0 | -6.8 | 9.1 | 31.3% | 676 | 36.4% | 24.6% | 0.42 | 0.16 |
90 | Zilean | Support | 3 | 3.6% | 0.0% | 3.6% | 67% | 100% | 1 | 4 | ... | 10 | -222 | -2.0 | 1.1 | 2.3% | 60 | 3.6% | 9.2% | 1.83 | 0.43 |
91 | Zoe | Middle | 14 | 16.9% | 9.6% | 26.5% | 64% | 50% | 43 | 22 | ... | 231 | 225 | -1.3 | 7.8 | 22.7% | 501 | 30.2% | 22.3% | 0.40 | 0.22 |
92 rows × 25 columns
import mysql.connector
import pandas as pd
mydb = mysql.connector.connect(
host="localhost",
port="3317",
user="root",
password="MRspaikIS899",
)
cursor = mydb.cursor()
cursor.execute('USE lol_worlds_2021')
players_2021 = pd.read_sql('SELECT * FROM Players', con=mydb)
players_2021
Player | Team | Pos | GP | W% | CTR% | K | D | A | KDA | ... | CSPM | CS%P15 | DPM | DMG% | D%P15 | EGPM | GOLD% | STL | WPM | WCPM | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | Abbedagge | 100 Thieves | Middle | 6 | 50% | 50% | 15 | 16 | 26 | 2.6 | ... | 8.6 | 26.7% | 349 | 22.2% | 23.1% | 242 | 23.2% | 0 | 0.53 | 0.24 |
1 | Adam | Fnatic | Top | 6 | 17% | 50% | 26 | 39 | 30 | 1.4 | ... | 7.6 | 26.4% | 528 | 24.0% | 23.2% | 264 | 23.8% | 0 | 0.32 | 0.22 |
2 | Ale | LNG Esports | Top | 7 | 43% | 86% | 24 | 20 | 29 | 2.7 | ... | 8.7 | 28.6% | 416 | 25.9% | 24.9% | 280 | 25.7% | 0 | 0.33 | 0.22 |
3 | Alphari | Team Liquid | Top | 7 | 43% | 57% | 19 | 17 | 22 | 2.4 | ... | 8.6 | 26.9% | 394 | 24.6% | 21.2% | 266 | 24.8% | 0 | 0.48 | 0.15 |
4 | Aria | DetonatioN FocusMe | Middle | 6 | 0% | 50% | 14 | 13 | 20 | 2.6 | ... | 7.8 | 26.0% | 395 | 28.7% | 26.4% | 226 | 24.8% | 0 | 0.40 | 0.21 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
76 | Wei | Royal Never Give Up | Jungle | 12 | 58% | 42% | 53 | 41 | 95 | 3.6 | ... | 4.8 | 13.9% | 377 | 19.7% | 19.0% | 214 | 18.5% | 1 | 0.50 | 0.48 |
77 | Willer | Hanwha Life Esports | Jungle | 10 | 40% | 10% | 25 | 28 | 64 | 3.2 | ... | 5.2 | 14.7% | 221 | 12.3% | 12.9% | 181 | 16.6% | 3 | 0.52 | 0.38 |
78 | Xiaohu | Royal Never Give Up | Top | 12 | 58% | 58% | 46 | 33 | 78 | 3.8 | ... | 8.8 | 30.6% | 505 | 25.7% | 24.6% | 294 | 25.7% | 0 | 0.38 | 0.24 |
79 | Yutapon | DetonatioN FocusMe | ADC | 6 | 0% | 67% | 12 | 16 | 12 | 1.5 | ... | 9.1 | 33.4% | 329 | 21.6% | 25.6% | 249 | 27.1% | 0 | 0.26 | 0.31 |
80 | Zven | Cloud9 | ADC | 10 | 30% | 60% | 24 | 29 | 45 | 2.4 | ... | 9.4 | 29.5% | 378 | 24.5% | 26.6% | 275 | 24.9% | 0 | 0.43 | 0.35 |
81 rows × 27 columns
champions_2021.sort_values('GP', ascending = False).head(10)
Champion | Pos | GP | P% | B% | P+B% | W% | CTR% | K | D | ... | GD10 | XPD10 | CSD10 | CSPM | CS%P15 | DPM | DMG% | GOLD% | WPM | WCPM | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
46 | Miss Fortune | ADC | 51 | 61.4% | 12.0% | 73.5% | 43% | 35% | 179 | 110 | ... | -17 | 37 | 0.5 | 9.3 | 31.3% | 429 | 24.7% | 26.6% | 0.45 | 0.40 |
36 | Lee Sin | Jungle | 37 | 44.6% | 54.2% | 98.8% | 59% | 0% | 117 | 90 | ... | -42 | 14 | -1.3 | 5.4 | 15.4% | 256 | 14.1% | 17.9% | 0.61 | 0.36 |
21 | Graves | Top | 33 | 39.8% | 34.9% | 78.3% | 64% | 9% | 102 | 76 | ... | 33 | 39 | 2.6 | 9.5 | 30.6% | 487 | 26.9% | 26.0% | 0.35 | 0.28 |
37 | Leona | Support | 32 | 38.6% | 26.5% | 65.1% | 34% | 34% | 22 | 103 | ... | -34 | -8 | 1.1 | 1.1 | 2.9% | 94 | 5.9% | 8.3% | 1.66 | 0.44 |
5 | Aphelios | ADC | 31 | 37.3% | 41.0% | 78.3% | 61% | 48% | 115 | 63 | ... | 128 | -47 | 4.2 | 9.3 | 31.2% | 444 | 25.7% | 26.4% | 0.48 | 0.32 |
84 | Xin Zhao | Jungle | 30 | 36.1% | 30.1% | 66.3% | 33% | 63% | 57 | 106 | ... | -125 | -145 | -2.2 | 5.0 | 13.5% | 276 | 16.3% | 16.6% | 0.38 | 0.45 |
56 | Rakan | Support | 29 | 34.9% | 19.3% | 54.2% | 62% | 28% | 24 | 75 | ... | -52 | -81 | 0.3 | 1.1 | 3.1% | 130 | 7.1% | 9.1% | 2.15 | 0.51 |
29 | Jhin | ADC | 28 | 33.7% | 2.4% | 36.1% | 64% | 68% | 93 | 31 | ... | -126 | -72 | -4.8 | 8.9 | 28.7% | 384 | 21.3% | 24.4% | 0.45 | 0.25 |
25 | Jarvan IV | Jungle | 28 | 33.7% | 39.8% | 77.1% | 50% | 54% | 58 | 87 | ... | 16 | -74 | -0.8 | 4.9 | 12.4% | 235 | 13.6% | 16.3% | 0.42 | 0.51 |
61 | Ryze | Middle | 24 | 28.9% | 38.6% | 67.5% | 46% | 29% | 71 | 62 | ... | 17 | 40 | 1.2 | 8.9 | 27.0% | 349 | 21.4% | 24.4% | 0.45 | 0.18 |
10 rows × 25 columns
World champion metu žaidėjai daugiausia rinkosi ADC/ Jungle rolės personažus.
Champ_role = champions_2021.groupby('Pos')[['Champion']].count().sort_values(by = 'Champion', ascending = False)
print(Champ_role)
Champion Pos Top 24 Middle 21 Support 17 Jungle 16 ADC 14
c = champions_2021.groupby('Pos')
c
<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000001BDA7785DF0>
for x in c:
print(x)
('ADC', Champion Pos GP P% B% P+B% W% CTR% K D ... \ 5 Aphelios ADC 31 37.3% 41.0% 78.3% 61% 48% 115 63 ... 6 Ashe ADC 1 1.2% 0.0% 1.2% 0% 0% 2 6 ... 10 Draven ADC 4 4.8% 21.7% 26.5% 25% 50% 11 8 ... 11 Ezreal ADC 11 13.3% 9.6% 22.9% 55% 82% 42 23 ... 29 Jhin ADC 28 33.7% 2.4% 36.1% 64% 68% 93 31 ... 30 Jinx ADC 1 1.2% 3.6% 4.8% 0% 100% 0 3 ... 31 Kai'Sa ADC 8 9.6% 2.4% 12.0% 38% 63% 32 18 ... 32 Kalista ADC 1 1.2% 1.2% 2.4% 0% 100% 6 4 ... 40 Lucian ADC 15 18.1% 54.2% 78.3% 60% 7% 58 41 ... 46 Miss Fortune ADC 51 61.4% 12.0% 73.5% 43% 35% 179 110 ... 74 Tristana ADC 3 3.6% 1.2% 4.8% 67% 67% 9 5 ... 79 Varus ADC 2 2.4% 1.2% 3.6% 0% 100% 1 6 ... 83 Xayah ADC 1 1.2% 1.2% 2.4% 0% 100% 0 5 ... 89 Ziggs ADC 9 10.8% 7.2% 18.1% 33% 78% 21 21 ... GD10 XPD10 CSD10 CSPM CS%P15 DPM DMG% GOLD% WPM WCPM 5 128 -47 4.2 9.3 31.2% 444 25.7% 26.4% 0.48 0.32 6 1138 555 15.0 7.3 27.3% 409 23.5% 20.9% 0.36 0.32 10 166 50 4.0 9.2 30.9% 317 20.5% 27.9% 0.64 0.33 11 -62 -158 -6.5 8.9 29.0% 569 30.8% 25.2% 0.42 0.21 29 -126 -72 -4.8 8.9 28.7% 384 21.3% 24.4% 0.45 0.25 30 2 -480 -7.0 10.1 35.0% 376 24.0% 26.1% 0.49 0.46 31 -4 52 4.0 9.2 31.8% 432 24.1% 27.0% 0.57 0.32 32 109 -285 3.0 8.4 28.6% 509 23.4% 28.6% 0.96 0.38 40 196 206 4.6 9.6 31.9% 507 27.6% 26.3% 0.47 0.48 46 -17 37 0.5 9.3 31.3% 429 24.7% 26.6% 0.45 0.40 74 -50 65 -5.7 9.6 32.7% 325 19.1% 24.9% 0.47 0.28 79 -420 -165 -6.0 8.4 28.0% 600 35.9% 23.8% 0.67 0.25 83 261 0 11.0 8.4 27.7% 240 11.1% 19.6% 0.30 0.16 89 -331 0 -6.8 9.1 31.3% 676 36.4% 24.6% 0.42 0.16 [14 rows x 25 columns]) ('Jungle', Champion Pos GP P% B% P+B% W% CTR% K D ... \ 12 Fiddlesticks Jungle 2 2.4% 2.4% 4.8% 50% 100% 8 9 ... 17 Gragas Jungle 1 1.2% 1.2% 8.4% 0% 100% 2 7 ... 20 Graves Jungle 3 3.6% 34.9% 78.3% 0% 100% 4 12 ... 25 Jarvan IV Jungle 28 33.7% 39.8% 77.1% 50% 54% 58 87 ... 36 Lee Sin Jungle 37 44.6% 54.2% 98.8% 59% 0% 117 90 ... 38 Lillia Jungle 2 2.4% 0.0% 2.4% 50% 100% 7 8 ... 49 Olaf Jungle 6 7.2% 14.5% 24.1% 50% 83% 18 16 ... 52 Poppy Jungle 8 9.6% 6.0% 18.1% 63% 88% 14 15 ... 55 Qiyana Jungle 10 12.0% 10.8% 22.9% 70% 70% 43 25 ... 62 Sejuani Jungle 1 1.2% 0.0% 1.2% 0% 0% 4 3 ... 66 Shaco Jungle 1 1.2% 0.0% 1.2% 0% 0% 0 4 ... 71 Taliyah Jungle 1 1.2% 3.6% 4.8% 0% 100% 2 5 ... 72 Talon Jungle 12 14.5% 13.3% 27.7% 58% 58% 41 25 ... 75 Trundle Jungle 6 7.2% 6.0% 13.3% 50% 83% 11 14 ... 80 Viego Jungle 18 21.7% 8.4% 30.1% 56% 50% 64 38 ... 84 Xin Zhao Jungle 30 36.1% 30.1% 66.3% 33% 63% 57 106 ... GD10 XPD10 CSD10 CSPM CS%P15 DPM DMG% GOLD% WPM WCPM 12 -94 -476 -5.5 5.0 13.0% 341 16.9% 16.7% 0.10 0.16 17 -653 -73 -7.0 4.2 13.6% 144 7.1% 13.9% 0.71 0.33 20 -36 111 10.0 7.2 22.1% 314 19.5% 20.8% 0.33 0.57 25 16 -74 -0.8 4.9 12.4% 235 13.6% 16.3% 0.42 0.51 36 -42 14 -1.3 5.4 15.4% 256 14.1% 17.9% 0.61 0.36 38 77 84 7.0 6.7 13.3% 432 28.8% 23.5% 0.46 0.43 49 257 339 4.5 5.2 14.6% 365 16.8% 17.1% 0.37 0.45 52 19 45 2.5 5.7 16.4% 245 14.4% 17.0% 0.32 0.45 55 63 -72 -0.2 6.2 18.7% 363 19.1% 18.8% 0.67 0.52 62 -328 410 15.0 4.4 10.7% 326 15.4% 15.9% 0.44 0.42 66 -621 49 -12.0 4.6 13.2% 141 11.2% 14.7% 1.11 0.67 71 847 313 4.0 5.4 14.2% 299 24.1% 16.8% 1.37 0.60 72 154 213 3.9 6.0 17.9% 338 18.6% 19.4% 0.35 0.36 75 -76 -183 -8.0 4.5 12.6% 174 9.7% 16.0% 0.65 0.53 80 113 140 3.2 5.6 16.1% 286 14.5% 18.0% 0.36 0.49 84 -125 -145 -2.2 5.0 13.5% 276 16.3% 16.6% 0.38 0.45 [16 rows x 25 columns]) ('Middle', Champion Pos GP P% B% P+B% W% CTR% K D ... \ 0 Aatrox Middle 1 1.2% 1.2% 4.8% 100% 100% 2 2 ... 4 Annie Middle 2 2.4% 1.2% 3.6% 50% 100% 6 6 ... 7 Azir Middle 10 12.0% 14.5% 26.5% 40% 60% 34 34 ... 14 Galio Middle 3 3.6% 9.6% 13.3% 33% 67% 8 10 ... 23 Irelia Middle 4 4.8% 54.2% 65.1% 25% 50% 12 17 ... 33 Kassadin Middle 1 1.2% 1.2% 2.4% 100% 100% 8 2 ... 35 LeBlanc Middle 23 27.7% 61.4% 89.2% 57% 30% 95 44 ... 39 Lissandra Middle 5 6.0% 3.6% 9.6% 80% 100% 8 8 ... 44 Malzahar Middle 3 3.6% 2.4% 6.0% 67% 100% 10 6 ... 51 Orianna Middle 12 14.5% 3.6% 18.1% 33% 67% 23 25 ... 59 Rumble Middle 1 1.2% 1.2% 4.8% 0% 100% 1 3 ... 61 Ryze Middle 24 28.9% 38.6% 67.5% 46% 29% 71 62 ... 63 Seraphine Middle 1 1.2% 0.0% 1.2% 0% 100% 2 1 ... 68 Sylas Middle 16 19.3% 9.6% 28.9% 50% 88% 63 54 ... 69 Syndra Middle 14 16.9% 10.8% 28.9% 64% 57% 60 29 ... 76 Tryndamere Middle 2 2.4% 7.2% 10.8% 0% 50% 9 7 ... 78 Twisted Fate Middle 24 28.9% 67.5% 96.4% 54% 8% 47 44 ... 81 Viktor Middle 3 3.6% 0.0% 3.6% 0% 100% 13 12 ... 85 Yasuo Middle 2 2.4% 1.2% 6.0% 50% 50% 11 5 ... 87 Yone Middle 1 1.2% 0.0% 1.2% 0% 100% 2 3 ... 91 Zoe Middle 14 16.9% 9.6% 26.5% 64% 50% 43 22 ... GD10 XPD10 CSD10 CSPM CS%P15 DPM DMG% GOLD% WPM WCPM 0 46 467 -2.0 8.8 25.1% 377 22.1% 20.9% 0.21 0.38 4 -345 -457 -14.0 5.6 16.4% 396 20.1% 16.6% 0.60 0.11 7 37 188 9.0 8.4 25.1% 523 30.0% 22.9% 0.48 0.21 14 -767 -783 -28.7 6.1 18.3% 316 20.0% 19.6% 0.54 0.14 23 -418 -55 4.0 9.2 26.8% 294 18.3% 26.7% 0.41 0.17 33 -209 -226 0.0 8.2 24.2% 842 43.6% 25.2% 0.41 0.49 35 57 231 6.4 8.3 24.2% 573 30.2% 24.1% 0.57 0.37 39 -154 -231 -5.2 8.5 23.7% 388 21.6% 21.2% 0.23 0.36 44 -391 -157 -7.7 8.6 27.1% 405 21.1% 24.5% 0.30 0.32 51 -175 -109 -3.7 8.8 26.4% 385 25.8% 23.7% 0.44 0.21 59 -760 -102 -8.0 8.3 24.8% 394 32.4% 25.6% 0.53 0.21 61 17 40 1.2 8.9 27.0% 349 21.4% 24.4% 0.45 0.18 63 226 447 0.0 8.1 26.5% 326 22.1% 25.7% 0.64 0.30 68 -467 -115 -5.5 8.1 24.9% 404 25.0% 23.8% 0.34 0.21 69 101 -259 0.4 8.3 24.9% 513 26.3% 23.0% 0.46 0.21 76 -459 237 4.0 9.7 30.7% 437 28.4% 31.4% 0.41 0.34 78 455 -44 0.2 8.0 23.8% 410 21.5% 23.9% 0.34 0.24 81 -101 322 4.3 8.8 27.6% 727 34.8% 26.6% 0.40 0.20 85 184 -220 4.5 9.6 30.3% 316 16.6% 26.4% 0.33 0.38 87 58 47 1.0 8.3 24.8% 245 19.7% 25.5% 0.20 0.40 91 231 225 -1.3 7.8 22.7% 501 30.2% 22.3% 0.40 0.22 [21 rows x 25 columns]) ('Support', Champion Pos GP P% B% P+B% W% CTR% K D ... \ 2 Alistar Support 5 6.0% 0.0% 6.0% 40% 80% 6 19 ... 3 Amumu Support 1 1.2% 7.2% 8.4% 100% 0% 2 4 ... 8 Braum Support 11 13.3% 9.6% 22.9% 36% 100% 4 38 ... 18 Gragas Support 1 1.2% 1.2% 8.4% 0% 100% 1 5 ... 37 Leona Support 32 38.6% 26.5% 65.1% 34% 34% 22 103 ... 42 Lulu Support 16 19.3% 3.6% 22.9% 56% 75% 12 34 ... 45 Maokai Support 2 2.4% 0.0% 2.4% 50% 50% 2 7 ... 47 Nami Support 13 15.7% 24.1% 39.8% 54% 38% 11 29 ... 48 Nautilus Support 10 12.0% 4.8% 16.9% 50% 90% 4 47 ... 54 Pyke Support 3 3.6% 0.0% 3.6% 33% 67% 13 15 ... 56 Rakan Support 29 34.9% 19.3% 54.2% 62% 28% 24 75 ... 57 Rell Support 9 10.8% 3.6% 14.5% 56% 78% 9 32 ... 64 Sett Support 1 1.2% 7.2% 10.8% 100% 100% 2 5 ... 67 Shen Support 3 3.6% 0.0% 3.6% 33% 100% 4 9 ... 73 Thresh Support 12 14.5% 21.7% 36.1% 58% 42% 5 26 ... 88 Yuumi Support 15 18.1% 78.3% 96.4% 53% 0% 21 21 ... 90 Zilean Support 3 3.6% 0.0% 3.6% 67% 100% 1 4 ... GD10 XPD10 CSD10 CSPM CS%P15 DPM DMG% GOLD% WPM WCPM 2 -44 -77 2.4 0.9 1.9% 121 7.1% 8.4% 1.96 0.34 3 -39 446 2.0 0.8 1.8% 155 6.6% 8.8% 1.69 0.34 8 -47 -75 2.5 1.1 2.3% 156 9.9% 8.5% 1.67 0.30 18 28 433 1.0 0.8 1.6% 168 9.5% 6.9% 1.64 0.21 37 -34 -8 1.1 1.1 2.9% 94 5.9% 8.3% 1.66 0.44 42 156 80 -3.2 0.5 1.8% 105 6.7% 9.2% 1.57 0.37 45 48 291 3.0 1.5 5.1% 393 19.6% 9.7% 1.61 0.46 47 91 70 -2.9 0.5 1.9% 184 10.5% 9.0% 2.07 0.39 48 -194 -63 8.0 1.0 1.9% 118 6.4% 7.9% 1.59 0.30 54 317 331 4.7 1.3 4.4% 205 10.3% 14.1% 2.34 0.51 56 -52 -81 0.3 1.1 3.1% 130 7.1% 9.1% 2.15 0.51 57 -26 -141 3.7 1.1 2.4% 134 7.0% 8.6% 1.60 0.37 64 -590 -539 4.0 1.4 4.2% 234 9.0% 8.8% 1.78 0.59 67 76 289 -0.7 1.2 3.1% 112 5.8% 9.6% 2.40 0.49 73 -54 52 -0.1 1.0 1.9% 101 5.4% 8.5% 1.64 0.28 88 117 51 -8.4 0.2 1.0% 323 17.6% 9.6% 1.51 0.17 90 10 -222 -2.0 1.1 2.3% 60 3.6% 9.2% 1.83 0.43 [17 rows x 25 columns]) ('Top', Champion Pos GP P% B% P+B% W% CTR% K D ... GD10 \ 1 Aatrox Top 2 2.4% 1.2% 4.8% 50% 100% 6 2 ... 14 9 Camille Top 7 8.4% 10.8% 19.3% 57% 43% 16 30 ... -346 13 Fiora Top 2 2.4% 0.0% 2.4% 100% 100% 10 2 ... 564 15 Gangplank Top 2 2.4% 3.6% 6.0% 100% 50% 10 7 ... 660 16 Gnar Top 8 9.6% 2.4% 12.0% 38% 75% 17 20 ... -139 19 Gragas Top 4 4.8% 1.2% 8.4% 50% 75% 13 13 ... -267 21 Graves Top 33 39.8% 34.9% 78.3% 64% 9% 102 76 ... 33 22 Gwen Top 14 16.9% 10.8% 27.7% 29% 50% 36 34 ... -28 24 Irelia Top 5 6.0% 54.2% 65.1% 20% 80% 7 25 ... -472 26 Jarvan IV Top 3 3.6% 39.8% 77.1% 0% 100% 10 9 ... 262 27 Jax Top 7 8.4% 0.0% 8.4% 43% 86% 18 23 ... -1 28 Jayce Top 23 27.7% 27.7% 55.4% 43% 22% 73 83 ... 378 34 Kennen Top 22 26.5% 34.9% 61.4% 73% 50% 94 74 ... -201 41 Lucian Top 5 6.0% 54.2% 78.3% 60% 80% 21 16 ... 626 43 Malphite Top 2 2.4% 3.6% 6.0% 0% 100% 0 6 ... -770 50 Olaf Top 2 2.4% 14.5% 24.1% 0% 100% 11 10 ... -285 53 Poppy Top 2 2.4% 6.0% 18.1% 0% 100% 1 7 ... -305 58 Renekton Top 12 14.5% 4.8% 19.3% 50% 58% 19 30 ... 72 60 Rumble Top 2 2.4% 1.2% 4.8% 50% 50% 2 7 ... -152 65 Sett Top 2 2.4% 7.2% 10.8% 0% 100% 3 10 ... -389 70 Syndra Top 1 1.2% 10.8% 28.9% 100% 100% 3 1 ... 1218 77 Tryndamere Top 1 1.2% 7.2% 10.8% 0% 100% 6 6 ... -432 82 Wukong Top 3 3.6% 4.8% 8.4% 67% 100% 14 14 ... -593 86 Yasuo Top 2 2.4% 1.2% 6.0% 50% 100% 2 5 ... -232 XPD10 CSD10 CSPM CS%P15 DPM DMG% GOLD% WPM WCPM 1 184 -10.0 7.9 25.0% 260 17.5% 24.0% 0.36 0.22 9 -397 -19.3 7.6 24.7% 340 19.7% 21.2% 0.40 0.23 13 847 16.5 10.1 32.5% 389 21.3% 25.6% 0.31 0.22 15 701 19.5 8.2 29.3% 714 31.6% 27.9% 0.39 0.21 16 -24 3.0 7.9 24.1% 398 24.9% 23.0% 0.48 0.25 19 -213 -8.8 7.6 26.2% 486 27.4% 21.7% 0.35 0.20 21 39 2.6 9.5 30.6% 487 26.9% 26.0% 0.35 0.28 22 -3 1.7 8.7 27.6% 415 26.1% 24.9% 0.33 0.32 24 -364 -4.2 8.4 29.2% 264 18.3% 24.6% 0.38 0.17 26 272 0.0 7.5 22.4% 299 18.7% 22.2% 0.44 0.26 27 130 0.7 8.2 25.9% 345 20.5% 24.3% 0.39 0.16 28 157 9.3 8.6 27.9% 626 34.2% 25.6% 0.41 0.29 34 -122 -4.5 7.5 24.2% 510 27.7% 21.9% 0.33 0.23 41 367 13.2 8.7 28.9% 591 27.7% 25.7% 0.54 0.33 43 -288 -20.0 7.2 23.6% 201 23.5% 19.3% 0.44 0.15 50 -163 -5.0 7.2 26.0% 493 23.4% 23.6% 0.36 0.18 53 -351 -13.5 6.7 20.5% 250 20.8% 19.6% 0.28 0.23 58 58 0.0 8.6 27.7% 324 19.6% 23.8% 0.37 0.26 60 111 -6.0 7.6 21.8% 426 25.0% 19.3% 0.37 0.11 65 -382 -15.5 6.4 24.1% 258 19.1% 20.3% 0.32 0.26 70 302 24.0 8.4 26.6% 552 29.0% 22.3% 0.51 0.19 77 -797 -26.0 7.1 27.1% 693 29.2% 24.2% 0.28 0.34 82 -528 -14.0 7.1 22.5% 406 18.2% 22.5% 0.36 0.35 86 -9 -9.5 9.2 29.7% 212 12.1% 24.9% 0.40 0.19 [24 rows x 25 columns])
c.get_group('Top')
Champion | Pos | GP | P% | B% | P+B% | W% | CTR% | K | D | ... | GD10 | XPD10 | CSD10 | CSPM | CS%P15 | DPM | DMG% | GOLD% | WPM | WCPM | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | Aatrox | Top | 2 | 2.4% | 1.2% | 4.8% | 50% | 100% | 6 | 2 | ... | 14 | 184 | -10.0 | 7.9 | 25.0% | 260 | 17.5% | 24.0% | 0.36 | 0.22 |
9 | Camille | Top | 7 | 8.4% | 10.8% | 19.3% | 57% | 43% | 16 | 30 | ... | -346 | -397 | -19.3 | 7.6 | 24.7% | 340 | 19.7% | 21.2% | 0.40 | 0.23 |
13 | Fiora | Top | 2 | 2.4% | 0.0% | 2.4% | 100% | 100% | 10 | 2 | ... | 564 | 847 | 16.5 | 10.1 | 32.5% | 389 | 21.3% | 25.6% | 0.31 | 0.22 |
15 | Gangplank | Top | 2 | 2.4% | 3.6% | 6.0% | 100% | 50% | 10 | 7 | ... | 660 | 701 | 19.5 | 8.2 | 29.3% | 714 | 31.6% | 27.9% | 0.39 | 0.21 |
16 | Gnar | Top | 8 | 9.6% | 2.4% | 12.0% | 38% | 75% | 17 | 20 | ... | -139 | -24 | 3.0 | 7.9 | 24.1% | 398 | 24.9% | 23.0% | 0.48 | 0.25 |
19 | Gragas | Top | 4 | 4.8% | 1.2% | 8.4% | 50% | 75% | 13 | 13 | ... | -267 | -213 | -8.8 | 7.6 | 26.2% | 486 | 27.4% | 21.7% | 0.35 | 0.20 |
21 | Graves | Top | 33 | 39.8% | 34.9% | 78.3% | 64% | 9% | 102 | 76 | ... | 33 | 39 | 2.6 | 9.5 | 30.6% | 487 | 26.9% | 26.0% | 0.35 | 0.28 |
22 | Gwen | Top | 14 | 16.9% | 10.8% | 27.7% | 29% | 50% | 36 | 34 | ... | -28 | -3 | 1.7 | 8.7 | 27.6% | 415 | 26.1% | 24.9% | 0.33 | 0.32 |
24 | Irelia | Top | 5 | 6.0% | 54.2% | 65.1% | 20% | 80% | 7 | 25 | ... | -472 | -364 | -4.2 | 8.4 | 29.2% | 264 | 18.3% | 24.6% | 0.38 | 0.17 |
26 | Jarvan IV | Top | 3 | 3.6% | 39.8% | 77.1% | 0% | 100% | 10 | 9 | ... | 262 | 272 | 0.0 | 7.5 | 22.4% | 299 | 18.7% | 22.2% | 0.44 | 0.26 |
27 | Jax | Top | 7 | 8.4% | 0.0% | 8.4% | 43% | 86% | 18 | 23 | ... | -1 | 130 | 0.7 | 8.2 | 25.9% | 345 | 20.5% | 24.3% | 0.39 | 0.16 |
28 | Jayce | Top | 23 | 27.7% | 27.7% | 55.4% | 43% | 22% | 73 | 83 | ... | 378 | 157 | 9.3 | 8.6 | 27.9% | 626 | 34.2% | 25.6% | 0.41 | 0.29 |
34 | Kennen | Top | 22 | 26.5% | 34.9% | 61.4% | 73% | 50% | 94 | 74 | ... | -201 | -122 | -4.5 | 7.5 | 24.2% | 510 | 27.7% | 21.9% | 0.33 | 0.23 |
41 | Lucian | Top | 5 | 6.0% | 54.2% | 78.3% | 60% | 80% | 21 | 16 | ... | 626 | 367 | 13.2 | 8.7 | 28.9% | 591 | 27.7% | 25.7% | 0.54 | 0.33 |
43 | Malphite | Top | 2 | 2.4% | 3.6% | 6.0% | 0% | 100% | 0 | 6 | ... | -770 | -288 | -20.0 | 7.2 | 23.6% | 201 | 23.5% | 19.3% | 0.44 | 0.15 |
50 | Olaf | Top | 2 | 2.4% | 14.5% | 24.1% | 0% | 100% | 11 | 10 | ... | -285 | -163 | -5.0 | 7.2 | 26.0% | 493 | 23.4% | 23.6% | 0.36 | 0.18 |
53 | Poppy | Top | 2 | 2.4% | 6.0% | 18.1% | 0% | 100% | 1 | 7 | ... | -305 | -351 | -13.5 | 6.7 | 20.5% | 250 | 20.8% | 19.6% | 0.28 | 0.23 |
58 | Renekton | Top | 12 | 14.5% | 4.8% | 19.3% | 50% | 58% | 19 | 30 | ... | 72 | 58 | 0.0 | 8.6 | 27.7% | 324 | 19.6% | 23.8% | 0.37 | 0.26 |
60 | Rumble | Top | 2 | 2.4% | 1.2% | 4.8% | 50% | 50% | 2 | 7 | ... | -152 | 111 | -6.0 | 7.6 | 21.8% | 426 | 25.0% | 19.3% | 0.37 | 0.11 |
65 | Sett | Top | 2 | 2.4% | 7.2% | 10.8% | 0% | 100% | 3 | 10 | ... | -389 | -382 | -15.5 | 6.4 | 24.1% | 258 | 19.1% | 20.3% | 0.32 | 0.26 |
70 | Syndra | Top | 1 | 1.2% | 10.8% | 28.9% | 100% | 100% | 3 | 1 | ... | 1218 | 302 | 24.0 | 8.4 | 26.6% | 552 | 29.0% | 22.3% | 0.51 | 0.19 |
77 | Tryndamere | Top | 1 | 1.2% | 7.2% | 10.8% | 0% | 100% | 6 | 6 | ... | -432 | -797 | -26.0 | 7.1 | 27.1% | 693 | 29.2% | 24.2% | 0.28 | 0.34 |
82 | Wukong | Top | 3 | 3.6% | 4.8% | 8.4% | 67% | 100% | 14 | 14 | ... | -593 | -528 | -14.0 | 7.1 | 22.5% | 406 | 18.2% | 22.5% | 0.36 | 0.35 |
86 | Yasuo | Top | 2 | 2.4% | 1.2% | 6.0% | 50% | 100% | 2 | 5 | ... | -232 | -9 | -9.5 | 9.2 | 29.7% | 212 | 12.1% | 24.9% | 0.40 | 0.19 |
24 rows × 25 columns
c.get_group('ADC')
Champion | Pos | GP | P% | B% | P+B% | W% | CTR% | K | D | ... | GD10 | XPD10 | CSD10 | CSPM | CS%P15 | DPM | DMG% | GOLD% | WPM | WCPM | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
5 | Aphelios | ADC | 31 | 37.3% | 41.0% | 78.3% | 61% | 48% | 115 | 63 | ... | 128 | -47 | 4.2 | 9.3 | 31.2% | 444 | 25.7% | 26.4% | 0.48 | 0.32 |
6 | Ashe | ADC | 1 | 1.2% | 0.0% | 1.2% | 0% | 0% | 2 | 6 | ... | 1138 | 555 | 15.0 | 7.3 | 27.3% | 409 | 23.5% | 20.9% | 0.36 | 0.32 |
10 | Draven | ADC | 4 | 4.8% | 21.7% | 26.5% | 25% | 50% | 11 | 8 | ... | 166 | 50 | 4.0 | 9.2 | 30.9% | 317 | 20.5% | 27.9% | 0.64 | 0.33 |
11 | Ezreal | ADC | 11 | 13.3% | 9.6% | 22.9% | 55% | 82% | 42 | 23 | ... | -62 | -158 | -6.5 | 8.9 | 29.0% | 569 | 30.8% | 25.2% | 0.42 | 0.21 |
29 | Jhin | ADC | 28 | 33.7% | 2.4% | 36.1% | 64% | 68% | 93 | 31 | ... | -126 | -72 | -4.8 | 8.9 | 28.7% | 384 | 21.3% | 24.4% | 0.45 | 0.25 |
30 | Jinx | ADC | 1 | 1.2% | 3.6% | 4.8% | 0% | 100% | 0 | 3 | ... | 2 | -480 | -7.0 | 10.1 | 35.0% | 376 | 24.0% | 26.1% | 0.49 | 0.46 |
31 | Kai'Sa | ADC | 8 | 9.6% | 2.4% | 12.0% | 38% | 63% | 32 | 18 | ... | -4 | 52 | 4.0 | 9.2 | 31.8% | 432 | 24.1% | 27.0% | 0.57 | 0.32 |
32 | Kalista | ADC | 1 | 1.2% | 1.2% | 2.4% | 0% | 100% | 6 | 4 | ... | 109 | -285 | 3.0 | 8.4 | 28.6% | 509 | 23.4% | 28.6% | 0.96 | 0.38 |
40 | Lucian | ADC | 15 | 18.1% | 54.2% | 78.3% | 60% | 7% | 58 | 41 | ... | 196 | 206 | 4.6 | 9.6 | 31.9% | 507 | 27.6% | 26.3% | 0.47 | 0.48 |
46 | Miss Fortune | ADC | 51 | 61.4% | 12.0% | 73.5% | 43% | 35% | 179 | 110 | ... | -17 | 37 | 0.5 | 9.3 | 31.3% | 429 | 24.7% | 26.6% | 0.45 | 0.40 |
74 | Tristana | ADC | 3 | 3.6% | 1.2% | 4.8% | 67% | 67% | 9 | 5 | ... | -50 | 65 | -5.7 | 9.6 | 32.7% | 325 | 19.1% | 24.9% | 0.47 | 0.28 |
79 | Varus | ADC | 2 | 2.4% | 1.2% | 3.6% | 0% | 100% | 1 | 6 | ... | -420 | -165 | -6.0 | 8.4 | 28.0% | 600 | 35.9% | 23.8% | 0.67 | 0.25 |
83 | Xayah | ADC | 1 | 1.2% | 1.2% | 2.4% | 0% | 100% | 0 | 5 | ... | 261 | 0 | 11.0 | 8.4 | 27.7% | 240 | 11.1% | 19.6% | 0.30 | 0.16 |
89 | Ziggs | ADC | 9 | 10.8% | 7.2% | 18.1% | 33% | 78% | 21 | 21 | ... | -331 | 0 | -6.8 | 9.1 | 31.3% | 676 | 36.4% | 24.6% | 0.42 | 0.16 |
14 rows × 25 columns
c.get_group('Middle')
Champion | Pos | GP | P% | B% | P+B% | W% | CTR% | K | D | ... | GD10 | XPD10 | CSD10 | CSPM | CS%P15 | DPM | DMG% | GOLD% | WPM | WCPM | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | Aatrox | Middle | 1 | 1.2% | 1.2% | 4.8% | 100% | 100% | 2 | 2 | ... | 46 | 467 | -2.0 | 8.8 | 25.1% | 377 | 22.1% | 20.9% | 0.21 | 0.38 |
4 | Annie | Middle | 2 | 2.4% | 1.2% | 3.6% | 50% | 100% | 6 | 6 | ... | -345 | -457 | -14.0 | 5.6 | 16.4% | 396 | 20.1% | 16.6% | 0.60 | 0.11 |
7 | Azir | Middle | 10 | 12.0% | 14.5% | 26.5% | 40% | 60% | 34 | 34 | ... | 37 | 188 | 9.0 | 8.4 | 25.1% | 523 | 30.0% | 22.9% | 0.48 | 0.21 |
14 | Galio | Middle | 3 | 3.6% | 9.6% | 13.3% | 33% | 67% | 8 | 10 | ... | -767 | -783 | -28.7 | 6.1 | 18.3% | 316 | 20.0% | 19.6% | 0.54 | 0.14 |
23 | Irelia | Middle | 4 | 4.8% | 54.2% | 65.1% | 25% | 50% | 12 | 17 | ... | -418 | -55 | 4.0 | 9.2 | 26.8% | 294 | 18.3% | 26.7% | 0.41 | 0.17 |
33 | Kassadin | Middle | 1 | 1.2% | 1.2% | 2.4% | 100% | 100% | 8 | 2 | ... | -209 | -226 | 0.0 | 8.2 | 24.2% | 842 | 43.6% | 25.2% | 0.41 | 0.49 |
35 | LeBlanc | Middle | 23 | 27.7% | 61.4% | 89.2% | 57% | 30% | 95 | 44 | ... | 57 | 231 | 6.4 | 8.3 | 24.2% | 573 | 30.2% | 24.1% | 0.57 | 0.37 |
39 | Lissandra | Middle | 5 | 6.0% | 3.6% | 9.6% | 80% | 100% | 8 | 8 | ... | -154 | -231 | -5.2 | 8.5 | 23.7% | 388 | 21.6% | 21.2% | 0.23 | 0.36 |
44 | Malzahar | Middle | 3 | 3.6% | 2.4% | 6.0% | 67% | 100% | 10 | 6 | ... | -391 | -157 | -7.7 | 8.6 | 27.1% | 405 | 21.1% | 24.5% | 0.30 | 0.32 |
51 | Orianna | Middle | 12 | 14.5% | 3.6% | 18.1% | 33% | 67% | 23 | 25 | ... | -175 | -109 | -3.7 | 8.8 | 26.4% | 385 | 25.8% | 23.7% | 0.44 | 0.21 |
59 | Rumble | Middle | 1 | 1.2% | 1.2% | 4.8% | 0% | 100% | 1 | 3 | ... | -760 | -102 | -8.0 | 8.3 | 24.8% | 394 | 32.4% | 25.6% | 0.53 | 0.21 |
61 | Ryze | Middle | 24 | 28.9% | 38.6% | 67.5% | 46% | 29% | 71 | 62 | ... | 17 | 40 | 1.2 | 8.9 | 27.0% | 349 | 21.4% | 24.4% | 0.45 | 0.18 |
63 | Seraphine | Middle | 1 | 1.2% | 0.0% | 1.2% | 0% | 100% | 2 | 1 | ... | 226 | 447 | 0.0 | 8.1 | 26.5% | 326 | 22.1% | 25.7% | 0.64 | 0.30 |
68 | Sylas | Middle | 16 | 19.3% | 9.6% | 28.9% | 50% | 88% | 63 | 54 | ... | -467 | -115 | -5.5 | 8.1 | 24.9% | 404 | 25.0% | 23.8% | 0.34 | 0.21 |
69 | Syndra | Middle | 14 | 16.9% | 10.8% | 28.9% | 64% | 57% | 60 | 29 | ... | 101 | -259 | 0.4 | 8.3 | 24.9% | 513 | 26.3% | 23.0% | 0.46 | 0.21 |
76 | Tryndamere | Middle | 2 | 2.4% | 7.2% | 10.8% | 0% | 50% | 9 | 7 | ... | -459 | 237 | 4.0 | 9.7 | 30.7% | 437 | 28.4% | 31.4% | 0.41 | 0.34 |
78 | Twisted Fate | Middle | 24 | 28.9% | 67.5% | 96.4% | 54% | 8% | 47 | 44 | ... | 455 | -44 | 0.2 | 8.0 | 23.8% | 410 | 21.5% | 23.9% | 0.34 | 0.24 |
81 | Viktor | Middle | 3 | 3.6% | 0.0% | 3.6% | 0% | 100% | 13 | 12 | ... | -101 | 322 | 4.3 | 8.8 | 27.6% | 727 | 34.8% | 26.6% | 0.40 | 0.20 |
85 | Yasuo | Middle | 2 | 2.4% | 1.2% | 6.0% | 50% | 50% | 11 | 5 | ... | 184 | -220 | 4.5 | 9.6 | 30.3% | 316 | 16.6% | 26.4% | 0.33 | 0.38 |
87 | Yone | Middle | 1 | 1.2% | 0.0% | 1.2% | 0% | 100% | 2 | 3 | ... | 58 | 47 | 1.0 | 8.3 | 24.8% | 245 | 19.7% | 25.5% | 0.20 | 0.40 |
91 | Zoe | Middle | 14 | 16.9% | 9.6% | 26.5% | 64% | 50% | 43 | 22 | ... | 231 | 225 | -1.3 | 7.8 | 22.7% | 501 | 30.2% | 22.3% | 0.40 | 0.22 |
21 rows × 25 columns
c.get_group('Jungle')
Champion | Pos | GP | P% | B% | P+B% | W% | CTR% | K | D | ... | GD10 | XPD10 | CSD10 | CSPM | CS%P15 | DPM | DMG% | GOLD% | WPM | WCPM | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
12 | Fiddlesticks | Jungle | 2 | 2.4% | 2.4% | 4.8% | 50% | 100% | 8 | 9 | ... | -94 | -476 | -5.5 | 5.0 | 13.0% | 341 | 16.9% | 16.7% | 0.10 | 0.16 |
17 | Gragas | Jungle | 1 | 1.2% | 1.2% | 8.4% | 0% | 100% | 2 | 7 | ... | -653 | -73 | -7.0 | 4.2 | 13.6% | 144 | 7.1% | 13.9% | 0.71 | 0.33 |
20 | Graves | Jungle | 3 | 3.6% | 34.9% | 78.3% | 0% | 100% | 4 | 12 | ... | -36 | 111 | 10.0 | 7.2 | 22.1% | 314 | 19.5% | 20.8% | 0.33 | 0.57 |
25 | Jarvan IV | Jungle | 28 | 33.7% | 39.8% | 77.1% | 50% | 54% | 58 | 87 | ... | 16 | -74 | -0.8 | 4.9 | 12.4% | 235 | 13.6% | 16.3% | 0.42 | 0.51 |
36 | Lee Sin | Jungle | 37 | 44.6% | 54.2% | 98.8% | 59% | 0% | 117 | 90 | ... | -42 | 14 | -1.3 | 5.4 | 15.4% | 256 | 14.1% | 17.9% | 0.61 | 0.36 |
38 | Lillia | Jungle | 2 | 2.4% | 0.0% | 2.4% | 50% | 100% | 7 | 8 | ... | 77 | 84 | 7.0 | 6.7 | 13.3% | 432 | 28.8% | 23.5% | 0.46 | 0.43 |
49 | Olaf | Jungle | 6 | 7.2% | 14.5% | 24.1% | 50% | 83% | 18 | 16 | ... | 257 | 339 | 4.5 | 5.2 | 14.6% | 365 | 16.8% | 17.1% | 0.37 | 0.45 |
52 | Poppy | Jungle | 8 | 9.6% | 6.0% | 18.1% | 63% | 88% | 14 | 15 | ... | 19 | 45 | 2.5 | 5.7 | 16.4% | 245 | 14.4% | 17.0% | 0.32 | 0.45 |
55 | Qiyana | Jungle | 10 | 12.0% | 10.8% | 22.9% | 70% | 70% | 43 | 25 | ... | 63 | -72 | -0.2 | 6.2 | 18.7% | 363 | 19.1% | 18.8% | 0.67 | 0.52 |
62 | Sejuani | Jungle | 1 | 1.2% | 0.0% | 1.2% | 0% | 0% | 4 | 3 | ... | -328 | 410 | 15.0 | 4.4 | 10.7% | 326 | 15.4% | 15.9% | 0.44 | 0.42 |
66 | Shaco | Jungle | 1 | 1.2% | 0.0% | 1.2% | 0% | 0% | 0 | 4 | ... | -621 | 49 | -12.0 | 4.6 | 13.2% | 141 | 11.2% | 14.7% | 1.11 | 0.67 |
71 | Taliyah | Jungle | 1 | 1.2% | 3.6% | 4.8% | 0% | 100% | 2 | 5 | ... | 847 | 313 | 4.0 | 5.4 | 14.2% | 299 | 24.1% | 16.8% | 1.37 | 0.60 |
72 | Talon | Jungle | 12 | 14.5% | 13.3% | 27.7% | 58% | 58% | 41 | 25 | ... | 154 | 213 | 3.9 | 6.0 | 17.9% | 338 | 18.6% | 19.4% | 0.35 | 0.36 |
75 | Trundle | Jungle | 6 | 7.2% | 6.0% | 13.3% | 50% | 83% | 11 | 14 | ... | -76 | -183 | -8.0 | 4.5 | 12.6% | 174 | 9.7% | 16.0% | 0.65 | 0.53 |
80 | Viego | Jungle | 18 | 21.7% | 8.4% | 30.1% | 56% | 50% | 64 | 38 | ... | 113 | 140 | 3.2 | 5.6 | 16.1% | 286 | 14.5% | 18.0% | 0.36 | 0.49 |
84 | Xin Zhao | Jungle | 30 | 36.1% | 30.1% | 66.3% | 33% | 63% | 57 | 106 | ... | -125 | -145 | -2.2 | 5.0 | 13.5% | 276 | 16.3% | 16.6% | 0.38 | 0.45 |
16 rows × 25 columns
c.get_group('Support')
Champion | Pos | GP | P% | B% | P+B% | W% | CTR% | K | D | ... | GD10 | XPD10 | CSD10 | CSPM | CS%P15 | DPM | DMG% | GOLD% | WPM | WCPM | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2 | Alistar | Support | 5 | 6.0% | 0.0% | 6.0% | 40% | 80% | 6 | 19 | ... | -44 | -77 | 2.4 | 0.9 | 1.9% | 121 | 7.1% | 8.4% | 1.96 | 0.34 |
3 | Amumu | Support | 1 | 1.2% | 7.2% | 8.4% | 100% | 0% | 2 | 4 | ... | -39 | 446 | 2.0 | 0.8 | 1.8% | 155 | 6.6% | 8.8% | 1.69 | 0.34 |
8 | Braum | Support | 11 | 13.3% | 9.6% | 22.9% | 36% | 100% | 4 | 38 | ... | -47 | -75 | 2.5 | 1.1 | 2.3% | 156 | 9.9% | 8.5% | 1.67 | 0.30 |
18 | Gragas | Support | 1 | 1.2% | 1.2% | 8.4% | 0% | 100% | 1 | 5 | ... | 28 | 433 | 1.0 | 0.8 | 1.6% | 168 | 9.5% | 6.9% | 1.64 | 0.21 |
37 | Leona | Support | 32 | 38.6% | 26.5% | 65.1% | 34% | 34% | 22 | 103 | ... | -34 | -8 | 1.1 | 1.1 | 2.9% | 94 | 5.9% | 8.3% | 1.66 | 0.44 |
42 | Lulu | Support | 16 | 19.3% | 3.6% | 22.9% | 56% | 75% | 12 | 34 | ... | 156 | 80 | -3.2 | 0.5 | 1.8% | 105 | 6.7% | 9.2% | 1.57 | 0.37 |
45 | Maokai | Support | 2 | 2.4% | 0.0% | 2.4% | 50% | 50% | 2 | 7 | ... | 48 | 291 | 3.0 | 1.5 | 5.1% | 393 | 19.6% | 9.7% | 1.61 | 0.46 |
47 | Nami | Support | 13 | 15.7% | 24.1% | 39.8% | 54% | 38% | 11 | 29 | ... | 91 | 70 | -2.9 | 0.5 | 1.9% | 184 | 10.5% | 9.0% | 2.07 | 0.39 |
48 | Nautilus | Support | 10 | 12.0% | 4.8% | 16.9% | 50% | 90% | 4 | 47 | ... | -194 | -63 | 8.0 | 1.0 | 1.9% | 118 | 6.4% | 7.9% | 1.59 | 0.30 |
54 | Pyke | Support | 3 | 3.6% | 0.0% | 3.6% | 33% | 67% | 13 | 15 | ... | 317 | 331 | 4.7 | 1.3 | 4.4% | 205 | 10.3% | 14.1% | 2.34 | 0.51 |
56 | Rakan | Support | 29 | 34.9% | 19.3% | 54.2% | 62% | 28% | 24 | 75 | ... | -52 | -81 | 0.3 | 1.1 | 3.1% | 130 | 7.1% | 9.1% | 2.15 | 0.51 |
57 | Rell | Support | 9 | 10.8% | 3.6% | 14.5% | 56% | 78% | 9 | 32 | ... | -26 | -141 | 3.7 | 1.1 | 2.4% | 134 | 7.0% | 8.6% | 1.60 | 0.37 |
64 | Sett | Support | 1 | 1.2% | 7.2% | 10.8% | 100% | 100% | 2 | 5 | ... | -590 | -539 | 4.0 | 1.4 | 4.2% | 234 | 9.0% | 8.8% | 1.78 | 0.59 |
67 | Shen | Support | 3 | 3.6% | 0.0% | 3.6% | 33% | 100% | 4 | 9 | ... | 76 | 289 | -0.7 | 1.2 | 3.1% | 112 | 5.8% | 9.6% | 2.40 | 0.49 |
73 | Thresh | Support | 12 | 14.5% | 21.7% | 36.1% | 58% | 42% | 5 | 26 | ... | -54 | 52 | -0.1 | 1.0 | 1.9% | 101 | 5.4% | 8.5% | 1.64 | 0.28 |
88 | Yuumi | Support | 15 | 18.1% | 78.3% | 96.4% | 53% | 0% | 21 | 21 | ... | 117 | 51 | -8.4 | 0.2 | 1.0% | 323 | 17.6% | 9.6% | 1.51 | 0.17 |
90 | Zilean | Support | 3 | 3.6% | 0.0% | 3.6% | 67% | 100% | 1 | 4 | ... | 10 | -222 | -2.0 | 1.1 | 2.3% | 60 | 3.6% | 9.2% | 1.83 | 0.43 |
17 rows × 25 columns
Pagal pasirinkima rolėje galima teigti:
Jungle ir Supp rolėse žaidėjai renkasi pilnai tai rolej sukurtus personažus.
Adc, mid ir top rolėse žaidėjai linkę ekspermentuoti, daryti nestandartinius sprendimus imtis kitokių taktikų.
pvz.: Adc rolėje pasiimtas mid (pilnos magijos) personažas(Ziggs), Top rolėje pasiimtas adc (marksmen) personažas(Lucian)
players_2021.nlargest(10, ['K'])
Player | Team | Pos | GP | W% | CTR% | K | D | A | KDA | ... | CSPM | CS%P15 | DPM | DMG% | D%P15 | EGPM | GOLD% | STL | WPM | WCPM | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
65 | ShowMaker | DWG KIA | Middle | 19 | 74% | 53% | 75 | 27 | 125 | 7.4 | ... | 8.2 | 23.9% | 506 | 27.1% | 28.5% | 276 | 23.6% | 0 | 0.51 | 0.36 |
73 | Viper | EDward Gaming | ADC | 21 | 62% | 52% | 73 | 41 | 104 | 4.3 | ... | 9.2 | 29.7% | 477 | 25.6% | 25.9% | 287 | 25.0% | 1 | 0.56 | 0.38 |
31 | Ghost | DWG KIA | ADC | 19 | 74% | 68% | 72 | 30 | 102 | 5.8 | ... | 8.7 | 28.9% | 463 | 24.1% | 24.9% | 292 | 24.8% | 0 | 0.53 | 0.25 |
64 | Scout | EDward Gaming | Middle | 21 | 62% | 43% | 69 | 38 | 109 | 4.7 | ... | 8.5 | 25.1% | 415 | 22.7% | 23.1% | 269 | 23.4% | 0 | 0.35 | 0.26 |
46 | Khan | DWG KIA | Top | 19 | 74% | 58% | 67 | 55 | 113 | 3.3 | ... | 8.6 | 28.3% | 508 | 26.3% | 24.4% | 287 | 24.4% | 0 | 0.44 | 0.32 |
27 | Flandre | EDward Gaming | Top | 21 | 62% | 29% | 56 | 53 | 91 | 2.8 | ... | 9.3 | 29.9% | 531 | 28.7% | 28.4% | 295 | 25.9% | 0 | 0.26 | 0.31 |
62 | Ruler | Gen.G | ADC | 16 | 63% | 69% | 56 | 25 | 91 | 5.9 | ... | 9.4 | 31.4% | 440 | 25.2% | 27.0% | 313 | 27.1% | 0 | 0.55 | 0.40 |
13 | Canyon | DWG KIA | Jungle | 19 | 74% | 42% | 55 | 34 | 130 | 5.4 | ... | 5.2 | 15.1% | 253 | 13.2% | 13.7% | 201 | 17.1% | 1 | 0.63 | 0.33 |
76 | Wei | Royal Never Give Up | Jungle | 12 | 58% | 42% | 53 | 41 | 95 | 3.6 | ... | 4.8 | 13.9% | 377 | 19.7% | 19.0% | 214 | 18.5% | 1 | 0.50 | 0.48 |
6 | Bdd | Gen.G | Middle | 16 | 63% | 75% | 52 | 40 | 100 | 3.8 | ... | 8.0 | 23.0% | 539 | 30.4% | 28.6% | 259 | 22.6% | 0 | 0.44 | 0.21 |
10 rows × 27 columns
players_2021.nlargest(10, ['A'])
Player | Team | Pos | GP | W% | CTR% | K | D | A | KDA | ... | CSPM | CS%P15 | DPM | DMG% | D%P15 | EGPM | GOLD% | STL | WPM | WCPM | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
52 | Meiko | EDward Gaming | Support | 21 | 62% | 48% | 14 | 41 | 188 | 4.9 | ... | 0.8 | 2.1% | 156 | 8.4% | 7.5% | 105 | 9.0% | 0 | 1.69 | 0.39 |
8 | BeryL | DWG KIA | Support | 19 | 74% | 63% | 22 | 44 | 166 | 4.3 | ... | 1.3 | 3.8% | 173 | 9.2% | 8.5% | 119 | 10.0% | 0 | 1.93 | 0.57 |
42 | Jiejie | EDward Gaming | Jungle | 21 | 62% | 52% | 47 | 52 | 155 | 3.9 | ... | 5.2 | 13.3% | 269 | 14.6% | 15.1% | 193 | 16.7% | 4 | 0.43 | 0.48 |
48 | Life | Gen.G | Support | 16 | 63% | 25% | 19 | 39 | 149 | 4.3 | ... | 0.7 | 2.0% | 127 | 7.0% | 5.9% | 104 | 8.9% | 0 | 1.66 | 0.34 |
13 | Canyon | DWG KIA | Jungle | 19 | 74% | 42% | 55 | 34 | 130 | 5.4 | ... | 5.2 | 15.1% | 253 | 13.2% | 13.7% | 201 | 17.1% | 1 | 0.63 | 0.33 |
65 | ShowMaker | DWG KIA | Middle | 19 | 74% | 53% | 75 | 27 | 125 | 7.4 | ... | 8.2 | 23.9% | 506 | 27.1% | 28.5% | 276 | 23.6% | 0 | 0.51 | 0.36 |
45 | Keria | T1 | Support | 14 | 71% | 64% | 8 | 17 | 120 | 7.5 | ... | 0.9 | 1.7% | 136 | 7.4% | 5.9% | 116 | 9.8% | 0 | 1.82 | 0.30 |
20 | Cryin | Royal Never Give Up | Middle | 12 | 58% | 33% | 31 | 28 | 114 | 5.2 | ... | 6.9 | 20.9% | 425 | 23.5% | 24.2% | 237 | 20.6% | 0 | 0.42 | 0.15 |
46 | Khan | DWG KIA | Top | 19 | 74% | 58% | 67 | 55 | 113 | 3.3 | ... | 8.6 | 28.3% | 508 | 26.3% | 24.4% | 287 | 24.4% | 0 | 0.44 | 0.32 |
43 | Kaiser | MAD Lions | Support | 11 | 36% | 36% | 10 | 34 | 112 | 3.6 | ... | 0.9 | 2.5% | 153 | 8.0% | 7.1% | 99 | 8.6% | 0 | 2.05 | 0.42 |
10 rows × 27 columns
players_2021.nsmallest(10, ['D'])
Player | Team | Pos | GP | W% | CTR% | K | D | A | KDA | ... | CSPM | CS%P15 | DPM | DMG% | D%P15 | EGPM | GOLD% | STL | WPM | WCPM | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
49 | Light | LNG Esports | ADC | 7 | 43% | 43% | 20 | 7 | 35 | 7.9 | ... | 9.7 | 31.8% | 506 | 30.9% | 35.8% | 291 | 26.0% | 0 | 0.38 | 0.37 |
69 | Tarzan | LNG Esports | Jungle | 7 | 43% | 14% | 12 | 11 | 44 | 5.1 | ... | 5.3 | 13.6% | 236 | 13.7% | 12.9% | 178 | 16.5% | 0 | 0.39 | 0.39 |
72 | Unified | PSG Talon | ADC | 6 | 50% | 33% | 14 | 12 | 34 | 4.0 | ... | 8.3 | 28.0% | 384 | 20.3% | 20.5% | 257 | 23.1% | 0 | 0.48 | 0.28 |
4 | Aria | DetonatioN FocusMe | Middle | 6 | 0% | 50% | 14 | 13 | 20 | 2.6 | ... | 7.8 | 26.0% | 395 | 28.7% | 26.4% | 226 | 24.8% | 0 | 0.40 | 0.21 |
44 | Kaiwing | PSG Talon | Support | 6 | 50% | 50% | 3 | 13 | 62 | 5.0 | ... | 0.7 | 1.4% | 219 | 11.1% | 10.5% | 89 | 8.0% | 0 | 1.76 | 0.26 |
66 | Ssumday | 100 Thieves | Top | 6 | 50% | 50% | 11 | 13 | 29 | 3.1 | ... | 7.8 | 24.7% | 412 | 25.6% | 24.3% | 228 | 21.9% | 0 | 0.49 | 0.16 |
26 | FBI | 100 Thieves | ADC | 6 | 50% | 17% | 23 | 14 | 28 | 3.6 | ... | 9.3 | 30.6% | 430 | 29.6% | 31.6% | 293 | 27.6% | 0 | 0.49 | 0.28 |
32 | Gumayusi | T1 | ADC | 14 | 71% | 36% | 49 | 14 | 52 | 7.2 | ... | 10.2 | 34.2% | 459 | 26.5% | 28.8% | 324 | 27.5% | 0 | 0.47 | 0.33 |
35 | huhi | 100 Thieves | Support | 6 | 50% | 33% | 10 | 14 | 49 | 4.2 | ... | 0.7 | 2.1% | 178 | 11.0% | 9.9% | 108 | 10.0% | 0 | 2.02 | 0.41 |
40 | Iwandy | LNG Esports | Support | 7 | 43% | 57% | 5 | 14 | 49 | 3.9 | ... | 1.0 | 2.7% | 102 | 6.6% | 5.9% | 94 | 8.4% | 0 | 1.71 | 0.40 |
10 rows × 27 columns
players_2021.nlargest(10, ['D'])
Player | Team | Pos | GP | W% | CTR% | K | D | A | KDA | ... | CSPM | CS%P15 | DPM | DMG% | D%P15 | EGPM | GOLD% | STL | WPM | WCPM | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
46 | Khan | DWG KIA | Top | 19 | 74% | 58% | 67 | 55 | 113 | 3.3 | ... | 8.6 | 28.3% | 508 | 26.3% | 24.4% | 287 | 24.4% | 0 | 0.44 | 0.32 |
27 | Flandre | EDward Gaming | Top | 21 | 62% | 29% | 56 | 53 | 91 | 2.8 | ... | 9.3 | 29.9% | 531 | 28.7% | 28.4% | 295 | 25.9% | 0 | 0.26 | 0.31 |
42 | Jiejie | EDward Gaming | Jungle | 21 | 62% | 52% | 47 | 52 | 155 | 3.9 | ... | 5.2 | 13.3% | 269 | 14.6% | 15.1% | 193 | 16.7% | 4 | 0.43 | 0.48 |
16 | Clid | Gen.G | Jungle | 16 | 63% | 50% | 52 | 49 | 103 | 3.2 | ... | 5.1 | 14.8% | 285 | 16.3% | 17.0% | 196 | 17.1% | 5 | 0.60 | 0.49 |
37 | Hylissang | Fnatic | Support | 6 | 17% | 33% | 3 | 47 | 65 | 1.4 | ... | 0.9 | 2.3% | 188 | 9.1% | 8.6% | 96 | 8.9% | 0 | 1.69 | 0.36 |
5 | Armut | MAD Lions | Top | 11 | 36% | 64% | 35 | 46 | 61 | 2.1 | ... | 7.8 | 24.4% | 430 | 24.2% | 23.3% | 257 | 22.7% | 0 | 0.44 | 0.25 |
53 | Ming | Royal Never Give Up | Support | 12 | 58% | 75% | 14 | 45 | 105 | 2.6 | ... | 1.0 | 2.3% | 132 | 6.9% | 6.7% | 102 | 8.8% | 0 | 1.71 | 0.34 |
8 | BeryL | DWG KIA | Support | 19 | 74% | 63% | 22 | 44 | 166 | 4.3 | ... | 1.3 | 3.8% | 173 | 9.2% | 8.5% | 119 | 10.0% | 0 | 1.93 | 0.57 |
23 | Elyoya | MAD Lions | Jungle | 11 | 36% | 73% | 33 | 44 | 85 | 2.7 | ... | 5.3 | 15.3% | 258 | 13.5% | 13.6% | 200 | 17.3% | 0 | 0.39 | 0.45 |
36 | Humanoid | MAD Lions | Middle | 11 | 36% | 55% | 46 | 42 | 66 | 2.7 | ... | 8.9 | 27.7% | 534 | 28.5% | 29.5% | 289 | 25.8% | 0 | 0.44 | 0.24 |
10 rows × 27 columns
best_kda = players_2021.groupby(['KDA']).max().sort_values(by = 'KDA', ascending = False)
best_kda.head(10)
Player | Team | Pos | GP | W% | CTR% | K | D | A | KP | ... | CSPM | CS%P15 | DPM | DMG% | D%P15 | EGPM | GOLD% | STL | WPM | WCPM | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
KDA | |||||||||||||||||||||
7.9 | Light | LNG Esports | ADC | 7 | 43% | 43% | 20 | 7 | 35 | 62.5% | ... | 9.7 | 31.8% | 506 | 30.9% | 35.8% | 291 | 26.0% | 0 | 0.38 | 0.37 |
7.5 | Keria | T1 | Support | 14 | 71% | 64% | 8 | 17 | 120 | 78.0% | ... | 0.9 | 1.7% | 136 | 7.4% | 5.9% | 116 | 9.8% | 0 | 1.82 | 0.30 |
7.4 | ShowMaker | DWG KIA | Middle | 19 | 74% | 53% | 75 | 27 | 125 | 68.7% | ... | 8.2 | 23.9% | 506 | 27.1% | 28.5% | 276 | 23.6% | 0 | 0.51 | 0.36 |
7.2 | Gumayusi | T1 | ADC | 14 | 71% | 36% | 49 | 14 | 52 | 61.6% | ... | 10.2 | 34.2% | 459 | 26.5% | 28.8% | 324 | 27.5% | 0 | 0.47 | 0.33 |
5.9 | Ruler | Gen.G | ADC | 16 | 63% | 69% | 56 | 25 | 91 | 65.0% | ... | 9.4 | 31.4% | 440 | 25.2% | 27.0% | 313 | 27.1% | 0 | 0.55 | 0.40 |
5.8 | Ghost | DWG KIA | ADC | 19 | 74% | 68% | 72 | 30 | 102 | 59.8% | ... | 8.7 | 28.9% | 463 | 24.1% | 24.9% | 292 | 24.8% | 0 | 0.53 | 0.25 |
5.4 | Canyon | DWG KIA | Jungle | 19 | 74% | 42% | 55 | 34 | 130 | 63.6% | ... | 5.2 | 15.1% | 253 | 13.2% | 13.7% | 201 | 17.1% | 1 | 0.63 | 0.33 |
5.2 | GALA | Royal Never Give Up | Middle | 12 | 58% | 42% | 51 | 28 | 114 | 74.4% | ... | 9.3 | 32.3% | 468 | 24.1% | 25.6% | 304 | 26.4% | 0 | 0.42 | 0.33 |
5.1 | Tarzan | LNG Esports | Jungle | 7 | 43% | 14% | 12 | 11 | 44 | 63.6% | ... | 5.3 | 13.6% | 236 | 13.7% | 12.9% | 178 | 16.5% | 0 | 0.39 | 0.39 |
5.0 | Kaiwing | PSG Talon | Support | 6 | 50% | 50% | 3 | 13 | 62 | 69.9% | ... | 0.7 | 1.4% | 219 | 11.1% | 10.5% | 89 | 8.0% | 0 | 1.76 | 0.26 |
10 rows × 26 columns
Žaidime ypač žemesnėse lygose vyrauja tai jog 'kill'as' yra gaunamas, bet kokia kaina, net gi ir pačio personažo mirties, todėl mirčių ir nužudymų skaičius būna labai panašus.
Pažvelgus į mūsų duomenis galime teigti jog dviejų top komandų DWG KIA (2 vieta) ir EDward Gaming (laimėtojai), 2 komandos žaidėjai Khan ir Flandre buvo tarp TOP 10 daugiausia mirusiu ir daugiausia nuzudziusiu zaideju.
Puikiausias pasirodymas DWG KIA žaidėjo Canyon pasirodymas, kuris pateko tarp TOP 10 daugiausia nužudžiusių ir padėjusių komandai žaidėjų.
players_2021['Avg_gold_dif_per_game'] = players_2021['GD10'] / players_2021['GP']
players_2021
Player | Team | Pos | GP | W% | CTR% | K | D | A | KDA | ... | CS%P15 | DPM | DMG% | D%P15 | EGPM | GOLD% | STL | WPM | WCPM | Avg_gold_dif_per_game | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | Abbedagge | 100 Thieves | Middle | 6 | 50% | 50% | 15 | 16 | 26 | 2.6 | ... | 26.7% | 349 | 22.2% | 23.1% | 242 | 23.2% | 0 | 0.53 | 0.24 | -74.166667 |
1 | Adam | Fnatic | Top | 6 | 17% | 50% | 26 | 39 | 30 | 1.4 | ... | 26.4% | 528 | 24.0% | 23.2% | 264 | 23.8% | 0 | 0.32 | 0.22 | -51.833333 |
2 | Ale | LNG Esports | Top | 7 | 43% | 86% | 24 | 20 | 29 | 2.7 | ... | 28.6% | 416 | 25.9% | 24.9% | 280 | 25.7% | 0 | 0.33 | 0.22 | -3.571429 |
3 | Alphari | Team Liquid | Top | 7 | 43% | 57% | 19 | 17 | 22 | 2.4 | ... | 26.9% | 394 | 24.6% | 21.2% | 266 | 24.8% | 0 | 0.48 | 0.15 | 76.285714 |
4 | Aria | DetonatioN FocusMe | Middle | 6 | 0% | 50% | 14 | 13 | 20 | 2.6 | ... | 26.0% | 395 | 28.7% | 26.4% | 226 | 24.8% | 0 | 0.40 | 0.21 | 35.333333 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
76 | Wei | Royal Never Give Up | Jungle | 12 | 58% | 42% | 53 | 41 | 95 | 3.6 | ... | 13.9% | 377 | 19.7% | 19.0% | 214 | 18.5% | 1 | 0.50 | 0.48 | -11.750000 |
77 | Willer | Hanwha Life Esports | Jungle | 10 | 40% | 10% | 25 | 28 | 64 | 3.2 | ... | 14.7% | 221 | 12.3% | 12.9% | 181 | 16.6% | 3 | 0.52 | 0.38 | -1.200000 |
78 | Xiaohu | Royal Never Give Up | Top | 12 | 58% | 58% | 46 | 33 | 78 | 3.8 | ... | 30.6% | 505 | 25.7% | 24.6% | 294 | 25.7% | 0 | 0.38 | 0.24 | 15.000000 |
79 | Yutapon | DetonatioN FocusMe | ADC | 6 | 0% | 67% | 12 | 16 | 12 | 1.5 | ... | 33.4% | 329 | 21.6% | 25.6% | 249 | 27.1% | 0 | 0.26 | 0.31 | -126.833333 |
80 | Zven | Cloud9 | ADC | 10 | 30% | 60% | 24 | 29 | 45 | 2.4 | ... | 29.5% | 378 | 24.5% | 26.6% | 275 | 24.9% | 0 | 0.43 | 0.35 | -1.200000 |
81 rows × 28 columns
avg_gold_player = players_2021.groupby(['Avg_gold_dif_per_game']).max().sort_values(by = 'Avg_gold_dif_per_game', ascending = False)
avg_gold_player. head(10)
Player | Team | Pos | GP | W% | CTR% | K | D | A | KDA | ... | CSPM | CS%P15 | DPM | DMG% | D%P15 | EGPM | GOLD% | STL | WPM | WCPM | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Avg_gold_dif_per_game | |||||||||||||||||||||
114.333333 | FBI | 100 Thieves | ADC | 6 | 50% | 17% | 23 | 14 | 28 | 3.6 | ... | 9.3 | 30.6% | 430 | 29.6% | 31.6% | 293 | 27.6% | 0 | 0.49 | 0.28 |
78.833333 | huhi | 100 Thieves | Support | 6 | 50% | 33% | 10 | 14 | 49 | 4.2 | ... | 0.7 | 2.1% | 178 | 11.0% | 9.9% | 108 | 10.0% | 0 | 2.02 | 0.41 |
76.285714 | Alphari | Team Liquid | Top | 7 | 43% | 57% | 19 | 17 | 22 | 2.4 | ... | 8.6 | 26.9% | 394 | 24.6% | 21.2% | 266 | 24.8% | 0 | 0.48 | 0.15 |
46.600000 | Chovy | Hanwha Life Esports | Middle | 10 | 40% | 50% | 35 | 23 | 54 | 3.9 | ... | 9.0 | 28.4% | 570 | 31.0% | 30.3% | 288 | 26.5% | 0 | 0.49 | 0.45 |
35.333333 | Aria | DetonatioN FocusMe | Middle | 6 | 0% | 50% | 14 | 13 | 20 | 2.6 | ... | 7.8 | 26.0% | 395 | 28.7% | 26.4% | 226 | 24.8% | 0 | 0.40 | 0.21 |
33.300000 | Deft | Hanwha Life Esports | ADC | 10 | 40% | 80% | 40 | 23 | 47 | 3.8 | ... | 8.8 | 29.4% | 562 | 30.4% | 32.8% | 279 | 25.8% | 0 | 0.55 | 0.28 |
29.833333 | BEAN | Fnatic | ADC | 6 | 17% | 33% | 24 | 22 | 32 | 2.5 | ... | 8.0 | 26.7% | 484 | 22.5% | 21.8% | 264 | 23.4% | 0 | 0.37 | 0.29 |
22.000000 | Bwipo | Fnatic | Jungle | 6 | 17% | 67% | 22 | 30 | 52 | 2.5 | ... | 6.2 | 18.7% | 442 | 20.6% | 21.0% | 224 | 20.5% | 0 | 0.39 | 0.43 |
19.857143 | Oner | T1 | Jungle | 14 | 71% | 57% | 44 | 24 | 72 | 4.8 | ... | 5.6 | 15.6% | 315 | 16.9% | 18.1% | 210 | 17.8% | 3 | 0.39 | 0.55 |
19.571429 | Light | LNG Esports | ADC | 7 | 43% | 43% | 20 | 7 | 35 | 7.9 | ... | 9.7 | 31.8% | 506 | 30.9% | 35.8% | 291 | 26.0% | 0 | 0.38 | 0.37 |
10 rows × 27 columns
best_XP_diff = players_2021.groupby(['XPD10']).max().sort_values(by = 'XPD10', ascending = False)
best_XP_diff.head(10)
Player | Team | Pos | GP | W% | CTR% | K | D | A | KDA | ... | CS%P15 | DPM | DMG% | D%P15 | EGPM | GOLD% | STL | WPM | WCPM | Avg_gold_dif_per_game | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
XPD10 | |||||||||||||||||||||
408 | Chovy | Hanwha Life Esports | Middle | 10 | 40% | 50% | 35 | 23 | 54 | 3.9 | ... | 28.4% | 570 | 31.0% | 30.3% | 288 | 26.5% | 0 | 0.49 | 0.45 | 46.600000 |
331 | Bwipo | Fnatic | Jungle | 6 | 17% | 67% | 22 | 30 | 52 | 2.5 | ... | 18.7% | 442 | 20.6% | 21.0% | 224 | 20.5% | 0 | 0.39 | 0.43 | 22.000000 |
294 | Larssen | Rogue | Middle | 8 | 38% | 50% | 25 | 19 | 37 | 3.3 | ... | 25.1% | 323 | 19.3% | 17.9% | 269 | 24.4% | 0 | 0.35 | 0.14 | 7.500000 |
283 | huhi | 100 Thieves | Support | 6 | 50% | 33% | 10 | 14 | 49 | 4.2 | ... | 2.1% | 178 | 11.0% | 9.9% | 108 | 10.0% | 0 | 2.02 | 0.41 | 78.833333 |
277 | FBI | 100 Thieves | ADC | 6 | 50% | 17% | 23 | 14 | 28 | 3.6 | ... | 30.6% | 430 | 29.6% | 31.6% | 293 | 27.6% | 0 | 0.49 | 0.28 | 114.333333 |
268 | Tactical | Team Liquid | ADC | 7 | 43% | 86% | 14 | 17 | 24 | 2.2 | ... | 31.3% | 453 | 25.9% | 29.8% | 269 | 24.9% | 0 | 0.50 | 0.18 | -21.000000 |
243 | Humanoid | MAD Lions | Middle | 11 | 36% | 55% | 46 | 42 | 66 | 2.7 | ... | 27.7% | 534 | 28.5% | 29.5% | 289 | 25.8% | 0 | 0.44 | 0.24 | 7.090909 |
226 | Jiejie | EDward Gaming | Jungle | 21 | 62% | 52% | 47 | 52 | 155 | 3.9 | ... | 13.3% | 269 | 14.6% | 15.1% | 193 | 16.7% | 4 | 0.43 | 0.48 | 7.238095 |
216 | BEAN | Fnatic | ADC | 6 | 17% | 33% | 24 | 22 | 32 | 2.5 | ... | 26.7% | 484 | 22.5% | 21.8% | 264 | 23.4% | 0 | 0.37 | 0.29 | 29.833333 |
192 | BeryL | DWG KIA | Support | 19 | 74% | 63% | 22 | 44 | 166 | 4.3 | ... | 3.8% | 173 | 9.2% | 8.5% | 119 | 10.0% | 0 | 1.93 | 0.57 | 3.263158 |
10 rows × 27 columns
Galime matyti jog nei vienas žaidėjas iš pirmą (EDward gaming) ir antrą (DWG KIA) užėmusių komandų neturėjo max gold, todėl galime teigti jog ne visada nusipirktu items kiekis prieš kitą žaidėją mums gali padėti.
Nors ir vieni iš paskutinių, bet lyginant XP difference jau galime pamatyti Jiejie ir BeryL top 10, noriu pabrėžti, kad XP difference ypač svarbus early game.
Pasirinkau būtent Diamond lygą, nes nuo šios lygos gaming talentų ieško komandos, iš tiesų skaitant internete ar bendraujant su žaidėjais išsikeliama daug hipotezių, kad žaidimas nuo Diamond lygos nelabai skiriasi nuo Pro lygos žaidėjų, ką pastebėjau šios analizės metu, kad skirtumas yra didelis: