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'

image.png

ANALIZĖS APRAŠYMAS

Mano analizę sudarys 3 dalys:

  1. "Diamond" lygos žaidėjų elgiasys, išvados
  2. "League of legends championship 2021", išvados
  3. "Diamond" lygos ir "League of legends championship 2021" žaidimų palyginimas.

PIRMA DALIS: "Diamond" lygos žaidėjų elgiasys¶

Duomenų sukėlimas ("Diamond" lyga)

In [3]:
import numpy as np
import pandas as pd
import seaborn as sns
import json
import matplotlib
import matplotlib.pyplot as plt
In [4]:
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
Out[4]:
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

In [5]:
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
Out[5]:
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

In [6]:
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
Out[6]:
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 ...
In [7]:
ranked_games=pd.read_csv('C:\\Users\\migle\\Desktop\\baigiamasis\\champs and bans\\games.csv')
ranked_games
Out[7]:
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

Vieno žaidimo trukmė¶

In [13]:
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
In [115]:
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.

Ar 'Pirmieji' veiksmai turi įtakos laimėjimui¶

In [18]:
komandu_pav_pakeitimas = ranked_games.replace([0,1,2],['Nei viena','Blue','Red'])
komandu_pav_pakeitimas
Out[18]:
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:

In [72]:
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
Out[72]:
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:

In [116]:
ranked_games['firstBlood'].value_counts()
Out[116]:
1    26113
2    24822
0      555
Name: firstBlood, dtype: int64
In [188]:
sns.countplot(x = 'firstBlood', data = ranked_games)
plt.xlabel('First blood')
plt.ylabel('How many times')
plt.title('First blood chart', fontsize = 18)
Out[188]:
Text(0.5, 1.0, 'First blood chart')

Mėlyna (Blue) komanda daugiau kartų pasiimė 'first blood', skirtumas nežymus.

In [118]:
ranked_games['firstTower'].value_counts()
Out[118]:
1    25861
2    24416
0     1213
Name: firstTower, dtype: int64
In [119]:
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
Out[119]:
<function matplotlib.pyplot.show(close=None, block=None)>

Mėlyna (Blue) komanda daugiau kartų pasiimė 'first tower', skirtumas nežymus.

In [120]:
ranked_games['firstInhibitor'].value_counts()
Out[120]:
1    23054
2    22160
0     6276
Name: firstInhibitor, dtype: int64
In [121]:
sns.countplot(x = 'firstInhibitor', data = ranked_games)
plt.xlabel('First inhibitor')
plt.ylabel('How many times')
plt.title('First inhibitor chart', fontsize = 18)
Out[121]:
Text(0.5, 1.0, 'First inhibitor chart')

Mėlyna (Blue) komanda daugiau kartų žaidimuose 'best of 3', nuvertė 'inhibitor' bokštą, skirtumas nežymus.

In [122]:
ranked_games['firstBaron'].value_counts()
Out[122]:
0    20258
2    16474
1    14758
Name: firstBaron, dtype: int64
In [123]:
sns.countplot(x = 'firstBaron', data = ranked_games)
plt.xlabel('First Baron')
plt.ylabel('How many times')
plt.title('First Baron chart', fontsize = 18)
Out[123]:
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.

In [124]:
ranked_games['firstDragon'].value_counts()
Out[124]:
2    24800
1    24690
0     2000
Name: firstDragon, dtype: int64
In [125]:
sns.countplot(x = 'firstDragon', data = ranked_games)
plt.xlabel('First Dragon')
plt.ylabel('How many times')
plt.title('First Dragon chart', fontsize = 18)
Out[125]:
Text(0.5, 1.0, 'First Dragon chart')

Galima teigti jog 'First dragon' komandos pasidalijo pusiau, skirtumas visiškai nedaro įtakos.

In [126]:
ranked_games['firstRiftHerald'].value_counts()
Out[126]:
0    26179
1    12948
2    12363
Name: firstRiftHerald, dtype: int64
In [127]:
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)
Out[127]:
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.

In [128]:
ranked_games['winner'].value_counts()
Out[128]:
1    26077
2    25413
Name: winner, dtype: int64
In [129]:
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)
Out[129]:
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.

Kokius personažų (champions) pasirinkimus darė abi komandos?¶

In [54]:
ranked_games.columns
Out[54]:
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')
In [60]:
champions_picked = ['t1_champ1id', 't1_champ2id', 't1_champ3id', 't1_champ4id', 't1_champ5id',
                    't2_champ1id', 't2_champ2id', 't2_champ3id', 't2_champ4id', 't2_champ5id']
champions_picked
Out[60]:
['t1_champ1id',
 't1_champ2id',
 't1_champ3id',
 't1_champ4id',
 't1_champ5id',
 't2_champ1id',
 't2_champ2id',
 't2_champ3id',
 't2_champ4id',
 't2_champ5id']
In [61]:
Champions_banned = ['t1_ban1', 't1_ban2', 't1_ban3', 't1_ban4', 't1_ban5',
                    't2_ban1', 't2_ban2', 't2_ban3', 't2_ban4', 't2_ban5']
Champions_banned
Out[61]:
['t1_ban1',
 't1_ban2',
 't1_ban3',
 't1_ban4',
 't1_ban5',
 't2_ban1',
 't2_ban2',
 't2_ban3',
 't2_ban4',
 't2_ban5']
In [62]:
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
Out[62]:
{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:

In [63]:
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
In [64]:
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
In [94]:
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.

Kokius 'summoner spell' renkasi žaidėjai iš kiekvienos komandos:¶

In [68]:
ranked_games.columns
Out[68]:
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')
In [80]:
ranked_games['t1_champ1_sum1'].dtypes
Out[80]:
dtype('int64')
In [76]:
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
Out[76]:
['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']
In [109]:
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
Out[109]:
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

In [93]:
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
Out[93]:
{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'

ANTRA DALIS: "League of legends championship 2021"¶

Duomenų sukėlimas ("League of legends championship 2021")

In [130]:
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
Out[130]:
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

In [131]:
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
Out[131]:
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

Personažų pasirinkimai¶

In [181]:
champions_2021.sort_values('GP', ascending = False).head(10)
Out[181]:
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.

Rolės ir personažo pasirinkimas¶

In [142]:
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
In [144]:
c = champions_2021.groupby('Pos')
c
Out[144]:
<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000001BDA7785DF0>
In [145]:
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])
In [152]:
c.get_group('Top')
Out[152]:
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

In [153]:
c.get_group('ADC')
Out[153]:
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

In [154]:
c.get_group('Middle')
Out[154]:
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

In [155]:
c.get_group('Jungle')
Out[155]:
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

In [156]:
c.get_group('Support')
Out[156]:
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)

KDA top 10 žaidėjų¶

In [158]:
players_2021.nlargest(10, ['K'])
Out[158]:
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

In [159]:
players_2021.nlargest(10, ['A'])
Out[159]:
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

In [162]:
players_2021.nsmallest(10, ['D'])
Out[162]:
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

In [160]:
players_2021.nlargest(10, ['D'])
Out[160]:
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

In [164]:
best_kda = players_2021.groupby(['KDA']).max().sort_values(by = 'KDA', ascending = False)
best_kda.head(10)
Out[164]:
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.

XP ir GOLD difference¶

In [167]:
players_2021['Avg_gold_dif_per_game'] = players_2021['GD10'] / players_2021['GP']
players_2021
Out[167]:
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

In [171]:
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)
Out[171]:
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

In [173]:
best_XP_diff = players_2021.groupby(['XPD10']).max().sort_values(by = 'XPD10', ascending = False)
best_XP_diff.head(10)
Out[173]:
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.

Išvados¶

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:

  1. Diamond lygje žaidžiama pagal nusistovėjusius champion pasirinkimus ir ban'us
  2. Pro lygoje pagal champ pasirinkimus galima teigti jog žaidimas yra labiau strateginis, kai diamond lygoje jis labiau inpulsyvus
  3. Pro lygoje mažesnę įtaką žaidimui daro uždirbtas gold, kai diamond lygoje gautas gold už tam tikrus dalykus net padeda laimėti
  4. Diamond lygoje maža koncentracija į džiunglių pabaisas kurios gali duoti secialių boost'ų komandai, kai pro lygoje tai visiškai priešinga
In [ ]: