LoL Worlds 2020

Sep 24, 2020

Tutorials

1. Gathering LoLEsports data
1. 1.1. Find the tournament
2. 1.2. Getting the hashes

Get your stats ready for LoL Worlds 2020

In this article, the new way to gather official matches data will be presented, as well as some analysis oriented toward players.

You can find the notebook with all the code and the dataset on Github : https://github.com/HextechLab/Worlds2020

Gathering LoLEsports data

Following the shutdown of some important API from the lolesports website that allowed us to get hashes to gather data from official matches, the solution is to rely on Leaguepedia which has everything we need.

Here is an example of how to do it in Python.

We will use the leaguepdia-parser package to gather what we need from Leaguepedia.

Find the tournament

1
2
3

import leaguepedia_parser as lp

lp.get_regions()

['',
 'Africa',
 'Asia',
 'Brazil',
 'China',
 'CIS',
 'Europe',
 'International',
 'Japan',
 'Korea',
 'LAN',
 'LAS',
 'Latin America',
 'LMS',
 'MENA',
 'North America',
 'Oceania',
 'PCS',
 'SEA',
 'Turkey',
 'Unknown',
 'Vietnam',
 'Wildcard']

Pick your favorite region to get the list of tournaments in this region :

1 2	tournaments = lp.get_tournaments('Europe', year=2020) [t["name"] for t in tournaments]

['LEC 2020 Spring',
 'LEC 2020 Spring Playoffs',
 'EU Face-Off 2020',
 'LEC 2020 Summer',
 'LEC 2020 Summer Playoffs']

We will create a custom method for the leaguepedia_parser to get only the information we need :

import types

def get_games_hashes(self, tournament_name=None, **kwargs):
    """
    Returns the list server, gameId and hashes of games played in a tournament.

    :param tournament_name
                Name of the tournament, which can be gotten from get_tournaments().
    :return:
                A list of game dictionaries.
    """
    games = self._cargoquery(tables='ScoreboardGames',
                             fields='Tournament = tournament, '
                                    'MatchHistory = match_history_url, ',
                             where="ScoreboardGames.Tournament='{}'".format(tournament_name),
                             order_by="ScoreboardGames.DateTime_UTC",
                             **kwargs)
    data = [
        {
            "tournament":game["tournament"],
            "server":game["match_history_url"].split("/")[5],
            "gameId":game["match_history_url"].split("/")[6].split("?gameHash=")[0],
            "hash":game["match_history_url"].split("/")[6].split("?gameHash=")[1],
        }
        for game in games
    ]
    return data

lp.get_games_hashes = types.MethodType(get_games_hashes, lp)

Getting the hashes

Getting the hashes for LEC 2020 Summer :

1 2	games = lp.get_games_hashes(tournaments[3]['name']) games[:3]

[{'tournament': 'LEC 2020 Summer',
  'server': 'ESPORTSTMNT04',
  'gameId': '1230688',
  'hash': '25cb7e1966cbcdb5'},
 {'tournament': 'LEC 2020 Summer',
  'server': 'ESPORTSTMNT04',
  'gameId': '1220706',
  'hash': 'c3f45e5bb2a65c80'},
 {'tournament': 'LEC 2020 Summer',
  'server': 'ESPORTSTMNT04',
  'gameId': '1220728',
  'hash': '4bfd5c00f9292be3'}]

Requesting the match data from all those games :

import requests

base_match_history_stats_url = "https://acs.leagueoflegends.com/v1/stats/game/{}/{}?gameHash={}"
base_match_history_stats_timeline_url = "https://acs.leagueoflegends.com/v1/stats/game/{}/{}/timeline?gameHash={}"

all_games_data = []

for g in games:
    url = base_match_history_stats_url.format(g["server"],g["gameId"],g["hash"])
    timeline_url = base_match_history_stats_timeline_url.format(g["server"],g["gameId"],g["hash"])
    
    game_data = requests.get(url).json()
    game_data["timeline"] = requests.get(timeline_url).json()
    
    all_games_data.append(game_data)

If you get rate limited (errors 429), follow this guide : https://www.hextechdocs.dev/lol/esportsapi/13.esports-match-data#in-need-of-cookies

Analysis

Once the data is ehre, let’s crunch some numbers. First of all, you’ll need to select a few specific pieces of information out of the whole match data. These functions will help for that :

# Team stats
# Get each team total damages dealt to champions
def get_team_damages_to_champions(g):
    t_dam = {100:0,200:0}
    for p in g["participants"]:
        t_dam[p["teamId"]] += p["stats"]["totalDamageDealtToChampions"]
    return t_dam

# Get the total kills of the team, as this is only available player by player
def get_team_kills(g):
    t_kills = {100:0,200:0}
    for p in g["participants"]:
        t_kills[p["teamId"]] += p["stats"]["kills"]
    return t_kills

# Participant stats
# Get the Gold Advantage of a player at the 15th minute
def ga_at_15(g, pId):
    g_at_15 = [p["totalGold"] for p in g["timeline"]["frames"][15]["participantFrames"].values() if p["participantId"] == pId][0]
    opp_pId = (pId+5)%10 if not pId == 5 else 10
    opp_g_at_15 = [p["totalGold"] for p in g["timeline"]["frames"][15]["participantFrames"].values() if p["participantId"] == opp_pId][0]
    return g_at_15 - opp_g_at_15

# Get the CS Difference of a player at the 15th minute
def cs_diff_at_15(g, pId):
    cs_at_15 = [p["minionsKilled"]+p["jungleMinionsKilled"] for p in g["timeline"]["frames"][15]["participantFrames"].values() if p["participantId"] == pId][0]
    opp_pId = (pId+5)%10 if not pId == 5 else 10
    opp_cs_at_15 = [p["minionsKilled"]+p["jungleMinionsKilled"] for p in g["timeline"]["frames"][15]["participantFrames"].values() if p["participantId"] == opp_pId][0]
    return cs_at_15 - opp_cs_at_15

Loading ddragon module to translate championId to their name. Note : you need nest_asyncio only if you run this in a Jupyter Notebook.

import nest_asyncio
nest_asyncio.apply()

import static_data
dd = static_data.ddragon()

Gather stats from each player in each game.

players_stats = []

for g in all_games_data:
    
    # Get the teams stats
    teams_damages_to_champions = get_team_damages_to_champions(g)
    teams_kills = get_team_kills(g)
    
    
    # Translate participantId to the player's name
    pId_to_name = {}
    for p in g["participantIdentities"]:
        pId_to_name[p["participantId"]] = p["player"]["summonerName"]
    
    for p in g["participants"]:
        players_stats.append({
            "name":pId_to_name[p["participantId"]],
            "champion":dd.getChampion(p["championId"]).name,
            "damage_share":p["stats"]["totalDamageDealtToChampions"] / teams_damages_to_champions[p["teamId"]],
            "kill_participation":(p["stats"]["kills"] + p["stats"]["assists"]) / teams_kills[p["teamId"]] if teams_kills[p["teamId"]] > 0 else 0,
            "cs_diff_at_15":cs_diff_at_15(g, p["participantId"]),
            "ga_at_15":ga_at_15(g, p["participantId"]),
            "kills":p["stats"]["kills"],
            "deaths":p["stats"]["deaths"],
            "assists":p["stats"]["assists"],
            "vision_score":p["stats"]["visionScore"],
            "damage_per_gold":p["stats"]["totalDamageDealtToChampions"] / p["stats"]["goldEarned"],
            "win":p["stats"]["win"]
        })

Put the stats into a pandas DataFrame.

1
2
3

import pandas as pd
df = pd.DataFrame(players_stats)
df.head()

	name	champion	damage_share	kill_participation	cs_diff_at_15	ga_at_15	kills	deaths	assists	vision_score	damage_per_gold	win
0	G2 Wunder	Mordekaiser	0.207249	0.454545	8	955	6	1	4	32	0.877160	True
1	G2 Jankos	Rek'Sai	0.103866	0.318182	10	-67	1	2	6	64	0.540224	True
2	G2 Caps	Zoe	0.184098	0.636364	-30	37	5	1	9	34	0.844762	True
3	G2 Perkz	Varus	0.421698	0.818182	37	1680	9	0	9	26	1.516857	True
4	G2 Mikyx	Tahm Kench	0.083089	0.545455	8	650	1	1	11	80	0.566123	True

Group the mean of each stat and the number of games they played in.

1 2	df_results = pd.concat([df.groupby("name").mean(), df.groupby("name").agg(Games=("kills","count"))], axis=1) df_results

	damage_share	kill_participation	cs_diff_at_15	ga_at_15	kills	deaths	assists	vision_score	damage_per_gold	win	Games
name
FNC Bwipo	0.223127	0.593547	-9.722222	-194.111111	2.722222	3.833333	5.166667	32.777778	1.136960	0.500000	18
FNC Hylissang	0.083543	0.596570	-5.888889	140.444444	0.833333	4.555556	7.055556	84.555556	0.667025	0.500000	18
FNC Nemesis	0.247389	0.562223	-3.166667	-190.666667	2.722222	2.388889	4.611111	32.777778	1.105412	0.500000	18
FNC Rekkles	0.258534	0.775209	5.777778	72.444444	3.055556	1.333333	6.333333	36.944444	1.107452	0.500000	18
FNC Selfmade	0.187407	0.710503	11.444444	201.055556	3.000000	2.444444	5.500000	44.611111	0.961258	0.500000	18
G2 Caps	0.293738	0.700959	2.333333	384.222222	4.777778	2.111111	5.277778	31.500000	1.289828	0.611111	18
G2 Jankos	0.135096	0.701737	0.722222	4.722222	2.166667	2.666667	6.833333	50.777778	0.795349	0.611111	18
G2 Mikyx	0.085227	0.574378	8.388889	217.055556	0.777778	2.944444	7.388889	90.888889	0.647563	0.611111	18
G2 P1noy	0.147956	0.560606	-67.500000	-1493.500000	3.500000	2.500000	5.500000	43.500000	1.070342	0.500000	2
G2 Perkz	0.279655	0.557734	-3.437500	-260.187500	3.875000	2.562500	4.812500	37.000000	1.195827	0.625000	16
G2 Wunder	0.220917	0.571255	12.277778	404.777778	2.555556	2.722222	5.055556	31.277778	1.105366	0.611111	18
MAD Carzzy	0.252405	0.665892	-29.722222	-550.055556	3.055556	1.944444	6.722222	44.888889	1.336119	0.666667	18
MAD Humanoid	0.308612	0.676741	8.666667	269.055556	4.388889	2.777778	5.500000	31.888889	1.368744	0.666667	18
MAD Kaiser	0.104116	0.683497	19.555556	368.888889	1.333333	2.000000	8.444444	79.833333	0.794702	0.666667	18
MAD Orome	0.202742	0.556667	-3.777778	173.333333	2.777778	1.833333	5.222222	28.666667	1.038037	0.666667	18
MAD Shad0w	0.132126	0.716630	-8.222222	-109.666667	2.611111	2.555556	7.222222	44.055556	0.794622	0.666667	18
MSF Dan Dan	0.232844	0.593369	-3.666667	-257.555556	2.222222	2.000000	3.388889	30.833333	1.054232	0.388889	18
MSF Doss	0.071463	0.553586	-1.230769	19.692308	0.230769	3.153846	5.769231	81.538462	0.539777	0.384615	13
MSF FEBIVEN	0.285648	0.647716	-0.388889	-303.555556	2.444444	2.611111	4.166667	32.166667	1.293193	0.388889	18
MSF Kobbe	0.269913	0.641566	-2.722222	-4.277778	3.222222	1.833333	3.888889	35.611111	1.092888	0.388889	18
MSF Razork	0.137768	0.723598	7.222222	83.388889	2.000000	3.388889	5.111111	52.555556	0.777673	0.388889	18
MSF denyk	0.079974	0.631178	2.000000	-363.000000	0.800000	4.600000	6.600000	98.800000	0.739018	0.400000	5
OG Alphari	0.248336	0.631274	17.500000	388.944444	2.055556	2.388889	4.055556	35.555556	1.133699	0.333333	18
OG Destiny	0.059221	0.602118	-1.666667	-47.111111	0.666667	2.000000	5.888889	91.888889	0.479166	0.444444	9
OG Jactroll	0.068711	0.688606	-5.555556	-293.555556	0.222222	3.777778	5.444444	97.222222	0.608410	0.222222	9
OG Nukeduck	0.231971	0.704894	-5.111111	-249.500000	2.055556	2.333333	4.444444	35.888889	1.088042	0.333333	18
OG Upset	0.321765	0.723173	9.277778	11.055556	3.388889	1.222222	3.833333	47.500000	1.371337	0.333333	18
OG Xerxe	0.133963	0.688985	0.333333	-125.555556	1.555556	2.166667	4.833333	64.166667	0.802195	0.333333	18
RGE Finn	0.239404	0.599334	-4.333333	230.333333	1.888889	2.055556	6.111111	41.833333	1.118791	0.722222	18
RGE Hans sama	0.282284	0.656932	10.333333	525.111111	4.444444	1.722222	4.555556	42.444444	1.188017	0.722222	18
RGE Inspired	0.098220	0.700881	-4.833333	102.611111	1.666667	1.000000	8.000000	57.611111	0.567715	0.722222	18
RGE Larssen	0.312793	0.720134	11.388889	538.388889	4.777778	1.444444	4.666667	30.222222	1.247148	0.722222	18
RGE Vander	0.067299	0.595965	-5.555556	-98.277778	0.666667	0.944444	7.500000	82.055556	0.569031	0.722222	18
S04 Abbedagge	0.285159	0.706940	0.277778	3.611111	3.500000	2.111111	4.277778	40.833333	1.289298	0.444444	18
S04 Dreams	0.081275	0.671343	0.266667	-36.800000	0.866667	2.400000	7.400000	84.800000	0.678752	0.533333	15
S04 Gilius	0.137355	0.736826	5.833333	442.083333	4.000000	2.083333	6.250000	58.000000	0.814762	0.666667	12
S04 Innaxe	0.384667	0.858586	-24.666667	-1184.000000	2.666667	3.000000	1.666667	39.333333	1.650448	0.000000	3
S04 Lurox	0.097811	0.741703	-7.666667	-228.333333	0.666667	2.500000	2.000000	56.500000	0.522445	0.000000	6
S04 Neon	0.293243	0.727295	-1.600000	-83.800000	2.600000	2.133333	6.266667	39.266667	1.353045	0.533333	15
S04 Nukes	0.062412	0.797980	5.000000	199.000000	0.666667	2.666667	3.000000	106.666667	0.403134	0.000000	3
S04 Odoamne	0.204055	0.665810	4.444444	-25.500000	1.500000	2.555556	6.111111	33.000000	1.016403	0.444444	18
SK Crownshot	0.305696	0.682608	11.888889	264.555556	3.555556	1.833333	4.833333	40.111111	1.248497	0.500000	18
SK Jenax	0.231644	0.560635	-5.222222	-460.388889	2.611111	2.611111	4.444444	32.277778	1.120304	0.500000	18
SK LIMIT	0.089615	0.711859	-8.055556	-138.944444	0.888889	2.500000	7.833333	95.555556	0.699940	0.500000	18
SK Trick	0.114979	0.642871	0.222222	-151.333333	1.666667	2.833333	6.333333	40.666667	0.685385	0.500000	18
SK ZaZee	0.258067	0.749928	-4.166667	-79.666667	3.333333	2.166667	5.666667	33.500000	1.087049	0.500000	18
VIT Cabochard	0.195603	0.555988	-6.777778	-393.833333	1.944444	2.277778	4.000000	36.111111	0.942578	0.388889	18
VIT Comp	0.337604	0.788173	2.222222	94.888889	4.000000	2.277778	4.555556	45.722222	1.281572	0.388889	18
VIT Labrov	0.073434	0.792618	0.611111	-145.833333	0.388889	2.611111	8.222222	103.111111	0.552762	0.388889	18
VIT Milica	0.290508	0.692663	-6.166667	-254.000000	3.111111	1.888889	4.388889	45.000000	1.123236	0.388889	18
VIT Nji	0.092451	0.572866	-9.666667	-6.222222	0.555556	3.111111	4.777778	60.666667	0.463280	0.444444	9
VIT Skeanz	0.113250	0.673732	-7.444444	-270.666667	2.000000	2.666667	5.888889	50.111111	0.686650	0.333333	9
XL Caedrel	0.104300	0.608204	0.333333	-85.388889	1.444444	2.222222	4.055556	52.111111	0.576975	0.444444	18
XL Kryze	0.241151	0.546163	-0.722222	134.000000	1.888889	2.444444	2.944444	30.111111	0.992337	0.444444	18
XL Patrik	0.332169	0.704439	8.944444	250.666667	3.611111	2.055556	3.500000	42.000000	1.320411	0.444444	18
XL Special	0.233506	0.608768	-3.666667	-117.888889	2.000000	2.500000	3.833333	35.722222	0.950198	0.444444	18
XL Tore	0.088873	0.677933	-6.166667	-88.888889	0.166667	2.388889	6.388889	92.500000	0.662340	0.444444	18

Then find out who is the best support

1	df_results.sort_values("vision_score", ascending=False)[:5]

	damage_share	kill_participation	cs_diff_at_15	ga_at_15	kills	deaths	assists	vision_score	damage_per_gold	win	Games
name
S04 Nukes	0.062412	0.797980	5.000000	199.000000	0.666667	2.666667	3.000000	106.666667	0.403134	0.000000	3
VIT Labrov	0.073434	0.792618	0.611111	-145.833333	0.388889	2.611111	8.222222	103.111111	0.552762	0.388889	18
MSF denyk	0.079974	0.631178	2.000000	-363.000000	0.800000	4.600000	6.600000	98.800000	0.739018	0.400000	5
OG Jactroll	0.068711	0.688606	-5.555556	-293.555556	0.222222	3.777778	5.444444	97.222222	0.608410	0.222222	9
SK LIMIT	0.089615	0.711859	-8.055556	-138.944444	0.888889	2.500000	7.833333	95.555556	0.699940	0.500000	18

You can also filter out players that didn’t played a lot

1	df_results[df_results["Games"] > 9]

	damage_share	kill_participation	cs_diff_at_15	ga_at_15	kills	deaths	assists	vision_score	damage_per_gold	win	Games
name
FNC Bwipo	0.223127	0.593547	-9.722222	-194.111111	2.722222	3.833333	5.166667	32.777778	1.136960	0.500000	18
FNC Hylissang	0.083543	0.596570	-5.888889	140.444444	0.833333	4.555556	7.055556	84.555556	0.667025	0.500000	18
FNC Nemesis	0.247389	0.562223	-3.166667	-190.666667	2.722222	2.388889	4.611111	32.777778	1.105412	0.500000	18
FNC Rekkles	0.258534	0.775209	5.777778	72.444444	3.055556	1.333333	6.333333	36.944444	1.107452	0.500000	18
FNC Selfmade	0.187407	0.710503	11.444444	201.055556	3.000000	2.444444	5.500000	44.611111	0.961258	0.500000	18
G2 Caps	0.293738	0.700959	2.333333	384.222222	4.777778	2.111111	5.277778	31.500000	1.289828	0.611111	18
G2 Jankos	0.135096	0.701737	0.722222	4.722222	2.166667	2.666667	6.833333	50.777778	0.795349	0.611111	18
G2 Mikyx	0.085227	0.574378	8.388889	217.055556	0.777778	2.944444	7.388889	90.888889	0.647563	0.611111	18
G2 Perkz	0.279655	0.557734	-3.437500	-260.187500	3.875000	2.562500	4.812500	37.000000	1.195827	0.625000	16
G2 Wunder	0.220917	0.571255	12.277778	404.777778	2.555556	2.722222	5.055556	31.277778	1.105366	0.611111	18
MAD Carzzy	0.252405	0.665892	-29.722222	-550.055556	3.055556	1.944444	6.722222	44.888889	1.336119	0.666667	18
MAD Humanoid	0.308612	0.676741	8.666667	269.055556	4.388889	2.777778	5.500000	31.888889	1.368744	0.666667	18
MAD Kaiser	0.104116	0.683497	19.555556	368.888889	1.333333	2.000000	8.444444	79.833333	0.794702	0.666667	18
MAD Orome	0.202742	0.556667	-3.777778	173.333333	2.777778	1.833333	5.222222	28.666667	1.038037	0.666667	18
MAD Shad0w	0.132126	0.716630	-8.222222	-109.666667	2.611111	2.555556	7.222222	44.055556	0.794622	0.666667	18
MSF Dan Dan	0.232844	0.593369	-3.666667	-257.555556	2.222222	2.000000	3.388889	30.833333	1.054232	0.388889	18
MSF Doss	0.071463	0.553586	-1.230769	19.692308	0.230769	3.153846	5.769231	81.538462	0.539777	0.384615	13
MSF FEBIVEN	0.285648	0.647716	-0.388889	-303.555556	2.444444	2.611111	4.166667	32.166667	1.293193	0.388889	18
MSF Kobbe	0.269913	0.641566	-2.722222	-4.277778	3.222222	1.833333	3.888889	35.611111	1.092888	0.388889	18
MSF Razork	0.137768	0.723598	7.222222	83.388889	2.000000	3.388889	5.111111	52.555556	0.777673	0.388889	18
OG Alphari	0.248336	0.631274	17.500000	388.944444	2.055556	2.388889	4.055556	35.555556	1.133699	0.333333	18
OG Nukeduck	0.231971	0.704894	-5.111111	-249.500000	2.055556	2.333333	4.444444	35.888889	1.088042	0.333333	18
OG Upset	0.321765	0.723173	9.277778	11.055556	3.388889	1.222222	3.833333	47.500000	1.371337	0.333333	18
OG Xerxe	0.133963	0.688985	0.333333	-125.555556	1.555556	2.166667	4.833333	64.166667	0.802195	0.333333	18
RGE Finn	0.239404	0.599334	-4.333333	230.333333	1.888889	2.055556	6.111111	41.833333	1.118791	0.722222	18
RGE Hans sama	0.282284	0.656932	10.333333	525.111111	4.444444	1.722222	4.555556	42.444444	1.188017	0.722222	18
RGE Inspired	0.098220	0.700881	-4.833333	102.611111	1.666667	1.000000	8.000000	57.611111	0.567715	0.722222	18
RGE Larssen	0.312793	0.720134	11.388889	538.388889	4.777778	1.444444	4.666667	30.222222	1.247148	0.722222	18
RGE Vander	0.067299	0.595965	-5.555556	-98.277778	0.666667	0.944444	7.500000	82.055556	0.569031	0.722222	18
S04 Abbedagge	0.285159	0.706940	0.277778	3.611111	3.500000	2.111111	4.277778	40.833333	1.289298	0.444444	18
S04 Dreams	0.081275	0.671343	0.266667	-36.800000	0.866667	2.400000	7.400000	84.800000	0.678752	0.533333	15
S04 Gilius	0.137355	0.736826	5.833333	442.083333	4.000000	2.083333	6.250000	58.000000	0.814762	0.666667	12
S04 Neon	0.293243	0.727295	-1.600000	-83.800000	2.600000	2.133333	6.266667	39.266667	1.353045	0.533333	15
S04 Odoamne	0.204055	0.665810	4.444444	-25.500000	1.500000	2.555556	6.111111	33.000000	1.016403	0.444444	18
SK Crownshot	0.305696	0.682608	11.888889	264.555556	3.555556	1.833333	4.833333	40.111111	1.248497	0.500000	18
SK Jenax	0.231644	0.560635	-5.222222	-460.388889	2.611111	2.611111	4.444444	32.277778	1.120304	0.500000	18
SK LIMIT	0.089615	0.711859	-8.055556	-138.944444	0.888889	2.500000	7.833333	95.555556	0.699940	0.500000	18
SK Trick	0.114979	0.642871	0.222222	-151.333333	1.666667	2.833333	6.333333	40.666667	0.685385	0.500000	18
SK ZaZee	0.258067	0.749928	-4.166667	-79.666667	3.333333	2.166667	5.666667	33.500000	1.087049	0.500000	18
VIT Cabochard	0.195603	0.555988	-6.777778	-393.833333	1.944444	2.277778	4.000000	36.111111	0.942578	0.388889	18
VIT Comp	0.337604	0.788173	2.222222	94.888889	4.000000	2.277778	4.555556	45.722222	1.281572	0.388889	18
VIT Labrov	0.073434	0.792618	0.611111	-145.833333	0.388889	2.611111	8.222222	103.111111	0.552762	0.388889	18
VIT Milica	0.290508	0.692663	-6.166667	-254.000000	3.111111	1.888889	4.388889	45.000000	1.123236	0.388889	18
XL Caedrel	0.104300	0.608204	0.333333	-85.388889	1.444444	2.222222	4.055556	52.111111	0.576975	0.444444	18
XL Kryze	0.241151	0.546163	-0.722222	134.000000	1.888889	2.444444	2.944444	30.111111	0.992337	0.444444	18
XL Patrik	0.332169	0.704439	8.944444	250.666667	3.611111	2.055556	3.500000	42.000000	1.320411	0.444444	18
XL Special	0.233506	0.608768	-3.666667	-117.888889	2.000000	2.500000	3.833333	35.722222	0.950198	0.444444	18
XL Tore	0.088873	0.677933	-6.166667	-88.888889	0.166667	2.388889	6.388889	92.500000	0.662340	0.444444	18

You can also target a specific player and get stats on the champion he played

df[df["name"] == "G2 Caps"].groupby("champion").agg(
    played=("name","count"), 
    kill_participation=("kill_participation","mean"), 
    cs_diff_at_15=("cs_diff_at_15","mean"), 
    ga_at_15=("ga_at_15","mean"), 
    winrate=("win","mean")
)

	played	kill_participation	cs_diff_at_15	ga_at_15	winrate
champion
Azir	1	0.833333	13.0	955.0	0.0
Cassiopeia	1	0.714286	37.0	128.0	0.0
Ekko	1	0.750000	-2.0	38.0	1.0
Galio	1	1.000000	-18.0	-1312.0	0.0
Kassadin	1	0.200000	-4.0	-102.0	0.0
Kog'Maw	2	0.787879	-3.5	1119.5	0.5
LeBlanc	1	0.560000	-19.0	-163.0	1.0
Orianna	1	0.777778	6.0	-307.0	0.0
Syndra	2	0.663534	4.5	428.5	1.0
Twisted Fate	2	0.760417	16.0	855.5	1.0
Zoe	5	0.671642	-1.0	574.4	0.8

Now that you have the data and the keys to analyze esports data, make your own stats with more metrics, extend the games analyzed to Playoffs, and even to more regions like LCK and LPL. For other metric, I advise to take a look at this sheet made by LEC analyst that explains the metrics and how to compute them : https://docs.google.com/spreadsheets/d/1hBzgxIPpoBqinOoXCnPvpqUDz4Ja9Lmfm7tqgU3kHRw/edit#gid=1424953934

You may also want to look at last year article : https://hextechlab.com/2019/10/25/worlds2019/
The first part to gather the data is outdated, but the analysis on champions is still working and compatible with the data gathered earlier in this article.

League of Legends lolesports Data analysis

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