Welcome to pyETT’s documentation!

Python API for Eleven VR Table Tennis data.

Getting Started

pyETT: Python library for Eleven VR Table Tennis data.

from pyETT import ett
import pandas as pd

Player and user search

In order to get player’s information we first need to uniquely identify a player. We can use the user_search() method of an object of the class ETT to search players by username, as follows:

eleven = ett.ETT()
eleven.user_search_dataframe('Highlander').head()

	id	name	elo	rank	wins	losses	last_online
0	248681	Highlandersge	1488.0	361225	0	1	2021-03-30T17:29:28.963Z
1	246892	mwwhighlander	1500.0	222424	0	0	2021-01-02T23:00:36.762Z
2	218318	.::Highlander::.	1500.0	179327	0	0	2021-01-07T00:52:31.209Z
3	166876	The Highlander	1530.0	16962	4	1	2020-12-01T20:22:39.752Z
4	384291	highlander10	1500.0	71453	0	0	2021-04-16T22:18:26.874Z

Here, the function returns all players that contain the input username string ‘Highlander’. To search for a perfect match, we can pass the argument perfect_match=True:

eleven.user_search_dataframe('highlanderNJ', perfect_match=True)

	id	name	elo	rank	wins	losses	last_online
0	348353	highlanderNJ	2013.5	2042	475	372	2021-06-30T01:53:32.742Z

In this search, we were able to find the player with username ‘highlanderNJ’ which turns out to be me. We can now instantiate a Player object to obtain his information:

print(p:=ett.Player(348353))

highlanderNJ

print(f"username:{p.name}\n elo:{p.elo}\n rank:{p.rank}\n wins:{p.wins}\n losses:{p.losses}\n")

username:highlanderNJ
 elo:2013.5
 rank:2042
 wins:475
 losses:372

We can see I have an Elo number of 2013.5, which puts me ranked in number 2042 in the world with 475 ranked wins and 372 losses. That’s not entirely bad as the total number of users around 500,000 players!

Matches and rounds

Now that we have instantiated a Player object, we can obtain his matches using the method get_matches() or get_matches_dataframe() to obtain the data in a nice pandas dataframe. These methods return ranked matches by default but you can pass the parameters unranked=True to obtain unranked matches.

m = p.get_matches_dataframe()
m.head()

	created_at	id	ranked	number_of_rounds	state	winning_team	losing_team	home_score	away_score	home_player	away_player	rounds
0	2021-06-30T01:34:17.740Z	9837423	True	3	1	0	1	2	1	highlanderNJ	RandomPerson0504	[<pyETT.ett.Match.Round object at 0x7fcc741398...
1	2021-06-24T15:19:50.324Z	9652764	True	3	1	0	1	2	0	highlanderNJ	ZombieEngineerz8	[<pyETT.ett.Match.Round object at 0x7fcc3cdcfe...
2	2021-06-24T15:09:48.858Z	9652464	True	3	1	0	1	2	0	highlanderNJ	mcstives	[<pyETT.ett.Match.Round object at 0x7fcc3cdcf7...
3	2021-06-23T22:33:16.914Z	9631520	True	3	1	0	1	2	1	ilyap	highlanderNJ	[<pyETT.ett.Match.Round object at 0x7fcc3ce160...
4	2021-06-23T22:26:44.411Z	9631342	True	3	1	0	1	2	0	LearnerForever	highlanderNJ	[<pyETT.ett.Match.Round object at 0x7fcc3ce164...

We can see that my latest ranked match was against the player with username ‘RandomPerson0504’ and I won the match 2 x 1. In order to obtain the rounds information, we can use the method get_rounds_dataframe from the class Match, as follows:

ett.Match.get_rounds_dataframe(m.rounds[0])

	id	round_number	state	away_score	home_score	winner	created_at
0	22061114	0	0	8	11	0	2021-06-30T01:37:16.546Z
1	22061034	0	0	11	7	1	2021-06-30T01:35:59.004Z
2	22060923	0	0	9	11	0	2021-06-30T01:34:17.740Z

Friends list, leaderboard and elo history

From the library, we can also obtain a player’s friends list with the method get_friends() from an object of the Class Player, which also has a corresponding method that returns a list of friends in a pandas dataframe:

p.get_friends_dataframe().head()

	id	name	elo	rank	wins	losses	last_online
0	35497	perseph	1626.0	13492	1208	1883	2021-06-20T22:50:59.471Z
1	431729	JoeSixPaK	1804.5	5542	389	654	2021-06-30T02:15:30.270Z
2	495551	77696c6c	1500.0	165378	0	0	2021-06-14T16:21:31.027Z
3	456082	pete_ed5	1796.0	5807	123	128	2021-06-29T22:45:05.229Z
4	249045	Buttersmier	1897.0	3601	80	74	2021-06-28T21:37:01.090Z

I’m interested in comparing my Elo score with my friends’. For that purpose, we can get a player’s elo history with the method get_elo_history():

p_elo = p.get_elo_history()
p_elo.head()

	elo
2021-02-20T21:13:12.497Z	1500.0
2021-02-24T03:06:30.809Z	1513.0
2021-02-24T03:13:06.512Z	1502.0
2021-02-24T03:16:20.774Z	1489.0
2021-02-24T03:19:46.168Z	1482.0

Let’s now pull the Elo history from 5 of my friends and put it all together in a dataframe including my own elo history and then plot them all:

from functools import reduce

n_friends = 5

friends_elo = [f.get_elo_history().rename(columns={"elo":f.name}) for f in
               p.get_friends()[-n_friends:] if f.get_elo_history() is not None]

friends_elo.append(p.get_elo_history().rename(columns={"elo":p.name}))

group_elo_df = reduce(lambda df1,df2: pd.merge(df1,df2,how='outer', left_index=True, right_index=True),
                      friends_elo).ffill(axis = 0)

group_elo_df

	Konowl77	belmont	Plipplopplip	olofaa	Kennatius	highlanderNJ
2020-12-31T01:14:04.984Z	1500.0	NaN	NaN	NaN	NaN	NaN
2020-12-31T18:45:27.553Z	1493.0	NaN	NaN	NaN	NaN	NaN
2020-12-31T18:50:46.088Z	1476.0	NaN	NaN	NaN	NaN	NaN
2021-01-01T00:34:21.158Z	1468.0	NaN	NaN	NaN	NaN	NaN
2021-01-01T02:38:08.062Z	1453.0	NaN	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...
2021-06-30T01:34:17.740Z	1763.8	1907.2	1728.0	1506.0	1652.0	2005.5
2021-06-30T01:37:59.877Z	1763.8	1907.2	1733.0	1506.0	1652.0	2005.5
2021-06-30T01:44:09.865Z	1763.8	1907.2	1733.0	1506.0	1636.0	2005.5
2021-06-30T01:44:57.569Z	1763.8	1907.2	1736.0	1506.0	1636.0	2005.5
2021-06-30T01:49:24.921Z	1763.8	1907.2	1749.0	1506.0	1636.0	2005.5

4852 rows × 6 columns

group_elo_df.plot(figsize=(10,8))

<matplotlib.axes._subplots.AxesSubplot at 0x7fcc36dcd850>

Overall, my friends are making some good progress. Everyone seems to be improving over time.

Now let’s take a look at the leaderboard by using the method get_leaderboard_dataframe() from our eleven object. By default, this method returns the top 10 players. You can pas

eleven.get_leaderboard_dataframe()

https://www.elevenvr.club/leaderboards/?start=0
https://www.elevenvr.club/leaderboards/?start=10

	id	name	elo	rank	wins	losses	last_online
0	385684	yedat	3258.2	0	967	258	2021-06-28T19:21:18.464Z
1	347650	PROTT_VR	3095.0	1	662	119	2021-06-22T19:37:25.710Z
2	234850	lapoctopus90	3087.1	2	712	246	2021-06-28T17:55:13.346Z
3	289382	gselvas92	3034.0	3	947	347	2021-06-28T18:12:51.853Z
4	372961	sunking73	2995.5	4	599	251	2021-06-20T11:44:49.214Z
5	428341	NPiet	2995.0	5	979	193	2021-06-25T07:22:35.945Z
6	4410	wurstfriedl	2987.0	6	2208	932	2021-06-28T14:49:11.031Z
7	3831	alexttbcn	2980.0	7	1303	618	2021-06-27T18:11:37.414Z
8	11002	twISTer1979	2978.6	8	2411	931	2021-06-28T17:13:18.551Z
9	20585	fmwallin	2960.0	9	1553	931	2021-06-15T08:53:41.094Z

lb_elo = [f.get_elo_history().rename(columns={"elo":f.name}) for f in
               eleven.get_leaderboard() if f.get_elo_history() is not None]

lb_elo.append(p.get_elo_history().rename(columns={"elo":p.name}))

lb_elo_df = reduce(lambda df1,df2: pd.merge(df1,df2,how='outer', left_index=True, right_index=True),
                      lb_elo).ffill(axis = 0)

lb_elo_df.plot(figsize=(10,8))

<matplotlib.axes._subplots.AxesSubplot at 0x7fcc34838100>

The dynamics are quite different compared to the one from my cohort of friends. At the top of the leaderboard, we observe rapid Elo growth across the board. The Top 2 player (PROTT_VR), for instance, moved from a 1500 Elo to an impressive 3000+ Elo in a very short period of time. The number 1 player (yedat) has recently surpassed PROTT_VR also showing an amazing Elo growth. I have added my Elo evolution for comparison (the little blue curve at the bottom) to give a perspective on how competitive the game is at the highest level.

Matchup and Cohort Analysis

from pyETT import ett

The ett Class Cohort manages state, logic and analytics for a cohort of players (instances of the Class Player). Let’s take a look at how the top ETT players in the world match up against each other. First, we create a cohort with the top 10 players from the leaderboard:

lb_cohort = ett.Cohort(ett.ETT().get_leaderboard(num_players=10))
lb_cohort.size

10

lb_cohort.players_dataframe()

	id	name	elo	rank	wins	losses	last_online
0	315909	Aiphaton	3204.0	1	550	70	2021-08-26T18:33:35.943Z
1	347650	PROTT_VR	3134.0	2	715	142	2021-08-28T16:14:23.328Z
2	484532	Seldon36	3114.3	3	699	241	2021-08-30T15:48:03.137Z
3	20585	fmwallin	3072.0	4	1669	971	2021-08-30T12:41:21.933Z
4	234850	Lapoctopus	3069.1	5	759	268	2021-08-20T18:36:38.299Z
5	4410	wurstfriedl	3028.0	6	2364	954	2021-08-27T12:16:32.301Z
6	372961	sunking73	2995.5	7	599	251	2021-06-20T11:44:49.214Z
7	385684	yedat	2994.2	10	1037	297	2021-08-07T05:47:36.647Z
8	11002	twISTer1979	2960.6	9	2542	972	2021-08-28T16:53:27.962Z
9	3831	alexttbcn	2958.0	12	1327	625	2021-08-26T14:10:40.840Z

By accessing the attribute size and calling the methods players_dataframe() from the Cohort object, we obtain the cohort size (10) and the list of players in the cohort. We can get the Elo history of the players in the cohort with the method get_elo_history():

lb_cohort_elo = lb_cohort.get_elo_history()
lb_cohort_elo

	Aiphaton	PROTT_VR	Seldon36	fmwallin	Lapoctopus	wurstfriedl	sunking73	yedat	twISTer1979	alexttbcn
2020-02-29T13:03:53.451Z	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1500.0
2020-02-29T13:09:19.941Z	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1527.0
2020-02-29T13:18:01.313Z	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1521.0
2020-02-29T13:20:46.824Z	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1536.0
2020-02-29T13:27:06.816Z	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	1550.0
...	...	...	...	...	...	...	...	...	...	...
2021-08-30T12:11:47.612Z	3200.0	3138.0	3114.3	3072.0	3074.1	3028.0	3001.5	2995.2	2954.6	2955.0
2021-08-30T12:16:45.042Z	3200.0	3138.0	3117.3	3069.0	3074.1	3028.0	3001.5	2995.2	2954.6	2955.0
2021-08-30T12:23:36.680Z	3200.0	3138.0	3113.3	3073.0	3074.1	3028.0	3001.5	2995.2	2954.6	2955.0
2021-08-30T12:30:05.759Z	3200.0	3138.0	3110.3	3076.0	3074.1	3028.0	3001.5	2995.2	2954.6	2955.0
2021-08-30T12:37:25.487Z	3200.0	3138.0	3112.3	3074.0	3074.1	3028.0	3001.5	2995.2	2954.6	2955.0

14358 rows × 10 columns

lb_cohort_elo.plot(figsize=(10,8))

<matplotlib.axes._subplots.AxesSubplot at 0x7f27d2d5b640>

We can obtain the full list of matches played within the cohort, i.e., match ups, by accessing the method get_matches() or get_matches_dataframe() to obtain results in a pandas DataFrame:

lb_cohort.get_matches_dataframe()

	created_at	id	ranked	number_of_rounds	state	winning_team	losing_team	home_score	away_score	elo_change	home_elo_avg	away_elo_avg	home_player	away_player	rounds
0	2021-07-25T15:03:46.195Z	10674157	True	3	1	1	0	0	0	19.0	3107.0	3038.0	PROTT_VR	Aiphaton	[<pyETT.ett.Match.Round object at 0x7f27d00c09...
1	2021-07-25T15:11:19.296Z	10674382	True	3	1	1	0	0	0	9.0	3088.0	3057.0	PROTT_VR	Aiphaton	[<pyETT.ett.Match.Round object at 0x7f27d00c0b...
2	2021-07-25T15:17:52.938Z	10674591	True	3	1	1	0	0	0	5.0	3079.0	3066.0	PROTT_VR	Aiphaton	[<pyETT.ett.Match.Round object at 0x7f27d00d97...
3	2021-07-25T15:25:15.522Z	10674804	True	3	1	1	0	0	0	2.0	3074.0	3071.0	PROTT_VR	Aiphaton	[<pyETT.ett.Match.Round object at 0x7f27d00d9f...
4	2021-08-25T18:06:09.908Z	11753227	True	3	1	1	0	0	0	13.0	3138.0	3200.0	PROTT_VR	Aiphaton	[<pyETT.ett.Match.Round object at 0x7f27d00d14...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
911	2021-01-22T13:49:59.089Z	3694516	True	3	1	0	1	0	0	11.0	2816.0	2883.0	twISTer1979	alexttbcn	[<pyETT.ett.Match.Round object at 0x7f27ca6aa6...
912	2021-02-20T18:38:54.237Z	4832703	True	3	1	0	1	0	0	15.0	2905.0	2943.0	twISTer1979	alexttbcn	[<pyETT.ett.Match.Round object at 0x7f27ca6aa6...
913	2021-02-20T18:45:13.317Z	4832978	True	3	1	1	0	0	0	8.0	2920.0	2928.0	twISTer1979	alexttbcn	[<pyETT.ett.Match.Round object at 0x7f27ca6aa7...
914	2021-02-20T18:53:27.768Z	4833346	False	3	1	1	0	0	0	0.0	2912.0	2936.0	twISTer1979	alexttbcn	[<pyETT.ett.Match.Round object at 0x7f27ca6aa8...
915	2021-02-20T18:53:48.819Z	4833362	True	3	1	1	0	0	0	6.0	2912.0	2936.0	twISTer1979	alexttbcn	[<pyETT.ett.Match.Round object at 0x7f27ca6aa9...

916 rows × 15 columns

We observed the the top 10 players played an impressive 916 number of matches among each other. To obtain match up analytics of players in the cohort, we can call the method describe():

lb_cohort.describe()

	index	num_matches	wins	losses	win_rate	num_matches_ranked	wins_ranked	losses_ranked	win_rate_ranked	elo	rank
0	Aiphaton	311	208	103	67.0	143	98	45	69.0	3204.0	1
1	yedat	202	125	77	62.0	179	110	69	61.0	2994.2	10
2	wurstfriedl	112	38	74	34.0	23	12	11	52.0	3028.0	6
3	PROTT_VR	227	116	111	51.0	222	115	107	52.0	3134.0	2
4	Seldon36	87	38	49	44.0	34	17	17	50.0	3114.3	3
5	Lapoctopus	242	120	122	50.0	175	80	95	46.0	3069.1	5
6	sunking73	63	31	32	49.0	50	21	29	42.0	2995.5	7
7	fmwallin	212	90	122	42.0	147	59	88	40.0	3072.0	4
8	twISTer1979	249	98	151	39.0	110	39	71	35.0	2960.6	9
9	alexttbcn	127	52	75	41.0	43	12	31	28.0	2958.0	12

We observe that the player PROTTVR (ranked 2) played the most ranked matches against Top 10 ranked players with a total of 222 matches followed by yedat with 179 matches. The player with the highest win rate when playing against other Top 10 ranked players is Aiphaton, the current World’s number 1, followed by yedat, formerly ranked number 1.

Official Tournament

from pyETT import ett

Eleven has been running monthly official tournaments since April/2021. The tournaments draw interest from top players as well as upcoming ones who are looking to become more competitive. After each tournament, each player may gain rankings points and at the end of the year the top ranked players will compete on an year-end tournament to determine who’s the best player of the year. We can obtain the current standinds of the rankings points as follows:

ett.official_tournament_leaderboard_dataframe().head(10)

	Ranking	Player	April	May	June	July	Aug	Total
0	1	Aiphaton	175.0	40.0	90.0	NaN	NaN	305
1	2	lapoctopus90	NaN	90.0	125.0	NaN	NaN	215
2	3	ProTTVR	125.0	60.0	15.0	NaN	NaN	200
3	4	yedat	15.0	125.0	NaN	NaN	NaN	140
4	5	porter	40.0	15.0	60.0	NaN	NaN	115
5	6	ALIASREK	90.0	NaN	10.0	NaN	NaN	100
6	7	twISTer1979	25.0	25.0	40.0	NaN	NaN	90
7	8	PixelFire	60.0	10.0	15.0	NaN	NaN	85
8	9	l4mbo	40.0	15.0	10.0	NaN	NaN	65
9	9	fmwallin	25.0	25.0	15.0	NaN	NaN	65

Not surprisingly, the highest ranked players are at the top of ETT’s leaderboard:

ett.ETT().get_leaderboard_dataframe()

	id	name	elo	rank	wins	losses	last_online
0	385684	yedat	3135.2	0	1001	275	2021-07-08T21:55:09.544Z
1	183811	PROTTVR	3095.0	1	877	165	2021-07-07T17:28:18.291Z
2	234850	lapoctopus90	3087.1	2	723	250	2021-07-06T18:19:59.324Z
3	20585	fmwallin	3018.0	3	1603	943	2021-07-08T20:14:53.095Z
4	289382	gselvas92	3002.0	4	986	364	2021-07-08T18:02:14.002Z
5	372961	sunking73	2995.5	5	599	251	2021-06-20T11:44:49.214Z
6	428341	NPiet	2995.0	6	979	193	2021-06-25T07:22:35.945Z
7	11002	twISTer1979	2990.6	7	2455	941	2021-07-08T18:27:03.773Z
8	4410	wurstfriedl	2989.0	8	2212	932	2021-07-08T16:24:15.480Z
9	3831	alexttbcn	2980.0	9	1303	618	2021-07-02T09:41:40.627Z

Players with an Elo rating greater than 2000 at any day of the preceding month have direct entry to the tournament. Players who had an Elo under 2000 at any point in the previous month, can qualify by entering a qualifying tournament. We can verify if a player have direct entry or can qualify by instantiating an object of the Class Tournament as follows:

players = [ett.Player(id) for id in [357217, 348353, 12345]]
ett.Tournament(players).qualify(elo_min=2000, start="2021-06-01", end="2021-06-30")

	mean	min	max	direct_entry	can_qualify	id	name
0	1863.069231	1743.8	1974.8	0	1	357217	Cyrillus82
1	1984.258741	1923.5	2019.5	1	1	348353	highlanderNJ
2	1500.000000	1500.0	1500.0	0	1	12345	PastaAndPasta

We observe that the player highlanderNJ have direct entry to July’s official tournament as his max Elo within June is over 2000. He also has the option to play in the qualifying tournament as his min Elo rating in June is under 2000. The players Cyrillus82 and PastaAndPasta can only enter the qualifying tournament as their max Elo in June did not surpass the 2000 threshold.

An app is available at https://ett-monthly.herokuapp.com/ offering an easy to use UI constructed on top of this library, currently in use by the organizers of the official tournament when validating player entry.

pyETT

pyETT package

Submodules

pyETT.ett module

class pyETT.ett.Cohort(players)

Bases: object

A Class to represent a cohort of players

describe() → pandas.core.frame.DataFrame

Returns a dataframe with descriptive statistics of the players in the cohort considering only matches among themselves, such as win, losses and win rate. Additional player specific attributes are added to the dataframe, such as name, elo and rank.

Returns

Players matchup stats in the cohort.

Return type

pd.DataFrame

get_elo_history()

Returns Elo history of the players in the cohort

get_matches(unranked: bool = False) → List[pyETT.ett.Match]

Returns matches among players in the cohort

get_matches_dataframe(unranked: bool = False) → pandas.core.frame.DataFrame

Returns a dataframe containing all matches of the players in the cohort.

players_dataframe()

Returns cohort’s players in a dataframe

class pyETT.ett.ETT

Bases: object

A class to represent Eleven Table Tennis (ETT).

get_leaderboard(num_players: int = 10) → List[pyETT.ett.Player]

Returns a list of players from the leaderboard.

Parameters

num_players (int, optional) – Max number of players to return. Defaults to 10.

Returns

Top players in ETT’s leaderboard.

Return type

List[Player]

get_leaderboard_dataframe(num_players: int = 10) → pandas.core.frame.DataFrame

Returns a pandas dataframe with players from the leaderboard.

Parameters

num_players (int, optional) – Max number of players to return. Defaults to 10.

Returns

Top players in ETT’s leaderboard.

Return type

pd.DataFrame

user_search(username: str, perfect_match: bool = False) → List[pyETT.ett.Player]

Returns a list of players whose name contains username, if perfect_match is False. Otherwise, it returns a list of players whose username is a perfect match with username.

Parameters

• username (str) – Username

• perfect_match (bool, optional) – Whether to perform perfect match in string matching. Defaults to False.

Returns

List of players whose names match with username.

Return type

List[Player]

user_search_dataframe(username: str, perfect_match: bool = False) → pandas.core.frame.DataFrame

Returns a list of players whose name contains username, if perfect_match is False. Otherwise, it returns a list of players whose usernames is a perfect match with username.

Parameters

• username (str) – Username

• perfect_match (bool, optional) – Whether to perform perfect match in string matching. Defaults to False.

Returns

A dataframe of players whose names match with username.

Return type

pd.DataFrame

class pyETT.ett.Match(match_id, match)

Bases: object

A class to represent a Match.

class Round(round_attributes)

Bases: object

A class to represent a round of a Match.

get_rounds_dataframe() → pandas.core.frame.DataFrame

Converts a list of rounds to a DataFrame :param rounds: List of rounds. :type rounds: List[“Round”]

Returns

Dataframe with rounds, one per row.

Return type

pd.DataFrame

print()

Pretty-print match’s information

class pyETT.ett.Player(user_id, player=None, legacy_api=False)

Bases: object

A class to represent a Player.

AWAY = 1

HOME = 0

get_elo_history() → pandas.core.frame.DataFrame

Returns player’s elo score history.

Returns

[description]

Return type

pd.DataFrame

get_friends() → List[pyETT.ett.Player]

Return a player’s friends list

get_friends_dataframe() → pandas.core.frame.DataFrame

Return a player’s friends list in a dataframe

get_matches(unranked: bool = False) → List[pyETT.ett.Match]

Return player’s matches.

get_matches_dataframe(unranked: bool = False) → pandas.core.frame.DataFrame

Return player’s matches in a pandas dataframe.

get_matches_revertible() → List[numpy.int64]

Returns player’s lost matches that are eligible to be reverted/cancelled. If you have an incomplete match because of connection issues or because your opponent left in the middle of the play, you can request an Eleven Moderator (Discord) to revert/cancel the match and hence update your Elo rating.

Returns

List of matches ids that are eligible to be reverted/cancelled.

Return type

List[np.int64]

print_matches_revertible()

Pretty print player’s lost matches that are eligible to be reverted/cancelled. If you have an incomplete match because of connection issues or because your opponent left in the middle of the play, you can request an Eleven Moderator (Discord) to revert/cancel the match and hence update your Elo rating

class pyETT.ett.Tournament(players)

Bases: object

A Class to handle ETT official tournaments

qualify(elo_min: float, start: str, end: str) → pandas.core.frame.DataFrame

Implements logic to enter or qualify to ETT’s official monthly tournament. Players with an Elo rating exceeding elo_min at any point between start and end date have direct entry to the tournament. Otherwise, they can enter a Qualifying Tournament to try and qualify. This method returns a dataframe indicating which players have direct entry or can qualify.

Parameters

• elo_min (float) – Elo threshold to have direct entry to the Tournament.

• start (str) – Start date (YYYY-MM-DD)

• end (str) – End date (YYYY-MM-DD)

Returns

Data frame of players with the following information:

• mean: Player’s mean Elo between start and end

• min: Player’s min Elo between start and end`

• max: Player’s max Elo between start and end`

• direct_entry: Boolean indicating whether player have direct entry to the Tournament

• can_qualify: Boolean indicating whether player can enter Qualifying Tournament

• id: Player’s id

• name: Player’s username

Return type

pd.DataFrame

pyETT.ett.official_tournament_leaderboard_dataframe() → pandas.core.frame.DataFrame

Returns a pandas dataframe with the leaderboard of the Eleven official tournaments available at http://lavadesignstudio.co.uk/eleven-rankings/.

Returns

A dataframe with the leaderboard of Eleven official tournaments.

Return type

pd.DataFrame

pyETT.ett.print_match(match: pyETT.ett.Match)

Pretty print a match with rounds.

Parameters

match (Match) – A match.

pyETT.ett_parser module

pyETT.ett_parser.get_elo_history(user_id) → List

pyETT.ett_parser.get_friends(user_id) → List

pyETT.ett_parser.get_leaderboard(num_players=10)

pyETT.ett_parser.get_leaderboard_official_tournament() → List

pyETT.ett_parser.get_matches(user_id, unranked=False) → List

pyETT.ett_parser.get_matchup(user_id1, user_id2) → Iterator

pyETT.ett_parser.get_or_create_eventloop()

pyETT.ett_parser.get_user(user_id) → List

pyETT.ett_parser.user_search(username) → List

Module contents

Installation

From PyPI

$ pip install pyETT

Collaborate

Fork me at https://github.com/souzatharsis/pyETT.

Indices and tables

• Index

• Module Index

• Search Page

Licensed under Attribution-NonCommercial-ShareAlike 4.0 International.