Initialize a GameDataset and some sample operations¶

A GameDatset instance can contain tracking and events data in a TrackingFrame and EventsFrame respectively.

In [2]:

import sys
sys.path.insert(1, '../../')

from codeball import GameDataset, Zones

metadata_file = (r"../../codeball/tests/files/metadata.xml")
tracking_file = (r"../../codeball/tests/files/tracking.txt")
events_file = (r"../../codeball/tests/files/events.json")

game_dataset = GameDataset(
    tracking_metadata_file=metadata_file,
    tracking_data_file=tracking_file,
    events_metadata_file=metadata_file,
    events_data_file=events_file,
)

print(type(game_dataset.tracking))
print(type(game_dataset.events))

import sys sys.path.insert(1, '../../') from codeball import GameDataset, Zones metadata_file = (r"../../codeball/tests/files/metadata.xml") tracking_file = (r"../../codeball/tests/files/tracking.txt") events_file = (r"../../codeball/tests/files/events.json") game_dataset = GameDataset( tracking_metadata_file=metadata_file, tracking_data_file=tracking_file, events_metadata_file=metadata_file, events_data_file=events_file, ) print(type(game_dataset.tracking)) print(type(game_dataset.events))

<class 'codeball.codeball_frames.TrackingFrame'>
<class 'codeball.codeball_frames.EventsFrame'>

Tracking¶

GameDataset.tracking holds a TrackingFrame with all the tacking data of the game.

In [7]:

game_dataset.tracking.head()

game_dataset.tracking.head()

Out[7]:

	period_id	timestamp	ball_state	ball_owning_team_id	ball_x	ball_y	P3578_x	P3578_y	P3568_x	P3568_y	...	P3590_x	P3590_y	P3591_x	P3591_y	P3592_x	P3592_y	P3593_x	P3593_y	P3594_x	P3594_y
0	1	0.04	None	None	NaN	NaN	0.84722	0.52855	0.65268	0.24792	...	0.41381	0.52790	0.41787	0.48086	0.41215	0.36689	0.47050	0.73219	0.48864	0.36357
1	1	0.08	None	None	NaN	NaN	0.84722	0.52855	0.65231	0.24513	...	0.41375	0.52780	0.41719	0.47864	0.41132	0.36169	0.47040	0.73204	0.48834	0.36362
2	1	0.12	None	None	NaN	NaN	0.84722	0.52855	0.65197	0.24387	...	0.41371	0.52906	0.41697	0.47824	0.41131	0.36072	0.47075	0.73229	0.48814	0.36372
3	1	0.16	None	None	NaN	NaN	0.84722	0.52855	0.65166	0.24288	...	0.41370	0.53056	0.41685	0.47815	0.41117	0.35930	0.47118	0.73266	0.48793	0.36278
4	1	0.20	None	None	NaN	NaN	0.84722	0.52855	0.65141	0.24251	...	0.41369	0.53151	0.41669	0.47749	0.41120	0.35910	0.47163	0.73287	0.48784	0.36240

5 rows × 50 columns

If you want to filter the TrackingFrame, you can use it's methods (on top of all standard DataFrame methods). For example to get a TrackingFrame only with the data of team with team_id FIFATMA you can do:

In [8]:

game_dataset.tracking.team('FIFATMA').head()

game_dataset.tracking.team('FIFATMA').head()

Out[8]:

	P3578_x	P3578_y	P3568_x	P3568_y	P3569_x	P3569_y	P3570_x	P3570_y	P3571_x	P3571_y	...	P3573_x	P3573_y	P3574_x	P3574_y	P3575_x	P3575_y	P3576_x	P3576_y	P3577_x	P3577_y
0	0.84722	0.52855	0.65268	0.24792	0.66525	0.46562	0.68103	0.59083	0.62405	0.80669	...	0.60798	0.45155	0.50212	0.45314	0.62012	0.60667	0.51839	0.77140	0.50555	0.50863
1	0.84722	0.52855	0.65231	0.24513	0.66482	0.46548	0.68095	0.59054	0.62371	0.80594	...	0.60783	0.44918	0.50158	0.45544	0.61987	0.60474	0.51801	0.77130	0.50545	0.50532
2	0.84722	0.52855	0.65197	0.24387	0.66467	0.46537	0.68078	0.59035	0.62354	0.80601	...	0.60779	0.44866	0.50126	0.45662	0.61980	0.60422	0.51787	0.77080	0.50552	0.50524
3	0.84722	0.52855	0.65166	0.24288	0.66460	0.46488	0.68063	0.58987	0.62318	0.80604	...	0.60762	0.44898	0.50119	0.45815	0.61976	0.60397	0.51773	0.77031	0.50563	0.50524
4	0.84722	0.52855	0.65141	0.24251	0.66452	0.46469	0.68052	0.58934	0.62286	0.80626	...	0.60748	0.44888	0.50114	0.45986	0.61967	0.60417	0.51759	0.77008	0.50576	0.50531

5 rows × 22 columns

Final example, let's say you want to get the x coordiante data, only for the field players (excluding goalkeeper) for team_id FIFATMA, you can get that by doing:

In [6]:

game_dataset.tracking.team('FIFATMA').players('field').dimension('x').head()

game_dataset.tracking.team('FIFATMA').players('field').dimension('x').head()

Out[6]:

	P3568_x	P3569_x	P3570_x	P3571_x	P3572_x	P3573_x	P3574_x	P3575_x	P3576_x	P3577_x
0	0.65268	0.66525	0.68103	0.62405	0.50533	0.60798	0.50212	0.62012	0.51839	0.50555
1	0.65231	0.66482	0.68095	0.62371	0.50461	0.60783	0.50158	0.61987	0.51801	0.50545
2	0.65197	0.66467	0.68078	0.62354	0.50430	0.60779	0.50126	0.61980	0.51787	0.50552
3	0.65166	0.66460	0.68063	0.62318	0.50394	0.60762	0.50119	0.61976	0.51773	0.50563
4	0.65141	0.66452	0.68052	0.62286	0.50371	0.60748	0.50114	0.61967	0.51759	0.50576

Events¶

Similarly, GameDataset.events holds a TrackingFrame with all the tacking data of the game, and if you want to filter it, you can do so using it's methods. For example to get all event that go into the opponent box you can do game_dataset.events.into(Zones.OPPONENT_BOX), or if you want to get all the passes you can do:

In [15]:

game_dataset.events.type('PASS').head()

game_dataset.events.type('PASS').head()

Out[15]:

	event_id	event_type	result	success	period_id	timestamp	end_timestamp	ball_state	ball_owning_team	team_id	player_id	coordinates_x	coordinates_y	end_coordinates_x	end_coordinates_y	receiver_player_id	inverted
1	None	PASS	COMPLETE	True	1	14.44	15.08	alive	FIFATMA	FIFATMA	P3577	0.49875	0.51275	0.50136	0.51295	P3574	True
3	None	PASS	COMPLETE	True	1	15.36	17.04	alive	FIFATMA	FIFATMA	P3574	0.50300	0.51500	0.36627	0.36551	P3575	True
5	None	PASS	COMPLETE	True	1	18.60	20.28	alive	FIFATMA	FIFATMA	P3575	0.33014	0.40293	0.19398	0.60179	P3569	True
7	None	PASS	COMPLETE	True	1	21.20	23.20	alive	FIFATMA	FIFATMA	P3569	0.19071	0.57078	0.20094	0.18478	P3570	True
9	None	PASS	COMPLETE	True	1	23.92	25.12	alive	FIFATMA	FIFATMA	P3570	0.20244	0.18002	0.31899	0.01941	P3571	True

Since in this game tracking and event daa come from the same provider, GameDataset.metadata for this case is the same as GameDataset.tracking.metadata and GameDataset.events.metadata. There ou can access metadata about the data like frame rate, field dimensions, teams and players details, etc. Like:

In [23]:

game_dataset.metadata.teams[0].players[5].name

game_dataset.metadata.teams[0].players[5].name

Out[23]:

'Player 5'

In [24]:

game_dataset.metadata.frame_rate

game_dataset.metadata.frame_rate

Out[24]:

25

In [25]:

game_dataset.metadata.score

game_dataset.metadata.score

Out[25]:

Score(home=0, away=2)

For more details about metadata attributes and methods see kloppy's documentation.