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"""Implements serializers for StatsBomb data."""
import os
from typing import Any, Optional, cast
import pandas as pd # type: ignore
from pandera.typing import DataFrame
try:
from statsbombpy import sb
except ImportError:
sb = None
from socceraction.data.base import (
EventDataLoader,
ParseError,
_expand_minute,
_localloadjson,
)
from .schema import (
StatsBombCompetitionSchema,
StatsBombEventSchema,
StatsBombGameSchema,
StatsBombPlayerSchema,
StatsBombTeamSchema,
)
class StatsBombLoader(EventDataLoader):
"""Load Statsbomb data either from a remote location or from a local folder.
To load remote data, this loader uses the `statsbombpy
<https://github.com/statsbomb/statsbombpy>`__ package. Data can be retrieved
from the StatsBomb API and from the `Open Data GitHub repo
<https://github.com/statsbomb/open-data/>`__.
API access is for paying customers only. Authentication can be done by
setting environment variables named ``SB_USERNAME`` and ``SB_PASSWORD`` to
your login credentials. Alternatively, pass your login credentials using
the ``creds`` parameter.
StatsBomb's open data can be accessed without the need of authentication
but its use is subject to a `user agreement
<https://github.com/statsbomb/open-data/blob/master/LICENSE.pdf>`__.
To load local data, point ``root`` to the root folder of the data. This folder
should use the same directory structure as used in the Open Data GitHub repo.
Parameters
----------
getter : str
"remote" or "local"
root : str, optional
Root-path of the data. Only used when getter is "local".
creds: dict, optional
Login credentials in the format {"user": "", "passwd": ""}. Only used
when getter is "remote".
"""
def __init__(
self,
getter: str = "remote",
root: Optional[str] = None,
creds: Optional[dict[str, str]] = None,
) -> None:
if getter == "remote":
if sb is None:
raise ImportError(
"""The 'statsbombpy' package is required. Install with 'pip install statsbombpy'."""
)
self._creds = creds or sb.DEFAULT_CREDS
self._local = False
elif getter == "local":
if root is None:
raise ValueError("""The 'root' parameter is required when loading local data.""")
self._local = True
self._root = root
else:
raise ValueError("Invalid getter specified")
def competitions(self) -> DataFrame[StatsBombCompetitionSchema]:
"""Return a dataframe with all available competitions and seasons.
Raises
------
ParseError
When the raw data does not adhere to the expected format.
Returns
-------
pd.DataFrame
A dataframe containing all available competitions and seasons. See
:class:`~socceraction.spadl.statsbomb.StatsBombCompetitionSchema` for the schema.
"""
cols = [
"season_id",
"competition_id",
"competition_name",
"country_name",
"competition_gender",
"season_name",
]
if self._local:
obj = _localloadjson(str(os.path.join(self._root, "competitions.json")))
else:
obj = list(sb.competitions(fmt="dict", creds=self._creds).values())
if not isinstance(obj, list):
raise ParseError("The retrieved data should contain a list of competitions")
if len(obj) == 0:
return cast(DataFrame[StatsBombCompetitionSchema], pd.DataFrame(columns=cols))
return cast(DataFrame[StatsBombCompetitionSchema], pd.DataFrame(obj)[cols])
def games(self, competition_id: int, season_id: int) -> DataFrame[StatsBombGameSchema]:
"""Return a dataframe with all available games in a season.
Parameters
----------
competition_id : int
The ID of the competition.
season_id : int
The ID of the season.
Raises
------
ParseError
When the raw data does not adhere to the expected format.
Returns
-------
pd.DataFrame
A dataframe containing all available games. See
:class:`~socceraction.spadl.statsbomb.StatsBombGameSchema` for the schema.
"""
cols = [
"game_id",
"season_id",
"competition_id",
"competition_stage",
"game_day",
"game_date",
"home_team_id",
"away_team_id",
"home_score",
"away_score",
"venue",
"referee",
]
if self._local:
obj = _localloadjson(
str(os.path.join(self._root, "matches", f"{competition_id}", f"{season_id}.json"))
)
else:
obj = list(
sb.matches(competition_id, season_id, fmt="dict", creds=self._creds).values()
)
if not isinstance(obj, list):
raise ParseError("The retrieved data should contain a list of games")
if len(obj) == 0:
return cast(DataFrame[StatsBombGameSchema], pd.DataFrame(columns=cols))
gamesdf = pd.DataFrame(_flatten(m) for m in obj)
gamesdf["kick_off"] = gamesdf["kick_off"].fillna("12:00:00.000")
gamesdf["match_date"] = pd.to_datetime(
gamesdf[["match_date", "kick_off"]].agg(" ".join, axis=1)
)
gamesdf.rename(
columns={
"match_id": "game_id",
"match_date": "game_date",
"match_week": "game_day",
"stadium_name": "venue",
"referee_name": "referee",
"competition_stage_name": "competition_stage",
},
inplace=True,
)
if "venue" not in gamesdf:
gamesdf["venue"] = None
if "referee" not in gamesdf:
gamesdf["referee"] = None
return cast(DataFrame[StatsBombGameSchema], gamesdf[cols])
def _lineups(self, game_id: int) -> list[dict[str, Any]]:
if self._local:
obj = _localloadjson(str(os.path.join(self._root, "lineups", f"{game_id}.json")))
else:
obj = list(sb.lineups(game_id, fmt="dict", creds=self._creds).values())
if not isinstance(obj, list):
raise ParseError("The retrieved data should contain a list of teams")
if len(obj) != 2:
raise ParseError("The retrieved data should contain two teams")
return obj
def teams(self, game_id: int) -> DataFrame[StatsBombTeamSchema]:
"""Return a dataframe with both teams that participated in a game.
Parameters
----------
game_id : int
The ID of the game.
Raises
------
ParseError # noqa: DAR402
When the raw data does not adhere to the expected format.
Returns
-------
pd.DataFrame
A dataframe containing both teams. See
:class:`~socceraction.spadl.statsbomb.StatsBombTeamSchema` for the schema.
"""
cols = ["team_id", "team_name"]
obj = self._lineups(game_id)
return cast(DataFrame[StatsBombTeamSchema], pd.DataFrame(obj)[cols])
def players(self, game_id: int) -> DataFrame[StatsBombPlayerSchema]:
"""Return a dataframe with all players that participated in a game.
Parameters
----------
game_id : int
The ID of the game.
Raises
------
ParseError # noqa: DAR402
When the raw data does not adhere to the expected format.
Returns
-------
pd.DataFrame
A dataframe containing all players. See
:class:`~socceraction.spadl.statsbomb.StatsBombPlayerSchema` for the schema.
"""
cols = [
"game_id",
"team_id",
"player_id",
"player_name",
"nickname",
"jersey_number",
"is_starter",
"starting_position_id",
"starting_position_name",
"minutes_played",
]
obj = self._lineups(game_id)
playersdf = pd.DataFrame(_flatten_id(p) for lineup in obj for p in lineup["lineup"])
playergamesdf = extract_player_games(self.events(game_id))
playersdf = pd.merge(
playersdf,
playergamesdf[
["player_id", "team_id", "position_id", "position_name", "minutes_played"]
],
on="player_id",
)
playersdf["game_id"] = game_id
playersdf["position_name"] = playersdf["position_name"].replace(0, "Substitute")
playersdf["position_id"] = playersdf["position_id"].fillna(0).astype(int)
playersdf["is_starter"] = playersdf["position_id"] != 0
playersdf.rename(
columns={
"player_nickname": "nickname",
"country_name": "country",
"position_id": "starting_position_id",
"position_name": "starting_position_name",
},
inplace=True,
)
return cast(DataFrame[StatsBombPlayerSchema], playersdf[cols])
def events(self, game_id: int, load_360: bool = False) -> DataFrame[StatsBombEventSchema]:
"""Return a dataframe with the event stream of a game.
Parameters
----------
game_id : int
The ID of the game.
load_360 : bool
Whether to load the 360 data.
Raises
------
ParseError
When the raw data does not adhere to the expected format.
Returns
-------
pd.DataFrame
A dataframe containing the event stream. See
:class:`~socceraction.spadl.statsbomb.StatsBombEventSchema` for the schema.
"""
cols = [
"game_id",
"event_id",
"period_id",
"team_id",
"player_id",
"type_id",
"type_name",
"index",
"timestamp",
"minute",
"second",
"possession",
"possession_team_id",
"possession_team_name",
"play_pattern_id",
"play_pattern_name",
"team_name",
"duration",
"extra",
"related_events",
"player_name",
"position_id",
"position_name",
"location",
"under_pressure",
"counterpress",
]
# Load the events
if self._local:
obj = _localloadjson(str(os.path.join(self._root, "events", f"{game_id}.json")))
else:
obj = list(sb.events(game_id, fmt="dict", creds=self._creds).values())
if not isinstance(obj, list):
raise ParseError("The retrieved data should contain a list of events")
if len(obj) == 0:
return cast(DataFrame[StatsBombEventSchema], pd.DataFrame(columns=cols))
eventsdf = pd.DataFrame(_flatten_id(e) for e in obj)
eventsdf["match_id"] = game_id
eventsdf["timestamp"] = pd.to_timedelta(eventsdf["timestamp"])
eventsdf["related_events"] = eventsdf["related_events"].apply(
lambda d: d if isinstance(d, list) else []
)
eventsdf["under_pressure"] = eventsdf["under_pressure"].fillna(False).astype(bool)
eventsdf["counterpress"] = eventsdf["counterpress"].fillna(False).astype(bool)
eventsdf.rename(
columns={"id": "event_id", "period": "period_id", "match_id": "game_id"},
inplace=True,
)
if not load_360:
return cast(DataFrame[StatsBombEventSchema], eventsdf[cols])
# Load the 360 data
cols_360 = ["visible_area_360", "freeze_frame_360"]
if self._local:
obj = _localloadjson(str(os.path.join(self._root, "three-sixty", f"{game_id}.json")))
else:
obj = sb.frames(game_id, fmt="dict", creds=self._creds)
if not isinstance(obj, list):
raise ParseError("The retrieved data should contain a list of frames")
if len(obj) == 0:
eventsdf["visible_area_360"] = None
eventsdf["freeze_frame_360"] = None
return cast(DataFrame[StatsBombEventSchema], eventsdf[cols + cols_360])
framesdf = pd.DataFrame(obj).rename(
columns={
"event_uuid": "event_id",
"visible_area": "visible_area_360",
"freeze_frame": "freeze_frame_360",
},
)[["event_id", "visible_area_360", "freeze_frame_360"]]
return cast(
DataFrame[StatsBombEventSchema],
pd.merge(eventsdf, framesdf, on="event_id", how="left")[cols + cols_360],
)
def extract_player_games(events: pd.DataFrame) -> pd.DataFrame:
"""Extract player games [player_id, game_id, minutes_played] from statsbomb match events.
Parameters
----------
events : pd.DataFrame
DataFrame containing StatsBomb events of a single game.
Returns
-------
player_games : pd.DataFrame
A DataFrame with the number of minutes played by each player during the game.
"""
# get duration of each period
periods = pd.DataFrame(
[
{"period_id": 1, "minute": 45},
{"period_id": 2, "minute": 45},
{"period_id": 3, "minute": 15},
{"period_id": 4, "minute": 15},
# Shoot-outs should not contritbute to minutes played
# {"period_id": 5, "minute": 0},
]
).set_index("period_id")
periods_minutes = (
events.loc[events.type_name == "Half End", ["period_id", "minute"]]
.drop_duplicates()
.set_index("period_id")
.sort_index()
.subtract(periods.cumsum().shift(1).fillna(0))
.minute.dropna()
.astype(int)
.tolist()
)
# get duration of entire match
game_minutes = sum(periods_minutes)
game_id = events.game_id.mode().values[0]
players = {}
# Red cards
red_cards = events[
events.apply(
lambda x: any(
e in x.extra
and "card" in x.extra[e]
and x.extra[e]["card"]["name"] in ["Second Yellow", "Red Card"]
for e in ["foul_committed", "bad_behaviour"]
),
axis=1,
)
]
# stats for starting XI
for startxi in events[events.type_name == "Starting XI"].itertuples():
team_id, team_name = startxi.team_id, startxi.team_name
for player in startxi.extra["tactics"]["lineup"]:
player = _flatten_id(player)
player = {
**player,
**{
"game_id": game_id,
"team_id": team_id,
"team_name": team_name,
"minutes_played": game_minutes,
},
}
player_red_card = red_cards[red_cards.player_id == player["player_id"]]
if len(player_red_card) > 0:
red_card_minute = player_red_card.iloc[0].minute
player["minutes_played"] = _expand_minute(red_card_minute, periods_minutes)
players[player["player_id"]] = player
# stats for substitutions
for substitution in events[events.type_name == "Substitution"].itertuples():
exp_sub_minute = _expand_minute(substitution.minute, periods_minutes)
replacement = {
"player_id": substitution.extra["substitution"]["replacement"]["id"],
"player_name": substitution.extra["substitution"]["replacement"]["name"],
"minutes_played": game_minutes - exp_sub_minute,
"team_id": substitution.team_id,
"game_id": game_id,
"team_name": substitution.team_name,
}
player_red_card = red_cards[red_cards.player_id == replacement["player_id"]]
if len(player_red_card) > 0:
red_card_minute = player_red_card.iloc[0].minute
replacement["minutes_played"] = (
_expand_minute(red_card_minute, periods_minutes) - exp_sub_minute
)
players[replacement["player_id"]] = replacement
players[substitution.player_id]["minutes_played"] = exp_sub_minute
pg = pd.DataFrame(players.values()).fillna(0)
for col in pg.columns:
if "_id" in col:
pg[col] = pg[col].astype(int) # pylint: disable=E1136,E1137
return pg
def _flatten_id(d: dict[str, dict[str, Any]]) -> dict[str, Any]:
newd = {}
extra = {}
for k, v in d.items():
if isinstance(v, dict):
if "id" in v and "name" in v:
newd[k + "_id"] = v["id"]
newd[k + "_name"] = v["name"]
else:
extra[k] = v
else:
newd[k] = v
newd["extra"] = extra
return newd
def _flatten(d: dict[str, dict[str, Any]]) -> dict[str, Any]:
newd = {}
for k, v in d.items():
if isinstance(v, dict):
if "id" in v and "name" in v:
newd[k + "_id"] = v["id"]
newd[k + "_name"] = v["name"]
newd[k + "_extra"] = {l: w for (l, w) in v.items() if l in ("id", "name")}
else:
newd = {**newd, **_flatten(v)}
else:
newd[k] = v
return newd
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