def plays_before_time(connection, gsis_id_, before_quarter, before_time):
quarter, time = process_time_col(pd.read_sql_query(
"""SELECT play_id, time
FROM play
WHERE gsis_id = %(gsis_id)s
""",
connection,
index_col='play_id',
params=dict(gsis_id=gsis_id_),
).sort_index()['time'])
return [
int(play_id) for play_id in
quarter.index[is_before(quarter, time, before_quarter, before_time)]
]
def team_stats_by_drive(connection, include_preseason=False):
sum_columns_sql = ', '.join(_sum_query(col) for col in offense_team_stat_columns)
team_sums = pd.read_sql_query(
"""SELECT gsis_id, drive_id, {}
FROM drive
INNER JOIN agg_play USING(gsis_id, drive_id)
{}
GROUP BY gsis_id, drive_id
""".format(
sum_columns_sql,
'' if include_preseason else """
INNER JOIN game USING(gsis_id)
WHERE season_type != 'Preseason'
"""
),
connection,
index_col=['gsis_id', 'drive_id'],
).sort_index()
drive = pd.read_sql_table(
'drive', connection,
index_col=['gsis_id', 'drive_id'],
).sort_index()
drive['team'] = drive['pos_team']
del drive['pos_team']
for col in ['start_field', 'end_field', 'pos_time']:
drive.loc[~drive[col].isnull(), col] = _de_parenthesize(drive.loc[~drive[col].isnull(), col])
for time_type in ['start', 'end']:
drive[time_type + '_quarter'], drive[time_type + '_time'] = process_time_col(drive[time_type + '_time'])
drive = (pd.concat([drive, team_sums], axis=1, join='inner')
.reset_index()
.set_index(['gsis_id', 'team', 'drive_id'])
.sort_index()
)
drive['offense_score'] = 0
drive['defense_score'] = 0
for name, row in drive.iterrows():
gsis_id, team, drive_id = name
scores = score_before_time(connection, gsis_id, row['start_quarter'], row['start_time'])
drive.loc[name, 'offense_score'] = scores[team]
drive.loc[name, 'defense_score'] = scores[scores.index != team][0]
return drive