def get_player_shooting_splits(self):
for season in self.seasons:
player_list = nba_py_player.PlayerList(season=season).info()
if isinstance(player_list, pandas.DataFrame):
for index, row in player_list.iterrows():
if self.db.player_shooting_splits.count(
{
"SEASON_ID": season_to_season_id(season),
"PLAYER_ID": str(row.PERSON_ID)
}) > 0:
print(
"shooting splits of {} {} already exists, skipping"
.format(season, row.DISPLAY_FIRST_LAST))
continue
print("retrieving player shooting splits for {} {}".format(
season, row.DISPLAY_FIRST_LAST))
shooting_splits = nba_py_player.PlayerShootingSplits(
player_id=row.PERSON_ID, season=season).shot_5ft()
if isinstance(shooting_splits, pandas.DataFrame):
shooting_splits = shooting_splits.to_dict("records")
if shooting_splits:
for shooting_range in shooting_splits:
shooting_range['PLAYER_ID'] = str(row.PERSON_ID)
shooting_range['SEASON_ID'] = season_to_season_id(
season)
shooting_range[
'PLAYER_NAME'] = row.DISPLAY_FIRST_LAST
self.db.player_shooting_splits.insert_many(
shooting_splits)
else:
print("shooting splits of {} {} not found".format(
season, row.DISPLAY_FIRST_LAST))
def generate_ast_shot_df(player_id_list, year):
lst_of_dicts = []
for id in player_id_list:
print id
#get the shotchart for player
ast_shots = player.PlayerShootingSplits(id,
season=year).assisted_shots()
if not ast_shots.empty:
ast_shots_made = ast_shots.FGM[ast_shots.GROUP_VALUE ==
'Assisted'].sum()
lst_of_dicts.append({
'player_id': str(id),
'ast_shot_made': ast_shots_made
})
else:
lst_of_dicts.append({'player_id': str(id), 'ast_shot_made': 0})
ast_shot_df = pd.DataFrame(lst_of_dicts)
ast_shot_df.set_index('player_id', inplace=True, drop=True)
return ast_shot_df
def get_player_stats():
my_player = player.PlayerShootingSplits(player_id)
stats = my_player.shot_5ft()
## LONG RANGE, MID RANGE, CLOSE RANGE AND FREE THROW SHOOTING ##
global field_goals, field_goal_pcts
field_goals = stats["FGM"]
field_goal_pcts = stats["FG_PCT"]
get_2pt_impact(field_goals, field_goal_pcts)
get_3pt_impact_and_free_throw()
## DUNKS ##
shot_types = my_player.shot_types_summary()
shots_made = shot_types["FGM"]
dunks = shots_made[2]
## HEIGHT ##
height_info = get_player_info("HEIGHT")
height = get_inches_calc(height_info)
## ATHLETICISM ##
get_athletic_impact(height, dunks)
## SHOTS ASSISTED BY AND TO ##
assisted_stats = my_player.assisted_shots()
two_shots_made = assisted_stats["FGM"]
three_shots_made = assisted_stats["FG3M"]
solo_points = get_unassisted_impact(two_shots_made, three_shots_made)
points_created = get_passing_stats()
get_playmaking_impact(points_created, solo_points)
#return get_vertical_leap(1)
#return stats
## DEFENSE ##
get_defense_impact()
#return two_pt_stats + three_pt_and_free_stats
global total
total = long_range + inside + defense + athletic + playmaking
import nba_py
import json
import sys
import webbrowser
import os
from nba_py import team as team
from nba_py import player as p
from nba_py import constants as constants
firstName = sys.argv[1]
lastName = sys.argv[2]
print(firstName + " " + lastName + " " + "Stats")
pID = (p.get_player(firstName, lastName))
shootingSplits = p.PlayerShootingSplits(pID).shot_areas()
playerInfo = p.PlayerSummary(pID).info()
headlineStats = p.PlayerSummary(pID).headline_stats()
playerPhotoLink = "http://ak-static.cms.nba.com/wp-content/uploads/headshots/nba/latest/260x190/" + str(
pID) + ".png"
linkJSON = {}
print(json.dumps(headlineStats, indent=4))
print(type(playerInfo))
playerInfo[0]["link"] = playerPhotoLink
twoMade = 0
twoAttempted = 0
threeMade = 0
threeAttempted = 0
## General Splits
if str(case) == "generalsplits":
for name in my_own_namelist.keys():
player_id = my_own_namelist[name]
print(name, player_id)
season = '2018-19'
stats = player.PlayerGeneralSplits(player_id=player_id,
season=season).json
#print(json.dumps(stats,indent=4,sort_keys=True))
# So for now I don't care about the parameters in the dictionary. I just save them down first.
target_dir = str(season) + '\\generalsplits\\' + str(
player_id) + '_' + str(name)
with open(target_dir, 'w') as f:
json.dump(stats, f)
elif str(case) == "shootingsplits":
for name in my_own_namelist.keys():
player_id = my_own_namelist[name]
print(name, player_id)
season = '2018-19'
stats = player.PlayerShootingSplits(player_id=player_id,
season=season).json
# print(stats)
# print(json.dumps(stats,indent=4,sort_keys=True))
# So for now I don't care about the parameters in the dictionary. I just save them down first.
target_dir = str(season) + '\\shootingsplits\\' + str(
player_id) + '_' + str(name)
with open(target_dir, 'w') as f:
json.dump(stats, f)
# query_data("shootingsplits")
def get_player_stat(player_id):
"""
Takes player NBA ID and returns a data frame containing player performance
data such as shooting, blocking, rebound etc...
"""
# Extracting shooting and blocking columns
player_shot_block_raw = player.PlayerShootingSplits(player_id, season='2018-19').shot_areas()
if player_shot_block_raw.shape[0] == 0:
raise NoDataError("No data for {}".format(player_id))
player_shot_and_block_by_area = player_shot_block_raw.T
player_shot_and_block_by_area.columns = player_shot_and_block_by_area.loc['GROUP_VALUE']
player_shot_and_block_by_area = player_shot_and_block_by_area.loc[[
'FGA', 'BLKA'], "Restricted Area":"Above the Break 3"]
player_shot_by_area = player_shot_and_block_by_area.loc[["FGA"], :]
player_block_by_area = player_shot_and_block_by_area.loc[["BLKA"], :]
# Use a dict to map old col name to new col name, using dict is required
# because the API sometimes returns inconsistent column order and number
# of cols
shot_col_map = {"Restricted Area": 'shot_res',
"In The Paint (Non-RA)": 'shot_in_paint',
"Mid-Range": 'shot_mid_range',
'Left Corner 3': 'shot_lcorner_3',
'Right Corner 3': 'shot_rcorner_3',
'Above the Break 3': 'shot_above_3'}
block_col_map = {"Restricted Area": 'block_res',
"In The Paint (Non-RA)": 'block_in_paint',
"Mid-Range": 'block_mid_range',
'Left Corner 3': 'block_lcorner_3',
'Right Corner 3': 'block_rcorner_3',
'Above the Break 3': 'block_above_3'}
# Applying the column name map
player_shot_by_area = player_shot_by_area.rename(columns=shot_col_map)
player_block_by_area = player_block_by_area.rename(columns=block_col_map)
# Total attempted shots and blocks
total_shots = player_shot_by_area.loc['FGA', :].sum()
total_blocks = player_block_by_area.loc['BLKA', :].sum()
# Normalize all stats by total shots to get relative frequencies for each
# player
player_shot_by_area /= total_shots + 1e-5
player_block_by_area /= total_blocks + 1e-5
# Record total attempted shots and blocks
player_shot_by_area['fga'] = total_shots
player_block_by_area['blka'] = total_blocks
# PlayerYearOverYearSplits is the end point we will be calling
# to get player stats aside from shooting and blocking
# Take the most recent 2 years, and then add them
rebound_assist = player.PlayerYearOverYearSplits(
player_id, per_mode="Per48").by_year().iloc[:2, :]
cols_of_interest = ['OREB', 'DREB', 'AST', 'STL', "MIN"]
rebound_assist = rebound_assist[cols_of_interest]
rebound_assist.columns = [c.lower() for c in rebound_assist.columns]
rebound_assist_summed = pd.DataFrame(rebound_assist.sum()).T
# Finally, concatnate the dataframes together
player_stat = pd.concat([player_shot_by_area.reset_index(drop=True),
player_block_by_area.reset_index(drop=True),
rebound_assist_summed.reset_index(drop=True)],
axis=1)
player_stat = player_stat.rename({0: player_id})
return player_stat