im trying to scrape the first major table from the following site using BeautifulSoup : https://dailyfantasyrankings.com.au/resources/nba/cheatsheet/moneyball/allproj.php
Im receiving the error: AttributeError: ResultSet object has no attribute 'find_all'. You're probably treating a list of items like a single item. Did you call find_all() when you meant to call find()?
Im sure other parts of my code arent quite right but was hoping someone could assist!
Code:
import requests
from urllib.request import urlopen
from bs4 import BeautifulSoup
import pandas as pd
URL = 'https://dailyfantasyrankings.com.au/resources/nba/cheatsheet/moneyball/allproj.php'
response = requests.get(URL)
html = response.content
soup = BeautifulSoup(response.content, 'html.parser')
table = soup.find('table', attrs={'class': 'xl12520945'})
columns = ['#', 'PLAYER', 'POS', '@', 'TEAM', 'OPP', 'M-UP', 'PACE', 'REST', 'PRICE', 'PROJ', 'VALUE', 'AVE']
df = pd.DataFrame(columns=columns)
trs = table.find_all('tr')
for tr in trs:
tds = tr.find_all('td')
row = [td.text.replace('\n','') for td in tds]
df = df.append(pd.Series(row, index=columns), ignore_index=True)
df.to_csv('dfr_proj.csv', index = False)
Here's your answer: Beautiful Soup: 'ResultSet' object has no attribute 'find_all'?
However, I tried running your script and was running into a different issue. The "table" element didn't even exist.
The table you're trying to scrape is in an iframe, so it's better to get the iframe source html instead of the page html
URL = 'https://dailyfantasyrankings.com.au/resources/nba/htm/projections/mballproj.htm'
Get the table with CSS selector instead of class name because there is an issue with how HTML is formatted on the source page:
table = soup.select("#Finish_20945 > table")[0]
Working code. Change are marked with code comments:
import requests
from bs4 import BeautifulSoup
import pandas as pd
#CHANGED LINE BELOW
URL = 'https://dailyfantasyrankings.com.au/resources/nba/htm/projections/mballproj.htm'
response = requests.get(URL)
soup = BeautifulSoup(response.content, 'html.parser')
#CHANGED LINE BELOW
table = soup.select("#Finish_20945 > table")[0]
columns = ['#', 'PLAYER', 'POS', '@', 'TEAM', 'OPP', 'M-UP', 'PACE', 'REST', 'PRICE', 'PROJ', 'VALUE', 'AVE']
df = pd.DataFrame(columns=columns)
trs = table.find_all('tr')
for tr in trs:
tds = tr.find_all('td')
row = [td.text.replace('\n', '') for td in tds]
print(row)
# df = df.append(pd.Series(row, index=columns), ignore_index=True)
df.to_csv('dfr_proj.csv', index=False)
Output:
['', '', '', '', '', '', '', '', '', '', '', '↓', '↓', '', '\xa0', '\xa0', '\xa0', '\xa0', '\xa0']
['#', 'Player', '\xa0', 'Pos', '@', 'Team', 'Opp', 'M-UP', 'Pace', 'Rest', 'Price', 'Proj', 'Value', 'Ave', 'Last 5 Games']
['1', 'A. Davis', '\xa0', 'PF', 'A', 'LAL', 'NOP', '18', '3.5', 'B2B', '$10,800', '61', '5.6', '51', '70', '52', '61', '41', '37']
['2', 'B. Beal', '\xa0', 'SG', 'A', 'WAS', 'GSW', '5', '0.3', '1', '$9,600', '57', '5.9', '46', '34', '67', '53', '51', '67']
['3', 'L. James', '\xa0', 'SF', 'A', 'LAL', 'NOP', '3', '3.5', 'B2B', '$10,400', '53', '5.1', '51', '51', '50', '55', '\xa0', '45']
['4', 'N. Jokic', '\xa0', 'C', 'H', 'DEN', 'TOR', '19', '0.4', '1', '$10,100', '47', '4.7', '45', '53', '52', '50', '35', '35']
['5', 'P. Siakam', '\xa0', 'SF', 'A', 'TOR', 'DEN', '26', '-3.0', '1', '$8,400', '45', '5.4', '41', '34', '63', '28', '33', '50']
['6', 'K. Lowry', '\xa0', 'PG', 'A', 'TOR', 'DEN', '11', '-3.0', '1', '$7,600', '44', '5.8', '38', '43', '31', '57', '23', '47']
['7', 'A. Wiggins', '\xa0', 'SF', 'H', 'GSW', 'WAS', '11', '3.0', 'B2B', '$7,700', '42', '5.5', '36', '34', '34', '16', '\xa0', '46']
['8', 'B. Ingram', '\xa0', 'SF', 'H', 'NOP', 'LAL', '16', '0.5', '1', '$7,900', '40', '5.1', '40', '\xa0', '26', '32', '47', '47']
['9', 'C. Wood', 'GTD', 'PF', 'A', 'DET', 'SAC', '26', '-2.0', '1', '$7,900', '39', '4.9', '23', '48', '35', '44', '36', '48']
['10', 'D. Fox', 'GTD', 'PG', 'H', 'SAC', 'DET', '3', '-2.4', '1', '$7,500', '38', '5.1', '37', '34', '35', '36', '\xa0', '42']
['11', 'J. Holiday', '\xa0', 'SG', 'H', 'NOP', 'LAL', '24', '0.5', '1', '$8,900', '38', '4.3', '40', '35', '44', '52', '33', '40']
['12', 'D. Rose', '\xa0', 'PG', 'A', 'DET', 'SAC', '18', '-2.0', '1', '$6,000', '36', '6.0', '30', '11', '19', '17', '32', '47']
['13', 'Z. Williamson', '\xa0', 'PF', 'H', 'NOP', 'LAL', '16', '0.5', '1', '$8,300', '36', '4.3', '33', '41', '37', '41', '41', '33']
['14', 'D. Lee', '\xa0', 'SG', 'H', 'GSW', 'WAS', '7', '3.0', 'B2B', '$6,100', '34', '5.7', '24', '26', '36', '32', '25', '41']
['15', 'M. Chriss', '\xa0', 'C', 'H', 'GSW', 'WAS', '21', '3.0', 'B2B', '$6,400', '34', '5.3', '22', '21', '\xa0', '37', '20', '36']
['16', 'O. Anunoby', '\xa0', 'SF', 'A', 'TOR', 'DEN', '27', '-3.0', '1', '$5,000', '33', '6.7', '24', '12', '31', '19', '29', '49']
['17', 'J. Murray', '\xa0', 'PG', 'H', 'DEN', 'TOR', '15', '0.4', '1', '$7,200', '32', '4.4', '33', '49', '35', '29', '39', '17']
['18', 'L. Ball', '\xa0', 'PG', 'H', 'NOP', 'LAL', '30', '0.5', '1', '$6,900', '32', '4.6', '32', '33', '32', '33', '30', '46']
['19', 'E. Paschall', '\xa0', 'PF', 'H', 'GSW', 'WAS', '21', '3.0', 'B2B', '$4,600', '30', '6.5', '23', '21', '20', '20', '30', '34']
['20', 'N. Powell', '\xa0', 'SG', 'A', 'TOR', 'DEN', '10', '-3.0', '1', '$4,500', '28', '6.2', '26', '\xa0', '\xa0', '\xa0', '\xa0', '27']
['21', 'R. Hachimura', '\xa0', 'PF', 'A', 'WAS', 'GSW', '15', '0.3', '1', '$5,100', '28', '5.4', '25', '36', '25', '22', '20', '31']
['22', 'B. Hield', '\xa0', 'SG', 'H', 'SAC', 'DET', '26', '-2.4', '1', '$6,000', '27', '4.4', '32', '39', '12', '32', '24', '24']
['23', 'W. Barton', '\xa0', 'SF', 'H', 'DEN', 'TOR', '11', '0.4', '1', '$5,800', '26', '4.5', '31', '\xa0', '35', '25', '16', '25']
['24', 'H. Giles III', '\xa0', 'PF', 'H', 'SAC', 'DET', '7', '-2.4', '1', '$5,100', '26', '5.1', '15', '15', '33', '33', '29', '30']
['25', 'N. Bjelica', '\xa0', 'PF', 'H', 'SAC', 'DET', '11', '-2.4', '1', '$5,100', '26', '5.1', '27', '26', '24', '10', '31', '31']
['26', 'J. Grant', '\xa0', 'PF', 'H', 'DEN', 'TOR', '23', '0.4', '1', '$4,500', '25', '5.5', '21', '26', '8', '17', '35', '35']
['27', 'S. Napier', '\xa0', 'PG', 'A', 'WAS', 'GSW', '6', '0.3', '1', '$5,000', '24', '4.7', '22', '18', '17', '46', '22', '13']
['28', 'D. Bender', '\xa0', 'PF', 'H', 'GSW', 'WAS', '21', '3.0', 'B2B', '$3,900', '23', '5.9', '12', '\xa0', '20', '8', '13', '39']
['29', 'D. Favors', '\xa0', 'C', 'H', 'NOP', 'LAL', '30', '0.5', '1', '$5,400', '23', '4.3', '27', '21', '27', '24', '18', '37']
['30', 'K. Bazemore', '\xa0', 'SF', 'H', 'SAC', 'DET', '26', '-2.4', '1', '$5,400', '23', '4.2', '19', '25', '47', '28', '18', '26']
['31', 'D. Bertans', '\xa0', 'PF', 'A', 'WAS', 'GSW', '24', '0.3', '1', '$5,100', '23', '4.5', '26', '26', '25', '\xa0', '23', '19']
['32', 'H. Barnes', '\xa0', 'SF', 'H', 'SAC', 'DET', '29', '-2.4', '1', '$5,200', '23', '4.4', '25', '35', '36', '24', '29', '15']
['33', 'T. Bryant', '\xa0', 'C', 'A', 'WAS', 'GSW', '11', '0.3', '1', '$4,100', '23', '5.5', '25', '20', '13', '\xa0', '15', '22']
['34', 'B. Knight', '\xa0', 'PG', 'A', 'DET', 'SAC', '23', '-2.0', '1', '$3,900', '22', '5.8', '11', '\xa0', '1', '22', '25', '28']
['35', 'R.\r Hollis-Jefferson', '\xa0', 'PF', 'A', 'TOR', 'DEN', '26', '-3.0', '1', '$4,000', '22', '5.4', '19', '12', '22', '18', '25', '15']
['36', 'I. Smith', '\xa0', 'PG', 'A', 'WAS', 'GSW', '8', '0.3', '1', '$5,000', '22', '4.3', '24', '26', '29', '30', '12', '16']
['37', 'R. Rondo', '\xa0', 'PG', 'A', 'LAL', 'NOP', '4', '3.5', 'B2B', '$4,300', '21', '4.9', '19', '14', '28', '7', '30', '10']
['38', 'A. Len', '\xa0', 'C', 'H', 'SAC', 'DET', '7', '-2.4', '1', '$4,000', '21', '5.2', '20', '\xa0', '16', '11', '26', '24']
['39', 'B. Bogdanovic', '\xa0', 'SG', 'H', 'SAC', 'DET', '25', '-2.4', '1', '$5,100', '20', '4.0', '24', '16', '32', '28', '23', '24']
['40', 'D. Howard', '\xa0', 'C', 'A', 'LAL', 'NOP', '29', '3.5', 'B2B', '$4,400', '20', '4.6', '22', '12', '20', '15', '36', '20']
['41', 'M. Mulder', '\xa0', '-', 'H', 'GSW', 'WAS', '7', '3.0', 'B2B', '-', '20', '-', '-', '\xa0', '\xa0', '\xa0', '7', '21']
['42', 'M. Morris', '\xa0', 'PG', 'H', 'DEN', 'TOR', '15', '0.4', '1', '$4,600', '19', '4.2', '19', '26', '12', '34', '29', '18']
['43', 'J. Henson', '\xa0', 'C', 'A', 'DET', 'SAC', '21', '-2.0', '1', '$4,500', '19', '4.3', '17', '4', '12', '31', '14', '21']
['44', 'N. Melli', '\xa0', 'PF', 'H', 'NOP', 'LAL', '30', '0.5', '1', '$4,100', '19', '4.7', '14', '17', '24', '30', '22', '20']
['45', 'K. Caldwell-Pope', '\xa0', 'SG', 'A', 'LAL', 'NOP', '2', '3.5', 'B2B', '$4,200', '19', '4.5', '17', '19', '16', '22', '20', '16']
['46', 'T. Brown Jr.', '\xa0', 'SF', 'A', 'WAS', 'GSW', '24', '0.3', '1', '$4,400', '19', '4.3', '23', '24', '17', '9', '12', '24']
['47', 'K. Looney', '\xa0', 'C', 'H', 'GSW', 'WAS', '15', '3.0', 'B2B', '$3,500', '19', '5.3', '10', '12', '15', '14', '16', '22']
['48', 'S. Mykhailiuk', '\xa0', 'SG', 'A', 'DET', 'SAC', '23', '-2.0', '1', '$3,500', '18', '5.2', '15', '13', '16', '8', '10', '27']
['49', 'L. Galloway', '\xa0', 'SG', 'A', 'DET', 'SAC', '18', '-2.0', '1', '$4,100', '18', '4.4', '17', '17', '13', '26', '14', '12']
['50', 'J. Toscano-Anderson', '\xa0', 'SF', 'H', 'GSW', 'WAS', '11', '3.0', 'B2B', '$4,300', '18', '4.2', '18', '32', '38', '13', '10', '15']
['51', 'A. Bradley', '\xa0', 'SG', 'A', 'LAL', 'NOP', '3', '3.5', 'B2B', '$4,000', '18', '4.4', '15', '33', '10', '8', '19', '8']
['52', 'P. Millsap', 'GTD', 'PF', 'H', 'DEN', 'TOR', '23', '0.4', '1', '$5,400', '17', '3.2', '25', '27', '11', '41', '17', '8']
['53', 'C. Boucher', '\xa0', 'PF', 'A', 'TOR', 'DEN', '25', '-3.0', '1', '$4,200', '17', '3.9', '16', '4', '21', '37', '20', '8']
['54', 'T. Snell', '\xa0', 'SG', 'A', 'DET', 'SAC', '23', '-2.0', '1', '$3,900', '17', '4.2', '15', '30', '15', '9', '28', '13']
['55', 'K. Kuzma', '\xa0', 'PF', 'A', 'LAL', 'NOP', '18', '3.5', 'B2B', '$4,400', '17', '3.8', '20', '17', '22', '13', '25', '16']
['56', 'A. Caruso', '\xa0', 'PG', 'A', 'LAL', 'NOP', '4', '3.5', 'B2B', '$4,000', '16', '4.1', '14', '18', '9', '32', '14', '14']
['57', 'M. Plumlee', '\xa0', 'C', 'H', 'DEN', 'TOR', '19', '0.4', '1', '$4,200', '16', '3.8', '19', '\xa0', '12', '17', '21', '16']
['58', 'T. Davis', '\xa0', 'SG', 'A', 'TOR', 'DEN', '10', '-3.0', '1', '$4,600', '16', '3.4', '16', '3', '19', '21', '20', '8']
['59', 'J. Hart', '\xa0', 'SG', 'H', 'NOP', 'LAL', '22', '0.5', '1', '$4,900', '15', '3.2', '24', '20', '33', '20', '17', '13']
['60', 'J. Robinson', '\xa0', 'PG', 'A', 'WAS', 'GSW', '19', '0.3', '1', '$3,900', '14', '3.6', '8', '14', '10', '10', '22', '18']
['61', 'G. Harris', '\xa0', 'SG', 'H', 'DEN', 'TOR', '21', '0.4', '1', '$4,300', '14', '3.3', '20', '18', '10', '24', '19', '10']
['62', 'C. Joseph', 'GTD', 'PG', 'H', 'SAC', 'DET', '3', '-2.4', '1', '$3,900', '14', '3.6', '17', '17', '5', '19', '33', '14']
['63', 'S. Doumbouya', '\xa0', 'PF', 'A', 'DET', 'SAC', '23', '-2.0', '1', '$3,500', '14', '4.0', '12', '10', '18', '14', '3', '14']
['64', 'M. Porter Jr.', '\xa0', 'PF', 'H', 'DEN', 'TOR', '23', '0.4', '1', '$3,900', '13', '3.4', '16', '\xa0', '5', '6', '22', '15']
['65', 'J. McGee', '\xa0', 'C', 'A', 'LAL', 'NOP', '29', '3.5', 'B2B', '$3,900', '12', '3.0', '20', '10', '14', '13', '21', '7']
['66', 'T. Maker', 'GTD', 'C', 'A', 'DET', 'SAC', '21', '-2.0', '1', '$4,600', '11', '2.5', '11', '25', '25', '16', '9', '6']
['67', 'I. Mahinmi', '\xa0', 'C', 'A', 'WAS', 'GSW', '11', '0.3', '1', '$4,100', '11', '2.6', '20', '18', '6', '21', '15', '14']
['68', 'M. Morris', '\xa0', 'SF', 'A', 'LAL', 'NOP', '18', '3.5', 'B2B', '$4,200', '10', '2.4', '19', '\xa0', '\xa0', '8', '13', '13']
['69', 'E. Moore', '\xa0', 'SF', 'H', 'NOP', 'LAL', '24', '0.5', '1', '$3,500', '9', '2.7', '16', '15', '10', '6', '3', '19']
['70', 'I. Bonga', '\xa0', 'SF', 'A', 'WAS', 'GSW', '19', '0.3', '1', '$3,500', '9', '2.7', '12', '8', '3', '13', '14', '7']
['71', 'T. Craig', '\xa0', 'SG', 'H', 'DEN', 'TOR', '21', '0.4', '1', '$3,900', '9', '2.3', '12', '20', '7', '6', '\xa0', '12']
['72', 'M. Wagner', '\xa0', 'C', 'A', 'WAS', 'GSW', '11', '0.3', '1', '$4,000', '8', '2.1', '20', '11', '12', '28', '8', '4']
['73', 'P. McCaw', '\xa0', 'SG', 'A', 'TOR', 'DEN', '10', '-3.0', '1', '$3,500', '8', '2.3', '13', '9', '\xa0', '\xa0', '\xa0', '7']
['74', 'M. Thomas', '\xa0', 'SG', 'A', 'TOR', 'DEN', '10', '-3.0', '1', '$3,500', '7', '2.0', '8', '1', '1', '18', '14', '4']
['75', 'J. Hayes', '\xa0', 'C', 'H', 'NOP', 'LAL', '30', '0.5', '1', '$3,500', '3', '0.9', '17', '11', '9', '0', '\xa0', '10']
['76', 'P. Dozier', '\xa0', 'SG', 'H', 'DEN', 'TOR', '15', '0.4', '1', '$3,500', '3', '0.7', '10', '6', '\xa0', '0', '3', '6']
['77', 'J. McRae', '\xa0', 'SG', 'H', 'DEN', 'TOR', '11', '0.4', '1', '$4,100', '2', '0.6', '20', '6', '\xa0', '0', '\xa0', '3']
['78', 'J. Okafor', '\xa0', 'C', 'H', 'NOP', 'LAL', '30', '0.5', '1', '$3,500', '2', '0.6', '16', '\xa0', '\xa0', '\xa0', '2', '\xa0']
['79', 'F. Jackson', '\xa0', 'PG', 'H', 'NOP', 'LAL', '30', '0.5', '1', '$3,500', '1', '0.2', '9', '7', '0', '\xa0', '\xa0', '4']
Another option is just use Pandas to read in the table (it uses Beautifulsoup under the hood)
import pandas as pd
URL = 'https://dailyfantasyrankings.com.au/resources/nba/htm/projections/mballproj.htm'
df = pd.read_html(URL)[0]
df.columns = df.iloc[1,:]
df = df.iloc[2:,:]
df.to_csv('dfr_proj.csv', index=False)
Output:
print(df.to_string())
1 # Player NaN Pos @ Team Opp M-UP Pace Rest Price Proj Value Ave Last 5 Games Last 5 Games Last 5 Games Last 5 Games Last 5 Games
2 1 A. Davis GTD PF A LAL NOP 18 3.5 B2B $10,800 59 5.5 51 70 52 61 41 37
3 2 B. Beal NaN SG A WAS GSW 5 0.3 1 $9,600 57 5.9 46 34 67 53 51 67
4 3 L. James NaN SF A LAL NOP 3 3.5 B2B $10,400 53 5.1 51 51 50 55 NaN 45
5 4 N. Jokic NaN C H DEN TOR 19 0.4 1 $10,100 51 5.1 45 53 52 50 35 35
6 5 P. Siakam NaN SF A TOR DEN 26 -3.0 1 $8,400 45 5.4 41 34 63 28 33 50
7 6 K. Lowry NaN PG A TOR DEN 11 -3.0 1 $7,600 45 5.9 38 43 31 57 23 47
8 7 A. Wiggins NaN SF H GSW WAS 11 3.0 B2B $7,700 42 5.5 36 34 34 16 NaN 46
9 8 C. Wood NaN PF A DET SAC 26 -2.0 1 $7,900 42 5.3 23 48 35 44 36 48
10 9 B. Ingram NaN SF H NOP LAL 16 0.5 1 $7,900 40 5.1 40 NaN 26 32 47 47
11 10 D. Fox NaN PG H SAC DET 3 -2.4 1 $7,500 38 5.1 37 34 35 36 NaN 42
12 11 J. Holiday NaN SG H NOP LAL 24 0.5 1 $8,900 38 4.3 40 35 44 52 33 40
13 12 D. Rose NaN PG A DET SAC 18 -2.0 1 $6,000 36 6.0 30 11 19 17 32 47
14 13 Z. Williamson NaN PF H NOP LAL 16 0.5 1 $8,300 36 4.3 33 41 37 41 41 33
15 14 M. Chriss NaN C H GSW WAS 21 3.0 B2B $6,400 34 5.3 22 21 NaN 37 20 36
16 15 O. Anunoby NaN SF A TOR DEN 27 -3.0 1 $5,000 33 6.7 24 12 31 19 29 49
17 16 D. Lee NaN SG H GSW WAS 7 3.0 B2B $6,100 32 5.3 24 26 36 32 25 41
18 17 J. Murray NaN PG H DEN TOR 15 0.4 1 $7,200 32 4.4 33 49 35 29 39 17
19 18 L. Ball NaN PG H NOP LAL 30 0.5 1 $6,900 32 4.6 32 33 32 33 30 46
20 19 N. Powell NaN SG A TOR DEN 10 -3.0 1 $4,500 30 6.7 26 NaN NaN NaN NaN 27
21 20 E. Paschall NaN PF H GSW WAS 21 3.0 B2B $4,600 30 6.5 23 21 20 20 30 34
22 21 J. Grant NaN PF H DEN TOR 23 0.4 1 $4,500 30 6.6 21 26 8 17 35 35
23 22 R. Hachimura NaN PF A WAS GSW 15 0.3 1 $5,100 28 5.4 25 36 25 22 20 31
24 23 B. Hield NaN SG H SAC DET 26 -2.4 1 $6,000 27 4.4 32 39 12 32 24 24
25 24 W. Barton NaN SF H DEN TOR 11 0.4 1 $5,800 26 4.5 31 NaN 35 25 16 25
26 25 H. Giles III NaN PF H SAC DET 7 -2.4 1 $5,100 26 5.1 15 15 33 33 29 30
27 26 N. Bjelica NaN PF H SAC DET 11 -2.4 1 $5,100 26 5.1 27 26 24 10 31 31
28 27 S. Napier NaN PG A WAS GSW 6 0.3 1 $5,000 24 4.7 22 18 17 46 22 13
29 28 M. Porter Jr. NaN PF H DEN TOR 23 0.4 1 $3,900 23 6.0 16 NaN 5 6 22 15
30 29 D. Bender NaN PF H GSW WAS 21 3.0 B2B $3,900 23 5.9 12 NaN 20 8 13 39
31 30 D. Favors NaN C H NOP LAL 30 0.5 1 $5,400 23 4.3 27 21 27 24 18 37
32 31 K. Bazemore NaN SF H SAC DET 26 -2.4 1 $5,400 23 4.2 19 25 47 28 18 26
33 32 D. Bertans NaN PF A WAS GSW 24 0.3 1 $5,100 23 4.5 26 26 25 NaN 23 19
34 33 H. Barnes NaN SF H SAC DET 29 -2.4 1 $5,200 23 4.4 25 35 36 24 29 15
35 34 T. Bryant NaN C A WAS GSW 11 0.3 1 $4,100 23 5.5 25 20 13 NaN 15 22
36 35 B. Knight NaN PG A DET SAC 23 -2.0 1 $3,900 22 5.8 11 NaN 1 22 25 28
37 36 J. Henson NaN C A DET SAC 21 -2.0 1 $4,500 22 5.0 17 4 12 31 14 21
38 37 R. Hollis-Jeffers NaN PF A TOR DEN 26 -3.0 1 $4,000 22 5.4 19 12 22 18 25 15
39 38 I. Smith NaN PG A WAS GSW 8 0.3 1 $5,000 22 4.3 24 26 29 30 12 16
40 39 A. Len NaN C H SAC DET 7 -2.4 1 $4,000 21 5.2 20 NaN 16 11 26 24
41 40 B. Bogdanovic NaN SG H SAC DET 25 -2.4 1 $5,100 20 4.0 24 16 32 28 23 24
42 41 D. Howard NaN C A LAL NOP 29 3.5 B2B $4,400 20 4.6 22 12 20 15 36 20
43 42 J. Hart NaN SG H NOP LAL 22 0.5 1 $4,900 19 4.0 24 20 33 20 17 13
44 43 M. Morris NaN PG H DEN TOR 15 0.4 1 $4,600 19 4.2 19 26 12 34 29 18
45 44 R. Rondo NaN PG A LAL NOP 4 3.5 B2B $4,300 19 4.5 19 14 28 7 30 10
46 45 T. Brown Jr. NaN SF A WAS GSW 24 0.3 1 $4,400 19 4.3 23 24 17 9 12 24
47 46 T. Davis NaN SG A TOR DEN 10 -3.0 1 $4,600 19 4.1 16 3 19 21 20 8
48 47 C. Boucher NaN PF A TOR DEN 25 -3.0 1 $4,200 19 4.4 16 4 21 37 20 8
49 48 S. Mykhailiuk NaN SG A DET SAC 23 -2.0 1 $3,500 18 5.2 15 13 16 8 10 27
50 49 K. Caldwell-Pop NaN SG A LAL NOP 2 3.5 B2B $4,200 18 4.3 17 19 16 22 20 16
51 50 L. Galloway NaN SG A DET SAC 18 -2.0 1 $4,100 18 4.4 17 17 13 26 14 12
52 51 J. Toscano-And NaN SF H GSW WAS 11 3.0 B2B $4,300 18 4.2 18 32 38 13 10 15
53 52 J. Poole GTD SG H GSW WAS 18 3.0 B2B $4,900 18 3.6 15 27 27 20 25 NaN
54 53 K. Looney NaN C H GSW WAS 15 3.0 B2B $3,500 18 5.1 10 12 15 14 16 22
55 54 A. Bradley NaN SG A LAL NOP 3 3.5 B2B $4,000 18 4.4 15 33 10 8 19 8
56 55 T. Snell NaN SG A DET SAC 23 -2.0 1 $3,900 17 4.2 15 30 15 9 28 13
...
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