Step 1: Select the 'attendance' column from the table.
date |
opponent |
score |
loss |
attendance |
record |
9999-09-01 |
cardinals |
8 - 6 |
mcclellan (2 - 7) |
35075 |
70 - 67 |
9999-09-02 |
cardinals |
8 - 2 |
petit (3 - 4) |
27568 |
70 - 68 |
9999-09-03 |
cardinals |
4 - 3 |
perez (2 - 2) |
24350 |
71 - 68 |
9999-09-05 |
dodgers |
7 - 0 |
haren (14 - 8) |
52270 |
71 - 69 |
9999-09-06 |
dodgers |
7 - 2 |
webb (19 - 7) |
47543 |
71 - 70 |
9999-09-07 |
dodgers |
5 - 3 |
rauch (4 - 6) |
54137 |
71 - 71 |
9999-09-08 |
giants |
6 - 2 |
petit (3 - 5) |
30252 |
71 - 72 |
9999-09-09 |
giants |
5 - 4 |
rauch (4 - 7) |
30518 |
71 - 73 |
9999-09-10 |
giants |
4 - 3 |
lyon (2 - 5) |
30992 |
71 - 74 |
9999-09-12 |
reds |
3 - 2 |
harang (4 - 16) |
29046 |
72 - 74 |
9999-09-13 |
reds |
3 - 2 (10) |
peña (1 - 2) |
45075 |
72 - 75 |
9999-09-14 |
reds |
2 - 1 (10) |
rauch (4 - 8) |
27297 |
72 - 76 |
9999-09-15 |
giants |
3 - 1 |
hennessey (1 - 2) |
25969 |
73 - 76 |
9999-09-16 |
giants |
2 - 0 |
cain (8 - 13) |
33195 |
74 - 76 |
9999-09-17 |
giants |
7 - 6 |
sánchez (9 - 11) |
22616 |
75 - 76 |
9999-09-18 |
giants |
3 - 2 |
lincecum (17 - 4) |
34323 |
76 - 76 |
9999-09-19 |
rockies |
3 - 2 |
scherzer (0 - 3) |
43137 |
76 - 77 |
9999-09-20 |
rockies |
5 - 3 |
fuentes (1 - 5) |
38283 |
77 - 77 |
9999-09-21 |
rockies |
13 - 4 |
reynolds (2 - 8) |
32915 |
78 - 77 |
9999-09-22 |
cardinals |
4 - 2 |
wellemeyer (12 - 9) |
40349 |
79 - 77 |
9999-09-23 |
cardinals |
7 - 4 |
johnson (10 - 10) |
40013 |
79 - 78 |
9999-09-24 |
cardinals |
4 - 2 |
scherzer (0 - 4) |
40029 |
79 - 79 |
9999-09-25 |
cardinals |
12 - 3 |
rosales (1 - 1) |
40502 |
79 - 80 |
9999-09-26 |
rockies |
6 - 4 |
grilli (3 - 3) |
34950 |
80 - 80 |
9999-09-27 |
rockies |
6 - 4 |
corpas (3 - 4) |
33234 |
81 - 80 |
9999-09-28 |
rockies |
2 - 1 |
vizcaíno (1 - 2) |
35908 |
82 - 80 |
Step 2: Calculate the average of the 'attendance' column and add it as a new column 'avg_attendance' to the existing table.
attendance |
35075 |
27568 |
24350 |
52270 |
47543 |
54137 |
30252 |
30518 |
30992 |
29046 |
45075 |
27297 |
25969 |
33195 |
22616 |
34323 |
43137 |
38283 |
32915 |
40349 |
40013 |
40029 |
40502 |
34950 |
33234 |
35908 |
Step 3: Select rows where 'avg_attendance' is 31,521.
attendance |
avg_attendance |
35075.0 |
35751.769230769234 |
Step 4: Use a `CASE` statement to return TRUE if the number of rows is equal to the total number of rows in the table (26), otherwise return FALSE.
attendance |
avg_attendance |
Final output table:
verification_result |
FALSE |
Prediction: FALSE
Ground-truth: TRUE