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Datasets:
enesor0
/
player-valuer-synthetic-data

Tasks:
Tabular Regression
Modalities:
Tabular
Text
Formats:
csv
Size:
1K - 10K
Tags:
tabular
Synthetic
football
sports-analytics
regression
machine-learning
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pandas
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age
int64
16
36
position
stringclasses
4 values
minutes
int64
90
4k
goals
int64
0
31
assists
int64
0
14
yellow_cards
int64
0
11
red_cards
int64
0
2
rating
float64
5.53
9.28
contract_years_left
int64
0
6
market_value_eur
int64
7.67M
211M
21
FW
397
23
4
7
1
7.37
2
133,540,277
28
MF
1,516
0
0
4
0
6.82
2
31,250,847
21
FW
3,084
9
4
2
0
6.99
3
117,494,044
24
MF
1,633
6
9
3
0
7.38
3
76,471,116
23
GK
1,454
0
0
4
0
6.43
3
31,231,134
26
FW
3,378
8
0
2
0
7.77
2
79,760,202
23
FW
3,406
2
3
2
0
7.18
5
104,502,277
24
FW
2,665
12
5
2
0
7.82
4
100,676,824
24
DF
1,790
1
1
2
1
6.34
2
28,000,869
22
MF
1,984
2
1
5
0
6.98
2
71,560,848
27
DF
1,476
0
0
5
0
6.38
2
30,170,248
26
FW
1,304
13
9
5
0
7.45
2
63,758,340
25
MF
1,260
6
3
4
0
7.69
1
50,137,608
23
FW
1,643
17
6
1
0
7.14
1
110,658,787
28
MF
1,268
3
6
3
0
6.79
0
33,357,922
24
DF
792
0
1
5
0
6.97
1
35,116,145
24
MF
2,397
5
8
4
0
7.56
3
101,345,778
25
GK
2,411
0
1
6
1
7.01
2
15,412,348
31
FW
3,730
17
7
2
0
7.59
2
44,303,482
20
MF
1,598
1
3
2
0
7.24
2
66,986,596
28
FW
2,466
11
0
3
0
7.6
1
54,568,164
18
DF
2,481
0
0
2
0
7.12
2
47,035,609
26
FW
1,971
15
4
2
0
7.44
3
100,456,598
31
FW
2,869
8
8
4
0
6.05
2
28,181,633
27
FW
2,897
15
1
4
0
6.71
3
90,110,751
26
DF
2,494
0
2
5
1
6.07
4
35,982,185
23
MF
1,969
7
3
3
0
6.99
3
90,516,709
25
GK
1,878
0
0
3
1
6.7
1
21,782,882
24
DF
969
2
1
5
0
6.77
2
30,854,107
22
MF
1,474
0
4
3
0
7.22
5
111,303,791
24
MF
1,640
12
2
1
1
7.1
2
71,801,362
26
FW
1,188
9
7
2
0
7.89
2
94,684,005
22
DF
2,595
2
0
6
0
7.55
1
42,669,093
22
DF
3,276
3
1
3
0
6.81
2
49,664,524
20
MF
1,382
12
2
5
2
7.14
3
99,797,445
26
DF
2,455
3
1
1
0
7.5
2
27,792,238
27
DF
1,269
3
2
2
0
7.16
1
31,315,693
26
MF
1,925
10
5
7
0
6.92
2
58,942,113
28
DF
1,578
2
1
7
0
6.47
4
25,353,741
25
MF
2,741
8
5
2
0
6.72
3
55,485,991
26
MF
1,589
4
10
3
0
6.87
3
63,884,801
26
FW
3,110
15
6
1
1
6.57
3
114,343,949
25
MF
1,978
8
5
1
0
6.33
0
51,893,750
23
DF
1,615
2
0
6
1
6.78
2
36,605,166
33
FW
1,072
14
5
4
0
7.64
3
34,526,490
23
FW
2,427
12
3
5
0
7.55
2
107,445,829
23
DF
3,005
3
0
4
1
6.85
3
39,889,939
27
DF
2,445
3
0
5
0
6.52
3
37,901,788
26
MF
4,000
4
7
4
0
7.34
3
78,083,198
20
DF
3,486
2
1
3
0
6.51
2
40,953,789
29
DF
1,923
3
1
3
0
7.5
3
31,440,715
25
GK
1,913
0
0
3
0
7.18
0
19,737,881
21
FW
2,622
17
1
1
0
6.84
2
107,538,409
29
MF
1,445
0
1
8
0
6.51
3
34,138,529
27
MF
3,249
9
6
4
0
6.91
2
59,440,436
21
FW
2,525
3
9
2
1
6.2
3
78,429,634
31
MF
2,430
8
2
5
0
7.26
2
19,595,958
27
MF
2,818
4
5
2
0
7.34
1
54,796,007
25
DF
3,750
0
1
2
0
6.51
0
31,032,273
31
DF
2,353
1
0
2
0
7.33
2
16,046,982
26
MF
2,098
0
6
4
0
6.29
3
58,962,573
25
MF
3,557
2
4
3
0
6.96
3
49,756,307
23
MF
3,070
0
11
3
0
7.18
0
72,124,591
27
FW
1,911
6
6
4
1
6.92
4
67,079,734
27
MF
2,204
7
8
3
0
7.02
1
66,240,280
25
MF
2,375
0
8
2
1
7.54
2
68,076,411
22
MF
2,525
5
6
4
0
6.67
1
75,803,245
25
DF
979
1
1
4
0
6.73
3
43,118,466
25
GK
811
0
0
2
0
6.67
2
20,456,650
20
MF
1,401
1
9
4
0
6.18
3
67,939,254
20
DF
1,331
3
4
4
1
6.89
4
49,439,073
27
MF
1,620
0
0
3
0
7.32
3
49,061,114
16
FW
2,771
10
5
3
0
8.24
1
114,213,801
26
DF
2,047
4
0
4
1
7.12
3
40,468,747
23
GK
2,376
0
0
6
0
6.5
5
33,394,914
30
DF
1,098
0
0
4
0
6.66
3
22,301,064
23
DF
1,784
4
5
2
0
7.13
3
61,584,552
27
MF
2,728
5
8
4
1
6.79
0
49,920,056
24
MF
2,735
3
1
6
0
6.3
3
40,974,143
25
FW
1,547
3
3
8
0
6.77
2
42,650,494
28
MF
2,345
7
2
2
0
6.94
1
37,106,896
23
MF
4,000
0
6
5
0
7.49
2
56,442,431
28
FW
2,184
10
4
2
1
6.8
2
50,156,017
22
DF
1,576
0
2
7
0
6.08
1
33,660,739
26
GK
2,043
0
0
5
0
5.89
2
19,742,085
28
GK
2,282
0
0
5
0
5.95
2
13,807,591
27
MF
2,063
1
8
1
0
6.49
3
71,029,203
32
MF
2,171
0
1
5
0
7.93
2
17,062,459
22
DF
900
0
0
3
0
7.08
2
50,138,466
20
MF
1,333
0
2
2
0
6.66
3
60,244,257
22
DF
2,065
1
2
2
0
5.91
3
38,235,836
27
MF
2,051
10
4
3
0
7.03
2
71,983,357
27
MF
1,690
11
3
5
1
6.51
0
51,057,580
25
DF
2,040
1
0
3
0
6.81
3
30,439,040
21
DF
2,254
0
2
5
2
7.24
1
53,898,891
28
MF
3,000
4
7
3
0
7.02
2
29,305,692
21
DF
2,110
2
1
4
0
7.15
2
46,157,500
29
GK
2,559
0
0
1
0
5.87
4
17,816,234
35
DF
1,712
3
0
3
0
6.38
4
18,952,928
26
FW
2,714
10
4
2
0
6.7
1
66,687,217
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