Hi,
I'll try and keep this concise for ease:
I am looking at the relationship between football clubs financial performance and sporting performance. I have 80 observations over a 10 year time series but I am looking to compare the clubs that have a final position in the top 4 and bottom 4 each year. Due to relegation and promotion, the clubs are not the same each year.
Please could I have your comments on my data and recommendations on the best way to move forward? I have tried unbalanced regression based on clubs being the cross section, and balanced regression based on position as the cross section but I am unable to determine the best way forward. See below sample:
list
+---------------------------------------------------------------+
| position year roa leverage owncon id totala~s |
|---------------------------------------------------------------|
1. | 1 2010 -.2212 1.5945 1 1 8.546 |
2. | 1 2011 .0058 -1.017 2 2 9.2116 |
3. | 1 2012 -.1839 -.5771 1 5 8.7261 |
4. | 1 2013 -.0029 -.7975 2 2 9.1971 |
5. | 1 2014 -.0322 -.3265 1 5 8.8524 |
|---------------------------------------------------------------|
6. | 1 2015 -.0591 1.8232 1 1 8.7609 |
7. | 1 2016 .1423 -1.7165 1 7 8.1493 |
8. | 1 2017 -.0146 1.9694 1 1 8.8269 |
9. | 1 2018 .0147 0 1 5 8.8527 |
10. | 1 2019 .0014 0 1 5 8.876 |
|---------------------------------------------------------------|
11. | 2 2010 -.061 -1.0226 2 2 9.1371 |
12. | 2 2011 -.22 1.6279 1 1 8.5524 |
13. | 2 2012 .007 -.8983 2 2 9.2037 |
14. | 2 2013 -.0864 -.4002 1 5 8.7761 |
15. | 2 2014 .0018 6.101 1 6 8.3576 |
|---------------------------------------------------------------|
16. | 2 2015 .013 -.2563 1 5 8.9166 |
17. | 2 2016 .0018 -1.5406 2 3 8.9535 |
18. | 2 2017 .0562 -2.6997 1 4 8.8651 |
19. | 2 2018 .0192 -1.2992 2 2 9.2994 |
20. | 2 2019 .0473 -1.9207 1 6 8.8484 |
|---------------------------------------------------------------|
21. | 3 2010 .0777 -1.7183 2 3 8.8949 |
22. | 3 2011 -.387 -.7933 1 5 8.7079 |
23. | 3 2012 .0405 -1.3888 2 3 8.8689 |
24. | 3 2013 -.1169 1.7224 1 1 8.6796 |
25. | 3 2014 .0266 1.8719 1 1 8.7282 |
|---------------------------------------------------------------|
26. | 3 2015 .0229 -1.6273 2 3 8.943 |
27. | 3 2016 .0639 -2.0349 1 4 8.7137 |
28. | 3 2017 .0011 -.3563 1 5 9 |
29. | 3 2018 .1056 -2.8767 1 4 9.0294 |
30. | 3 2019 -.1214 2.2003 1 1 8.9458 |
|---------------------------------------------------------------|
31. | 4 2010 -.0229 -3.0874 1 4 8.4621 |
32. | 4 2011 .0174 -1.519 2 3 8.8603 |
33. | 4 2012 -.0149 -2.6632 1 4 8.4576 |
34. | 4 2013 .0075 -1.3999 2 3 8.8904 |
35. | 4 2014 .009 -1.5115 2 3 8.9093 |
|---------------------------------------------------------------|
36. | 4 2015 -.0244 -.9885 2 2 9.2397 |
37. | 4 2016 .0227 -.3184 1 5 8.9552 |
38. | 4 2017 .0845 -3.8897 1 6 8.6653 |
39. | 4 2018 .1874 -2.1317 1 6 8.7527 |
40. | 4 2019 .0584 -3.1972 1 4 9.1752 |
|---------------------------------------------------------------|
41. | 17 2010 -.1626 -7.9635 1 8 8.1037 |
42. | 17 2011 .0242 -.1619 1 12 7.9653 |
43. | 17 2012 -.5231 1.8764 1 15 7.6351 |
44. | 17 2013 -.0786 -4.1766 1 18 8.2193 |
45. | 17 2014 .1743 -.763 1 21 7.7906 |
|---------------------------------------------------------------|
46. | 17 2015 -.1965 -.9788 1 25 8.1429 |
47. | 17 2016 -.1103 9.7163 1 18 8.3316 |
48. | 17 2017 .0506 -.3675 1 27 8.1379 |
49. | 17 2018 .1814 -.6659 1 30 8.2736 |
50. | 17 2019 -.0796 6.247 1 32 8.4255 |
|---------------------------------------------------------------|
51. | 18 2010 .5881 14.2026 1 9 7.2411 |
52. | 18 2011 -.3009 2.7624 1 13 7.6161 |
53. | 18 2012 -.3235 1.6011 1 16 7.8348 |
54. | 18 2013 -.0251 -.475 1 19 8.0506 |
55. | 18 2014 .0942 -3.3848 1 22 7.8545 |
|---------------------------------------------------------------|
56. | 18 2015 .2437 .6239 1 10 7.6724 |
57. | 18 2016 -.0102 104.293 1 26 8.3551 |
58. | 18 2017 -.0137 2.9191 1 10 7.8026 |
59. | 18 2018 -.0233 -5.5515 1 29 8.0902 |
60. | 18 2019 -.0065 9.9573 1 24 8.0645 |
|---------------------------------------------------------------|
61. | 19 2010 -.2493 2.3172 1 10 7.4378 |
62. | 19 2011 1.4284 -1.852 1 14 7.1629 |
63. | 19 2012 .0712 -1.8513 1 17 7.7799 |
64. | 19 2013 -.0544 2.4029 1 20 7.6335 |
65. | 19 2014 -1.1617 1.3428 1 23 7.4574 |
|---------------------------------------------------------------|
66. | 19 2015 1.176 -.5245 1 9 7.4087 |
67. | 19 2016 .1101 -2.3712 1 22 7.9322 |
68. | 19 2017 .1062 3.1422 1 28 8.0342 |
69. | 19 2018 -.2579 3.6784 1 31 8.0925 |
70. | 19 2019 -.241 -3.6254 1 23 7.9284 |
|---------------------------------------------------------------|
71. | 20 2010 -.0035 0 1 11 8.026 |
72. | 20 2011 -.1693 29.175 1 8 8.04 |
73. | 20 2012 .0234 -.1639 1 12 7.9662 |
74. | 20 2013 -.9771 .4435 1 15 7.8255 |
75. | 20 2014 -.1569 2.3899 1 24 7.8841 |
|---------------------------------------------------------------|
76. | 20 2015 -.6982 1.328 1 15 7.8157 |
77. | 20 2016 -.6807 31.6126 1 25 8.0737 |
78. | 20 2017 -.0409 7.725 1 18 8.3676 |
79. | 20 2018 -.0465 -1.8241 1 21 8.0924 |
80. | 20 2019 .0339 6.865 1 33 7.9992 |
If you need any more information, please let me know.
Thank you in advance.
I'll try and keep this concise for ease:
I am looking at the relationship between football clubs financial performance and sporting performance. I have 80 observations over a 10 year time series but I am looking to compare the clubs that have a final position in the top 4 and bottom 4 each year. Due to relegation and promotion, the clubs are not the same each year.
Please could I have your comments on my data and recommendations on the best way to move forward? I have tried unbalanced regression based on clubs being the cross section, and balanced regression based on position as the cross section but I am unable to determine the best way forward. See below sample:
list
+---------------------------------------------------------------+
| position year roa leverage owncon id totala~s |
|---------------------------------------------------------------|
1. | 1 2010 -.2212 1.5945 1 1 8.546 |
2. | 1 2011 .0058 -1.017 2 2 9.2116 |
3. | 1 2012 -.1839 -.5771 1 5 8.7261 |
4. | 1 2013 -.0029 -.7975 2 2 9.1971 |
5. | 1 2014 -.0322 -.3265 1 5 8.8524 |
|---------------------------------------------------------------|
6. | 1 2015 -.0591 1.8232 1 1 8.7609 |
7. | 1 2016 .1423 -1.7165 1 7 8.1493 |
8. | 1 2017 -.0146 1.9694 1 1 8.8269 |
9. | 1 2018 .0147 0 1 5 8.8527 |
10. | 1 2019 .0014 0 1 5 8.876 |
|---------------------------------------------------------------|
11. | 2 2010 -.061 -1.0226 2 2 9.1371 |
12. | 2 2011 -.22 1.6279 1 1 8.5524 |
13. | 2 2012 .007 -.8983 2 2 9.2037 |
14. | 2 2013 -.0864 -.4002 1 5 8.7761 |
15. | 2 2014 .0018 6.101 1 6 8.3576 |
|---------------------------------------------------------------|
16. | 2 2015 .013 -.2563 1 5 8.9166 |
17. | 2 2016 .0018 -1.5406 2 3 8.9535 |
18. | 2 2017 .0562 -2.6997 1 4 8.8651 |
19. | 2 2018 .0192 -1.2992 2 2 9.2994 |
20. | 2 2019 .0473 -1.9207 1 6 8.8484 |
|---------------------------------------------------------------|
21. | 3 2010 .0777 -1.7183 2 3 8.8949 |
22. | 3 2011 -.387 -.7933 1 5 8.7079 |
23. | 3 2012 .0405 -1.3888 2 3 8.8689 |
24. | 3 2013 -.1169 1.7224 1 1 8.6796 |
25. | 3 2014 .0266 1.8719 1 1 8.7282 |
|---------------------------------------------------------------|
26. | 3 2015 .0229 -1.6273 2 3 8.943 |
27. | 3 2016 .0639 -2.0349 1 4 8.7137 |
28. | 3 2017 .0011 -.3563 1 5 9 |
29. | 3 2018 .1056 -2.8767 1 4 9.0294 |
30. | 3 2019 -.1214 2.2003 1 1 8.9458 |
|---------------------------------------------------------------|
31. | 4 2010 -.0229 -3.0874 1 4 8.4621 |
32. | 4 2011 .0174 -1.519 2 3 8.8603 |
33. | 4 2012 -.0149 -2.6632 1 4 8.4576 |
34. | 4 2013 .0075 -1.3999 2 3 8.8904 |
35. | 4 2014 .009 -1.5115 2 3 8.9093 |
|---------------------------------------------------------------|
36. | 4 2015 -.0244 -.9885 2 2 9.2397 |
37. | 4 2016 .0227 -.3184 1 5 8.9552 |
38. | 4 2017 .0845 -3.8897 1 6 8.6653 |
39. | 4 2018 .1874 -2.1317 1 6 8.7527 |
40. | 4 2019 .0584 -3.1972 1 4 9.1752 |
|---------------------------------------------------------------|
41. | 17 2010 -.1626 -7.9635 1 8 8.1037 |
42. | 17 2011 .0242 -.1619 1 12 7.9653 |
43. | 17 2012 -.5231 1.8764 1 15 7.6351 |
44. | 17 2013 -.0786 -4.1766 1 18 8.2193 |
45. | 17 2014 .1743 -.763 1 21 7.7906 |
|---------------------------------------------------------------|
46. | 17 2015 -.1965 -.9788 1 25 8.1429 |
47. | 17 2016 -.1103 9.7163 1 18 8.3316 |
48. | 17 2017 .0506 -.3675 1 27 8.1379 |
49. | 17 2018 .1814 -.6659 1 30 8.2736 |
50. | 17 2019 -.0796 6.247 1 32 8.4255 |
|---------------------------------------------------------------|
51. | 18 2010 .5881 14.2026 1 9 7.2411 |
52. | 18 2011 -.3009 2.7624 1 13 7.6161 |
53. | 18 2012 -.3235 1.6011 1 16 7.8348 |
54. | 18 2013 -.0251 -.475 1 19 8.0506 |
55. | 18 2014 .0942 -3.3848 1 22 7.8545 |
|---------------------------------------------------------------|
56. | 18 2015 .2437 .6239 1 10 7.6724 |
57. | 18 2016 -.0102 104.293 1 26 8.3551 |
58. | 18 2017 -.0137 2.9191 1 10 7.8026 |
59. | 18 2018 -.0233 -5.5515 1 29 8.0902 |
60. | 18 2019 -.0065 9.9573 1 24 8.0645 |
|---------------------------------------------------------------|
61. | 19 2010 -.2493 2.3172 1 10 7.4378 |
62. | 19 2011 1.4284 -1.852 1 14 7.1629 |
63. | 19 2012 .0712 -1.8513 1 17 7.7799 |
64. | 19 2013 -.0544 2.4029 1 20 7.6335 |
65. | 19 2014 -1.1617 1.3428 1 23 7.4574 |
|---------------------------------------------------------------|
66. | 19 2015 1.176 -.5245 1 9 7.4087 |
67. | 19 2016 .1101 -2.3712 1 22 7.9322 |
68. | 19 2017 .1062 3.1422 1 28 8.0342 |
69. | 19 2018 -.2579 3.6784 1 31 8.0925 |
70. | 19 2019 -.241 -3.6254 1 23 7.9284 |
|---------------------------------------------------------------|
71. | 20 2010 -.0035 0 1 11 8.026 |
72. | 20 2011 -.1693 29.175 1 8 8.04 |
73. | 20 2012 .0234 -.1639 1 12 7.9662 |
74. | 20 2013 -.9771 .4435 1 15 7.8255 |
75. | 20 2014 -.1569 2.3899 1 24 7.8841 |
|---------------------------------------------------------------|
76. | 20 2015 -.6982 1.328 1 15 7.8157 |
77. | 20 2016 -.6807 31.6126 1 25 8.0737 |
78. | 20 2017 -.0409 7.725 1 18 8.3676 |
79. | 20 2018 -.0465 -1.8241 1 21 8.0924 |
80. | 20 2019 .0339 6.865 1 33 7.9992 |
If you need any more information, please let me know.
Thank you in advance.
