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Post by Deleted on Nov 20, 2017 19:09:53 GMT -5
Am new to this metric-maze methodology of prediction; yet your "futures" seem to predict the future RPIs vis a' vis what the committee will be seeing/choosing from. Granted: you use this 'Pablo' (an early critic of RPI here on VT A Ways back in the past?), yet the Committee does not consider-use Pablo, Massey, nor any other "metric". Why do you? I like to think of this as a projection and not a prediction...
I use Pablo to project wins and losses (or % chance of winning and losing each match) so that I can have a placeholder for the other 333 teams. This allows me to answer the question for that 1 (each) team; 'If Team A wins X matches for the season, then their approximate (projected) RPI would be Y'. Or; 'how many wins does Team A need to win for an RPI ~ 16?'
Another way to look at this is; 'If Team A wins the expected number of matches (per Pablo) on the season, they will finish with approximately an RPI rank of Y. And if they want a better RPI, they will have to exceed the Pablo expectations.'
What I found, the RPI SOS becomes very predictable early in the season using the Pablo ratings - and once the RPI SOS becomes relatively known, we can quickly start answering that question of how many matches does my team need to win to achieve X goal.
While you think of this as a projection, over the course of this year's Pick the Winners Contest, using Pablo as a predictor was significantly better than using RPI. Picking the exact same contests, Pablo was accurate 73.3% of the time, compared with RPI's 67.0%.
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bluepenquin
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Post by bluepenquin on Nov 20, 2017 19:28:15 GMT -5
I like to think of this as a projection and not a prediction...
I use Pablo to project wins and losses (or % chance of winning and losing each match) so that I can have a placeholder for the other 333 teams. This allows me to answer the question for that 1 (each) team; 'If Team A wins X matches for the season, then their approximate (projected) RPI would be Y'. Or; 'how many wins does Team A need to win for an RPI ~ 16?'
Another way to look at this is; 'If Team A wins the expected number of matches (per Pablo) on the season, they will finish with approximately an RPI rank of Y. And if they want a better RPI, they will have to exceed the Pablo expectations.'
What I found, the RPI SOS becomes very predictable early in the season using the Pablo ratings - and once the RPI SOS becomes relatively known, we can quickly start answering that question of how many matches does my team need to win to achieve X goal.
While you think of this as a projection, over the course of this year's Pick the Winners Contest, using Pablo as a predictor was significantly better than using RPI. Picking the exact same contests, Pablo was accurate 73.3% of the time, compared with RPI's 67.0%. To be clear, I am not creating a metric or a projection/predictor model of who the best teams are or have been. I am only projecting an RPI rank - fully recognizing the limitations of RPI. In other words, I am not endorsing RPI as a viable metric - I am just projecting (predicting) where the final RPI will land.
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bluepenquin
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Post by bluepenquin on Nov 20, 2017 19:30:27 GMT -5
Here are individual probability scenarios.
This reads, If Penn State wins 19 conference matches (there is a 50% chance of this happening), they will end up with an average RPI rank of 1.2 and finish 1st 79% of the time.
2. Penn State - Big Ten, Avg RPI Rank - 2.449 (2) T4 - 90% T16 - 100% T45 - 100% T90 - 100% 19W (50%), Avg Rank - 1.2: 1st (79%), 2nd (17%) 18W (41%), Avg Rank - 3.1; 2nd (22%), 3rd (40%), 4th (30%) 17W (9%), Avg Rank - 6.3; 5th (16%), 6th (30%), 7th (53%)
6. Nebraska - Big Ten, Avg RPI Rank - 5.707 (6) T4 - 9% T16 - 100% T45 - 100% T90 - 100% 19W (87%), Avg Rank - 5.6; 3rd (1%), 4th (9%), 5th (31%), 6th (48%), 7th (10%) 18W (12%), Avg Rank - 6.6; 5th (4%), 6th (39%), 7th (54%), 8th (3%)
7. Minnesota - Big Ten, Avg RPI Rank - 6.595 (7) T4 - 6% T16 - 100% T45 - 100% T90 - 100% 16W (26%), Avg Rank - 5.3; 3rd (7%), 4th (17%), 5th (25%), 6th (37%), 7th (13%) 15W (74%), Avg Rank - 7.0; 5th (1%), 6th (9%), 7th (74%), 8th (16%)
13. Wisconsin - Big Ten, Avg RPI Rank - 13.16 (12) T4 - 0% T16 - 85% T45 - 100% T90 - 100% 12W (32%), Avg Rank - 9.8; 9th (46%), 10th (34%), 11th (14%), 12th (5%), 13h (1%) 11W (67%), Avg Rank - 14.8; 14th or better (46%), 15th (17%), 16th (15%), 17th (10%), 18th (6%), 19th or more (6%)
17. Michigan State - Big Ten, Avg RPI Rank - 17.073 (17) T4 - 0% T16 - 51% T45 - 100% T90 - 100% 15W (52%), Avg Rank - 13.1; 12th or better (40%), 14th or better (80%), 16th or better (97%) 14W (45%), Avg Rank - 21.1; 16th or better (1%), 17th (2%), 18th (5%), 19th (10%) 13W (4%), Avg Rank - 24.7;
24. Purdue - Big Ten, Avg RPI Rank - 22.353 (25) T4 - 0% T16 - 7% T45 - 100% T90 - 100% 13W (29%), Avg Rank - 17.5; 16th or better (25%), 17th (23%), 18th (27%), 19th (18%) 12W (53%), Avg Rank - 23.7; 25th or better (98%) 11W (18%), Avg Rank - 26.4; 25th or better (19%)
27. Illinois - Big Ten, Avg RPI Rank - 26.949 (27) T4 - 0% T16 - 0% T45 - 100% T90 - 100% 12W (47%), Avg Rank - 25.4; 25th or better (51%) 11W (44%), Avg Rank - 28.1; 25th or better <1%) 10W (8%), Avg Rank - 29.9;
31. Michigan - Big Ten, Avg RPI Rank - 31.086 (31) T4 - 0% T16 - 0% T45 - 100% T90 - 100% 11W (26%), Avg Rank - 29.3; 25th or better (1%) 10W (57%), Avg Rank - 31.0; 9W (17%), Avg Rank - 34.1;
43. Ohio State - Big Ten, Avg RPI Rank - 43.457 (39) T4 - 0% T16 - 0% T45 - 65% T90 - 100% 9W (15%), Avg Rank - 36.2; 8W (48%), Avg Rank - 41.5; Below .500 overall record 7W (37%), Avg Rank - 49.0; Below .500 overall record, 50th or better (73%)
48. Iowa - Big Ten, Avg RPI Rank - 45.718 (48) T4 - 0% T16 - 0% T45 - 37% T90 - 100% 8W (29%), Avg Rank - 40.2; 45th or better (99%) 7W (69%), Avg Rank - 48.4; 45th or better (9%), 50th or better (77%)
49. Maryland - Big Ten, Avg RPI Rank - 46.854 (49) T4 - 0% T16 - 0% T45 - 40% T90 - 100% 8W (39%), Avg Rank - 41.2; 45th or better (99%) 7W (45%), Avg Rank - 48.5; 45th or better (1%), 50th or better (86%) 6W (17%), Avg Rank - 55.5;
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bluepenquin
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Post by bluepenquin on Nov 20, 2017 19:31:02 GMT -5
5. Stanford - Pac 12, Avg RPI Rank - 4.673 (5) T4 - 45% T16 - 100% T45 - 100% T90 - 100% 19W (74%), Avg Rank - 4.2; 3rd (14%), 4th (42%), 5th (39%) 18W (25%), Avg Rank - 6.0; 4th (4%), 5th (18%), 6th (54%), 7th (24%)
8. Washington - Pac 12, Avg RPI Rank - 7.879 (8) T4 - 0% T16 - 100% T45 - 100% T90 - 100% 14W (76%), Avg Rank - 7.8; 13W (22%), Avg Rank - 8.0;
9. Utah - Pac 12, Avg RPI Rank - 11.778 (9) T4 - 0% T16 - 91% T45 - 100% T90 - 100% 14W (12%), Avg Rank - 9.0; 13W (50%), Avg Rank - 10.0; 9th (35%). 10th (40%), 11th (20%), 12th (4%) 12W (37%), Avg Rank - 15.2; 14th or better (36%), 16th or better (76%), 17th (13%), 18th (6%)
11. USC - Pac 12, Avg RPI Rank - 12.164 (10) T4 - 0% T16 - 81% T45 - 100% T90 - 100% 15W (23%), Avg Rank - 9.2; 9th (82%), 10th (16%) 14W (51%), Avg Rank - 10.8; 10th (11%), 11th (30%), 12th (35%), 13th (18%) 13W (26%), Avg Rank - 17.4; 16th or better (29%), 17th (20%), 18th (25%), 19th (14%)
20. Colorado - Pac 12, Avg RPI Rank - 20.12 (21) T4 - 0% T16 - 23% T45 - 100% T90 - 100% 13W (37%), Avg Rank - 15.7; 16th or better (62%), 17th (18%), 18th (12%), 19th (5%) 12W (51%), Avg Rank - 22.2; 20th or better (15%) 11W (12%), Avg Rank - 24.7; 25th or better (80%)
21. Oregon - Pac 12, Avg RPI Rank - 20.75 (22) T4 - 0% T16 - 21% T45 - 100% T90 - 100% 11W (25%), Avg Rank - 14.7; 16th or better (87%), 17th (10%), 18th (3%) 10W (52%), Avg Rank - 21.5; 20th or better (24%) 9W (24%), Avg Rank - 25.4; 25th or better (54%)
23. UCLA - Pac 12, Avg RPI Rank - 22.338 (24) T4 - 0% T16 - 12% T45 - 100% T90 - 100% 12W (24%), Avg Rank - 16.5; 16th or better (51%), 17th (23%), 18th (16%), 19th (7%) 11W (50%), Avg Rank - 23.1; 10W (26%), Avg Rank - 26.3; 25th or better (30%)
26. Oregon State - Pac 12, Avg RPI Rank - 26.73 (26) T4 - 0% T16 - 0% T45 - 100% T90 - 100% 13W (27%), Avg Rank - 24.0; 25th or better (89%) 12W (51%), Avg Rank - 26.9; 25th or better (7%) 11W (22%), Avg Rank - 29.6;
37. Washington State - Pac 12, Avg RPI Rank - 39.561 (37) T4 - 0% T16 - 0% T45 - 88% T90 - 100% 7W (13%), Avg Rank - 33.4; 6W (66%), Avg Rank - 38.8 ; 5W (21%), Avg Rank - 46.1; 45th or better (38%), 50th or better (99%)
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Post by bluepenquin on Nov 20, 2017 19:32:18 GMT -5
4. Texas - Big 12, Avg RPI Rank - 3.946 (4) T4 - 63% T16 - 100% T45 - 100% T90 - 100% 16W (57%), Avg Rank - 2.9; 1st (5%), 2nd (24%), 3rd (48%), 4th (24%) 15W (42%), Avg Rank - 5.3; 4th (13%), 5th (44%), 6th (37%)
10. Baylor - Big 12, Avg RPI Rank - 12.04 (11) T4 - 0% T16 - 97% T45 - 100% T90 - 100% 14W (41%), Avg Rank - 9.6; 13W (59%), Avg Rank - 13.8; 16th or better (94%)
12. Kansas - Big 12, Avg RPI Rank - 12.77 (13) T4 - 0% T16 - 87% T45 - 100% T90 - 100% 12W (82%), Avg Rank - 11.7; 12th or better (75%), 14th or better (93%), 16th or better (99%) 11W (18%), Avg Rank - 17.5; 16th or better (33%)
14. Iowa State - Big 12, Avg RPI Rank - 13.831 (14) T4 - 0% T16 - 88% T45 - 100% T90 - 100% 11W (88%), Avg Rank - 13.1; 12th or better (33%), 14th or better (84%), 16th or better (99%) 10W (12%), Avg Rank - 19.3;
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Post by bluepenquin on Nov 20, 2017 19:33:09 GMT -5
1. Florida - SEC, Avg RPI Rank - 2.049 (1) T4 - 97% T16 - 100% T45 - 100% T90 - 100% 17W (74%), Avg Rank - 1.6; 1st (52%), 2nd (38%), 3rd (10%) 16W (24%), Avg Rank - 3.3; 2nd (20%), 3rd (35%), 4th (34%)
3. Kentucky - SEC, Avg RPI Rank - 2.707 (3) T4 - 90% T16 - 100% T45 - 100% T90 - 100% 17W (57%), Avg Rank - 2.0; 1st (27%), 2nd (48%), 3rd (23%) 16W (37%), Avg Rank - 3.4; 2nd (16%), 3rd (31%), 4th (36%), 5th (13%)
33. Missouri - SEC, Avg RPI Rank - 33.725 (33) T4 - 0% T16 - 0% T45 - 100% T90 - 100% 14W (13%), Avg Rank - 30.2; 13W (64%), Avg Rank - 33.5; 12W (23%), Avg Rank - 37.1;
42. LSU - SEC, Avg RPI Rank - 42.36 (43) T4 - 0% T16 - 0% T45 - 75% T90 - 100% 12W (32%), Avg Rank - 36.1; 11W (48%), Avg Rank - 42.6; 40th or better (22%), 45th or better (88%) 10W (20%), Avg Rank - 51.7; 50th or better (28%)
45. Auburn - SEC, Avg RPI Rank - 44.686 (45) T4 - 0% T16 - 0% T45 - 49% T90 - 100% 10W (4%), Avg Rank - 34.1; 9W (42%), Avg Rank - 40.6; 40th or better (48%), 45th or better (99%) 8W (54%), Avg Rank - 48.7: 45th or better (7%), 50th or better (77%)
55. Arkansas - SEC, Avg RPI Rank - 53.505 (55) T4 - 0% T16 - 0% T45 - 16% T90 - 100% 10W (16%), Avg Rank - 41.9: 40th or better (22%), 45th or better (98%) 9W (53%), Avg Rank - 53.6; 50th or better (5%) 8W (31%), Avg Rank - 59.4;
56. Georgia - SEC, Avg RPI Rank - 56.633 (58) T4 - 0% T16 - 0% T45 - 6% T90 - 100% 11W (11%), Avg Rank - 45.0: 45th or better (56%) 10W (44%), Avg Rank - 55.1: 9W (45%), Avg Rank - 60.9;
59. Alabama - SEC, Avg RPI Rank - 58.665 (59) T4 - 0% T16 - 0% T45 - <1% T90 - 100% 8W (15%), Avg Rank - 51.3; 50th or better (32%) 7W (54%), Avg Rank - 58.0; 6W (31%), Avg Rank - 63.3;
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Post by bluepenquin on Nov 20, 2017 19:33:46 GMT -5
25. Louisville - ACC, Avg RPI Rank - 23.113 (23) T4 - 0% T16 - 0% T45 - 100% T90 - 100% 18W (73%), Avg Rank -224; 17W (26%), Avg Rank - 25.0; 25th or better (74%)
29. Pittsburgh - ACC, Avg RPI Rank - 28.289 (28) T4 - 0% T16 - 0% T45 - 100% T90 - 100% 18W (86%), Avg Rank - 28.0; 25th or better (2%) 17W (14%), Avg Rank - 29.8;
35. Miami-FL - ACC, Avg RPI Rank - 34.743 (34) T4 - 0% T16 - 0% T45 - 99% T90 - 100% 15W (58%), Avg Rank - 32.8: 14W (39%), Avg Rank - 36.9:
36. Notre Dame - ACC, Avg RPI Rank - 37.439 (36) T4 - 0% T16 - 0% T45 - 97% T90 - 100% 13W (65%), Avg Rank - 33.3: 12W (32%), Avg Rank - 40.6; 40th or better (49%), 45th or better (99%)
40. NC State - ACC, Avg RPI Rank - 42.042 (41) T4 - 0% T16 - 0% T45 - 75% T90 - 100% 16W (57%), Avg Rank - 38.7; 40th or better (91%) 15W (40%), Avg Rank - 45.7; 45th or better (46%)
47. Florida State - ACC, Avg RPI Rank - 45.626 (46) T4 - 0% T16 - 0% T45 - 55% T90 - 100% 12W (56%), Avg Rank - 41.5; 40th or better (27%) 11W (42%), Avg Rank - 50.4; 50th or better (53%) 10W (19%), Avg Rank - 55.6;
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Post by bluepenquin on Nov 20, 2017 19:34:24 GMT -5
18. BYU - West Coast, Avg RPI Rank - 17.699 (18) T4 - 0% T16 - 30% T45 - 100% T90 - 100% 17W (88%), Avg Rank - 17.1; 14th or better (11%), 15th (10%), 16th (14%), 17th (20%), 18th (20%) 16W (12%), Avg Rank - 22.1; 25th or better (99%)
22. San Diego - West Coast, Avg RPI Rank - 21.184 (20) T4 - 0% T16 - <0% T45 - 100% T90 - 100% 17W (90%), Avg Rank - 20.8; 20th or better (39%) 16W (10%), Avg Rank - 24.2; 25th or better (89%)
19. Cal Poly - Big West, Avg RPI Rank - 19.355 (19) T4 - 0% T16 - 2% T45 - 100% T90 - 100% 16th or better (2%), 20th or better (81%)
38. Hawaii - Big West, Avg RPI Rank - 39.919 (38) T4 - 0% T16 - 0% T45 - 99% T90 - 100% 40th or better (62%), 41st (16%), 42nd (11%), 43rd (5%), 44th (3%), 45th (2%)
16. Wichita State - American Athletic, Avg RPI Rank - 15.911 (16) T4 - 0% T16 - 60% T45 - 100% T90 - 100% 20W (97%), Avg Rank - 15.7; 12th or better (8%), 14th or better (29%), 16th or better (62%)
28. Western Kentucky - Conference USA, Avg RPI Rank - 27.634 (29) T4 - 0% T16 - 0% T45 - 100% T90 - 100% 25th or better (2%)
32. Colorado State - Mountain West, Avg RPI Rank - 32.222 (32) T4 - 0% T16 - 0% T45 - 100% T90 - 100% Between 29 and 38
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Post by toomuchvb on Nov 20, 2017 19:57:20 GMT -5
So is this to imply the Final Four could most likely be PSU, Texas, Kentucky, and Florida, based on results through Week 13? Thank you.
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Post by ProfessorPlum on Nov 20, 2017 20:25:00 GMT -5
While you think of this as a projection, over the course of this year's Pick the Winners Contest, using Pablo as a predictor was significantly better than using RPI. Picking the exact same contests, Pablo was accurate 73.3% of the time, compared with RPI's 67.0%. To be clear, I am not creating a metric or a projection/predictor model of who the best teams are or have been. I am only projecting an RPI rank - fully recognizing the limitations of RPI. In other words, I am not endorsing RPI as a viable metric - I am just projecting (predicting) where the final RPI will land.
You should probably put that statement on your tag line. I get it. Sports isn’t played in a vacuum and you just statiscally try to measure where the RPI will finish because it’s one of the main tools the committee uses to pick the field. Who has a guess to how many at-large teams come from variations outside the RPI model. For instance if every team gets in up to 44 RPI and the 45th doesn’t get in. How many teams get in above 45? The number could be 38 for all I know and 10 get in after 39 doesnt. Any idea historically? And Ohio St doesn’t count in this exercise.
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Post by Wiswell on Nov 20, 2017 21:15:04 GMT -5
So on the list, Iowa and Maryland are adjacent.
If you pick one you pick....Maryland right?
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bluepenquin
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Post by bluepenquin on Nov 20, 2017 23:10:06 GMT -5
I have now digested the updated RPI and probabilities and here is where I am at for the final 8 seeds. I will put them in tiers.
Tier 1: Utah, Kansas, USC, Baylor, and Wisconsin. I think these teams are most likely to get a seed, needing just one more win (in the case of Baylor, I think they are likely even with a loss). Utah and USC could still be a seed while losing twice (maybe). I would expect 4 of these 5 to be seeds and think there is a decent chance all 5 become seeds.
Tier 2: Creighton, Michigan State, Oregon. I think these 3 teams are in the same boat - they have to win both matches this week, and then I think they are in. They each have a clear path to a seed, but each has less than a 50% chance of winning both matches. Probably just 1 or 2 of these will make it.
Tier 3: Iowa State. I think ISU will end up being a seed (assuming they win their last match), however they will need help. At least one of the teams above will need to 'fail', maybe two of them.
Tier 4: UCLA, BYU, Colorado, Purdue - these teams will need multiple things to go right in order to get a seed - in addition to winning all their matches. UCLA probably has the clearest path. BYU is positioned to be a seed as a default if several teams end up playing their way out.
Tier 5: Wichita State. It will take a LOT of things to go right.
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bluepenquin
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Post by bluepenquin on Nov 20, 2017 23:12:03 GMT -5
So is this to imply the Final Four could most likely be PSU, Texas, Kentucky, and Florida, based on results through Week 13? Thank you. Nope - just that they are most likely to be the top 4 in the final RPI at the end of the season. I don't think this will even translate to the top 4 seeds, let alone the 4 teams that make the FF.
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Post by TCMullet on Nov 21, 2017 1:09:13 GMT -5
Am new to this metric-maze methodology of prediction; yet your "futures" seem to predict the future RPIs vis a' vis what the committee will be seeing/choosing from. Granted: you use this 'Pablo' (an early critic of RPI here on VT A Ways back in the past?), yet the Committee does not consider-use Pablo, Massey, nor any other "metric". Why do you? I like to think of this as a projection and not a prediction... Projection or prediction, no matter to me. I think it's great! My goals require me to know as much as can possibly be known what teams will be in the top 32, and I need to know that at the beginning. Obviously, not possible, but by week 4, things were firm enough (at least in 2016) that the top 40 or so *contained* the top 32. Wasn't quite so nice this year (lots of changes since week 4). But I'm very grateful to Bluepenguin for his mastery of this "whatever it is" that provides these insights. And speaking of top 32, I've discovered a need to give attention to the current "top 10". Comparing week 13 to 12, Kansas dropped off (the top 10) and Utah jumped on, with all the rest simply shifting up or down zero or 1 slot. Then going to 13's probabilities list, USC dropped off and Baylor jumped on. So if I keep Kansas, then I guess I have a fairly well defined "Big 12", ha ha! Again, thank you Bluepenguin for this amazing body of work and info. And to Dirtrider5 I say, maybe the NCAA needs to listen to Bluepenguin. But whether they do or not doesn't affect the validity of the charts, at least for me. But remember that (for my purposes) I don't care who's at 1 or at 32; I just want to know the set of teams in the top 32ish.
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Post by gobruins on Nov 21, 2017 8:03:36 GMT -5
How do you think that the fact that the UCLA-USC match won't end until around midnight, eastern time on Saturday night; will effect the committee's deliberations?
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