As I did in 2018, 2017, 2016, 2015, and 2014, I've compiled my preseason NCAAF rankings that take into account player turnover and recruiting classes.
There's a slightly new wrinkle this year though. As before, I used my final MDS Model ratings from last season as the base, and then factored in ESPN's Preseason FPI and the S&P+ projections (which are now housed over at ESPN with Bill Connelly leaving SB Nation), but this year I've also included Ed Feng's rating system from The Power Rank. Once the season starts, this "preseason" rating will be faded out as the season progresses, carrying less and less weight with each ensuing week.
As I have in the past, I missed Week 0, but got these ratings out before the first full weekend of games.
In the below list, the "Trend" indicates whether the respective team's new ranking rose or fell relative to last year's preseason ratings. No team movement in or out of FBS this year though!
My goodness are Alabama and Clemson evenly matched, and in a class of their own:
Alabama: 0.95035035751657
Clemson: 0.949045941382027
| Rank | Team | PRESEASON | Trend |
| 1 | Alabama | 0.950 | UP |
| 2 | Clemson | 0.949 | UP |
| 3 | Georgia | 0.851 | UP |
| 4 | Oklahoma | 0.813 | UP |
| 5 | LSU | 0.808 | UP |
| 6 | Ohio State | 0.806 | DOWN |
| 7 | Michigan | 0.793 | UP |
| 8 | Notre Dame | 0.792 | DOWN |
| 9 | Auburn | 0.784 | DOWN |
| 10 | Penn State | 0.743 | DOWN |
| 11 | Florida | 0.740 | UP |
| 12 | Washington | 0.732 | DOWN |
| 13 | Texas A&M | 0.731 | UP |
| 14 | Mississippi State | 0.723 | DOWN |
| 15 | Michigan State | 0.712 | DOWN |
| 16 | Oregon | 0.695 | UP |
| 17 | Wisconsin | 0.691 | DOWN |
| 18 | Missouri | 0.690 | UP |
| 19 | Miami (FL) | 0.669 | DOWN |
| 20 | Utah | 0.667 | UP |
| 21 | South Carolina | 0.660 | UP |
| 22 | Florida State | 0.654 | DOWN |
| 23 | Oklahoma State | 0.653 | DOWN |
| 24 | Iowa | 0.645 | UP |
| 25 | USC | 0.644 | DOWN |
| 26 | Texas | 0.636 | DOWN |
| 27 | Stanford | 0.635 | DOWN |
| 28 | UCF | 0.630 | UP |
| 29 | Virginia Tech | 0.629 | DOWN |
| 30 | Tennessee | 0.627 | UP |
| 31 | Iowa State | 0.627 | UP |
| 32 | TCU | 0.625 | DOWN |
| 33 | Boise State | 0.622 | DOWN |
| 34 | Washington State | 0.622 | UP |
| 35 | Baylor | 0.610 | UP |
| 36 | UCLA | 0.605 | UP |
| 37 | Minnesota | 0.603 | UP |
| 38 | Ole Miss | 0.602 | DOWN |
| 39 | Kentucky | 0.593 | UP |
| 40 | Nebraska | 0.585 | UP |
| 41 | North Carolina State | 0.583 | DOWN |
| 42 | Syracuse | 0.581 | UP |
| 43 | Memphis | 0.577 | UP |
| 44 | Arizona State | 0.575 | UP |
| 45 | West Virginia | 0.571 | DOWN |
| 46 | Virginia | 0.570 | UP |
| 47 | Northwestern | 0.569 | DOWN |
| 48 | Texas Tech | 0.568 | DOWN |
| 49 | Purdue | 0.568 | UP |
| 50 | Duke | 0.566 | DOWN |
| 51 | Pittsburgh | 0.563 | UP |
| 52 | Appalachian State | 0.554 | UP |
| 53 | Brigham Young | 0.552 | UP |
| 54 | Indiana | 0.547 | UP |
| 55 | Arizona | 0.546 | DOWN |
| 56 | North Carolina | 0.544 | DOWN |
| 57 | Vanderbilt | 0.536 | UP |
| 58 | Cincinnati | 0.535 | UP |
| 59 | Wake Forest | 0.532 | DOWN |
| 60 | California | 0.529 | DOWN |
| 61 | Arkansas | 0.524 | DOWN |
| 62 | Kansas State | 0.523 | DOWN |
| 63 | Louisville | 0.515 | DOWN |
| 64 | Boston College | 0.514 | DOWN |
| 65 | Fresno State | 0.508 | UP |
| 66 | Houston | 0.503 | DOWN |
| 67 | Temple | 0.495 | UP |
| 68 | Georgia Tech | 0.495 | DOWN |
| 69 | Utah State | 0.495 | UP |
| 70 | Maryland | 0.490 | UP |
| 71 | San Diego State | 0.489 | DOWN |
| 72 | South Florida | 0.479 | DOWN |
| 73 | Marshall | 0.477 | DOWN |
| 74 | Northern Illinois | 0.469 | UP |
| 75 | Toledo | 0.468 | DOWN |
| 76 | Colorado | 0.464 | UP |
| 77 | Western Michigan | 0.459 | UP |
| 78 | Florida Atlantic | 0.453 | DOWN |
| 79 | Army | 0.452 | UP |
| 80 | Ohio | 0.449 | DOWN |
| 81 | Southern Miss | 0.447 | UP |
| 82 | Arkansas State | 0.440 | DOWN |
| 83 | Georgia Southern | 0.438 | UP |
| 84 | Air Force | 0.437 | UP |
| 85 | Southern Methodist | 0.425 | DOWN |
| 86 | Troy | 0.425 | UP |
| 87 | Illinois | 0.421 | UP |
| 88 | North Texas | 0.417 | UP |
| 89 | Louisiana Tech | 0.412 | DOWN |
| 90 | Wyoming | 0.403 | DOWN |
| 91 | Tulane | 0.400 | UP |
| 92 | Middle Tennessee | 0.397 | DOWN |
| 93 | Florida International | 0.394 | UP |
| 94 | Tulsa | 0.387 | UP |
| 95 | Oregon State | 0.380 | UP |
| 96 | Miami (OH) | 0.380 | DOWN |
| 97 | Nevada | 0.379 | UP |
| 98 | Rutgers | 0.374 | DOWN |
| 99 | Western Kentucky | 0.373 | UP |
| 100 | Hawaii | 0.373 | UP |
| 101 | UAB | 0.363 | UP |
| 102 | Eastern Michigan | 0.360 | UP |
| 103 | Navy | 0.349 | DOWN |
| 104 | Buffalo | 0.348 | DOWN |
| 105 | Louisiana-Monroe | 0.345 | UP |
| 106 | Kansas | 0.341 | DOWN |
| 107 | Colorado State | 0.339 | DOWN |
| 108 | Louisiana-Lafayette | 0.336 | UP |
| 109 | UNLV | 0.325 | DOWN |
| 110 | Ball State | 0.324 | UP |
| 111 | East Carolina | 0.309 | UP |
| 112 | Texas State | 0.307 | UP |
| 113 | Liberty | 0.285 | UP |
| 114 | Georgia State | 0.276 | UP |
| 115 | Coastal Carolina | 0.274 | UP |
| 116 | Central Michigan | 0.274 | DOWN |
| 117 | San Jose State | 0.269 | UP |
| 118 | New Mexico | 0.260 | DOWN |
| 119 | Akron | 0.255 | DOWN |
| 120 | Kent State | 0.250 | UP |
| 121 | Bowling Green | 0.244 | DOWN |
| 122 | Charlotte | 0.234 | UP |
| 123 | New Mexico State | 0.234 | DOWN |
| 124 | UTSA | 0.229 | DOWN |
| 125 | Massachusetts | 0.218 | DOWN |
| 126 | South Alabama | 0.213 | DOWN |
| 127 | Old Dominion | 0.206 | DOWN |
| 128 | Connecticut | 0.201 | DOWN |
| 129 | Rice | 0.162 | DOWN |
| 130 | UTEP | 0.118 | DOWN |
Men's Tennis (ATP)
Roger Federer is arguably the greatest tennis player of all-time. He's spent 310 weeks at #1 in the ATP world rankings, including 237 consecutive weeks; both are all-time records. He has 20 Grand Slam titles, also the all-time record.
| Player | Grand Slams | Rank | Weeks at #1 | Rank | Win % | Rank | Career Wins | Rank | Total Titles | Rank |
| Federer | 20 | 1 | 310 | 1 | 82.2% | 3 | 1,222 | 1 | 102 | 1 |
| Nadal | 18 | 2 | 196 | 3 | 83.0% | 1 | 956 | 2 | 82 | 2 |
| Djokovic | 16 | 3 | 260 | 2 | 82.7% | 2 | 871 | 3 | 75 | 3 |
But he also has a strange hole in his resume; he has a losing record to two other players who have had their primes overlap with his: 22-26 vs Djokovic and 16-24 vs Nadal.
| H2H Wins, Overall |
| Player | Federer | Nadal | Djokovic | Total |
| Federer | - | 16 | 22 | 38 |
| Nadal | 24 | - | 26 | 50 |
| Djokovic | 26 | 28 | - | 54 |
| Total | 50 | 44 | 48 | 142 |
So can you be the GOAT, yet have 2 players be strictly better than you head-to-head?
This pattern holds up when you look at the matches that matter most: in both Grand Slams and finals, Federer does worse:
| H2H Wins, In Grand Slams |
| Player | Federer | Nadal | Djokovic | Total |
| Federer | - | 4 | 6 | 10 |
| Nadal | 10 | - | 9 | 19 |
| Djokovic | 10 | 6 | - | 16 |
| Total | 20 | 10 | 15 | 45 |
| H2H Wins, In Finals |
| Player | Federer | Nadal | Djokovic | Total |
| Federer | - | 10 | 6 | 16 |
| Nadal | 14 | - | 11 | 25 |
| Djokovic | 13 | 15 | - | 28 |
| Total | 27 | 25 | 17 | 69 |
You see this kind of intransitive property all the time in sports, especially in college football and college basketball. But tennis is an individual sport, so any variance in performance should theoretically be explained by that player, as opposed to a collection of players on a team.
But tennis does have one significant variable: the surface that the tournament is played on.
| H2H Wins, Grass |
| Player | Federer | Nadal | Djokovic | Total |
| Federer | - | 3 | 1 | 4 |
| Nadal | 1 | - | 2 | 3 |
| Djokovic | 3 | 2 | - | 5 |
| Total | 4 | 5 | 3 | 12 |
| H2H Wins, Hard |
| Player | Federer | Nadal | Djokovic | Total |
| Federer | - | 11 | 17 | 28 |
| Nadal | 9 | - | 7 | 16 |
| Djokovic | 19 | 19 | - | 38 |
| Total | 28 | 30 | 24 | 82 |
| H2H Wins, Clay |
| Player | Federer | Nadal | Djokovic | Total |
| Federer | - | 2 | 4 | 6 |
| Nadal | 14 | - | 17 | 31 |
| Djokovic | 4 | 7 | - | 11 |
| Total | 18 | 9 | 21 | 48 |
Federer is 37 and has been a pro for 21 years, Nadal: 33 and 18, Djokovic: 32 and 16. So it might just be too early to tell. Each player's career isn't over yet, so all I can do is present this information without offering a conclusion.
To answer the overall "GOAT" question, I think you have to consider the overall body of work first and foremost. But that's an opinion - I don't really have a data-driven answer from the above evidence.