| Rank | Overall | Change | SOS (Rank) | Standard (Rank) | Inference (Rank) | Vegas (Rank) |
|---|---|---|---|---|---|---|
| 1. Seattle Sounders (4-0-1) | 0.4706 | - | 0.1012 (5) | 0.4959 (1) | 0.5918 (1) | 0.3244 (2) |
| 2. Los Angeles FC (1-1-2) | 0.4059 | - | 0.0953 (7) | 0.2230 (4) | 0.3473 (2) | 0.5869 (1) |
| 3. New York City (2-1-1) | 0.2426 | +1 | -0.1526 (26) | 0.2665 (2) | 0.3094 (4) | 0.1822 (4) |
| 4. Columbus Crew (1-1-2) | 0.1826 | -1 | -0.1100 (25) | 0.1803 (7) | 0.1975 (5) | 0.1700 (5) |
| 5. Orlando City (1-0-3) | 0.1769 | - | -0.0556 (21) | 0.1847 (6) | 0.1328 (7) | 0.2131 (3) |
| 6. Portland Timbers (1-3-0) | 0.1367 | - | 0.0293 (12) | 0.2327 (3) | 0.0658 (11) | 0.1116 (10) |
| 7. Nashville SC (1-0-3) | 0.1325 | - | -0.1568 (27) | 0.0919 (14) | 0.1912 (6) | 0.1143 (9) |
| 8. New England Revolution (2-1-2) | 0.1276 | +1 | -0.0581 (22) | 0.1228 (11) | 0.1183 (9) | 0.1417 (8) |
| 9. Colorado Rapids (2-1-1) | 0.0993 | +2 | -0.0179 (19) | 0.1402 (10) | 0.0540 (12) | 0.1038 (11) |
| 10. FC Dallas (1-1-2) | 0.0991 | - | -0.0146 (17) | 0.0886 (15) | 0.1257 (8) | 0.0831 (13) |
| 11. Philadelphia Union (1-2-2) | 0.0856 | +1 | 0.0848 (8) | 0.2047 (5) | -0.0156 (16) | 0.0379 (16) |
| 12. Sporting Kansas City (2-2-1) | 0.0715 | -4 | -0.0172 (18) | 0.0977 (13) | -0.0394 (20) | 0.1559 (6) |
| 13. Toronto FC (1-2-1) | 0.0621 | +3 | -0.0643 (23) | 0.1171 (12) | -0.0524 (22) | 0.0913 (12) |
| 14. Los Angeles Galaxy (3-1-0) | 0.0578 | -1 | 0.1704 (2) | 0.1708 (8) | -0.0371 (19) | 0.0396 (15) |
| 15. Austin FC (2-2-0) | 0.0417 | -1 | 0.1420 (3) | 0.1429 (9) | 0.3311 (3) | -0.2879 (23) |
| 16. Minnesota United (1-4-0) | 0.0113 | -1 | 0.0616 (9) | -0.0228 (16) | 0.0718 (10) | -0.0151 (18) |
| 17. New York Red Bulls (2-2-0) | -0.0358 | - | 0.0453 (10) | -0.1352 (18) | -0.0345 (18) | 0.0618 (14) |
| 18. Atlanta United (1-1-2) | -0.0663 | - | 0.0414 (11) | -0.2370 (24) | -0.0426 (21) | 0.0055 (17) |
| 19. Real Salt Lake (2-1-0) | -0.0800 | - | -0.0009 (15) | -0.1704 (20) | -0.0189 (17) | -0.0508 (20) |
| 20. San Jose Earthquakes (3-2-0) | -0.0855 | +1 | -0.0253 (20) | -0.0319 (17) | 0.0230 (13) | -0.2472 (22) |
| 21. Inter Miami CF (1-2-2) | -0.1390 | -1 | 0.0028 (14) | -0.2233 (22) | -0.3498 (24) | 0.1551 (7) |
| 22. Montreal Impact (2-1-2) | -0.1703 | - | -0.0031 (16) | -0.1661 (19) | 0.0202 (14) | -0.3346 (24) |
| 23. Houston Dynamo (2-1-2) | -0.1787 | +1 | 0.1216 (4) | -0.2297 (23) | -0.2679 (23) | -0.1288 (21) |
| 24. Vancouver Whitecaps (2-2-1) | -0.2437 | -1 | 0.0278 (13) | -0.1798 (21) | -0.0021 (15) | -0.5484 (27) |
| 25. Chicago Fire (0-4-1) | -0.3197 | - | -0.0663 (24) | -0.3915 (27) | -0.5323 (26) | -0.0361 (19) |
| 26. D.C. United (2-3-0) | -0.4074 | - | 0.0955 (6) | -0.3805 (25) | -0.4310 (25) | -0.4108 (26) |
| 27. FC Cincinnati (0-2-1) | -0.4154 | - | 0.1873 (1) | -0.3847 (26) | -0.5771 (27) | -0.4047 (25) |
Ratings Over Time
Ratings Methodology
Below are listed the current MLS Soccer computer ratings as computed by the Donchess Inference Index. The MLS ratings will be updated every week during the season. More information on how these MLS soccer computer ratings are computed can be found on the Methodology page.
- DII Rating: This is a MLS soccer rankings system that is a combination of the Standard Rating and the Aegis Rating. The Aegis Rating is weighted more highly in the beginning of the season whereas the Standard Rating is weighted more highly at the end of the season. This is the final rating that is used to rank the teams.
- Standard Rating: Looks at game outcome and score differential only. Record is the most important factor in this rating system.
- Inference Rating: Heavily weights the outcomes of opponents’ games. This is system is a much better predictor of future game outcomes.
- Vegas Rating: A rating system based on what the Vegas line is on each game. This rating system is a strong predictor of future games, but does not heavily weight the outcomes of previous games. This rating is used for predictions, but not used for the overall rating.
For our daily predictions on all the games, see MLS Soccer Predictions.
Related Blog Posts
Could a College Basketball Team Beat an NBA Team?
Discussing whether or not the best college basketball team could beat the worst NBA team on a random night.
Chasing Steam in Sports Betting
Explaining steam moves and what it means to “chase steam” in the betting world.
2021 March Madness Breakdown
Our breakdowns for each of the brackets in the 2021 NCAA Tournament.
Strategy For Building a Winning Parlay Bet
Although they are typically regarded as sucker bets for casual bettors, one can make money betting into parlays.
What is Closing Line Value (CLV)?
What metric should be used to determine who the good sports bettors are?