How well the model reads each competition
The same log that scores the model on the accuracy page, grouped by competition. Every prediction here was written down before kick-off and scored once the match finished; nothing is added afterwards. A competition appears once it has 25 settled predictions behind it, which 60 competitions now do.
The order is the plain hit rate — how often the model called the right result, home win, draw or away win, in that competition's matches. Nothing is smoothed or weighted on the way: whoever we have got right most often is top, whoever we have got wrong most often is last.
Which is exactly why the sample column matters. At 25 predictions the margin of error on a hit rate is around 18 percentage points, so the very top and the very bottom of this table are partly luck and will move. Read the number of predictions next to every figure. The middle of the table, where most sit near 48.9%, is the honest picture of this model.
Read best
- Primera División — 72.4% of 29 calls right.
- HNL — 63.0% of 54 calls right.
- Bundesliga — 61.7% of 60 calls right.
- Pro League — 60.3% of 63 calls right.
- Serie A — 60.0% of 90 calls right.
- Ligat Ha'al — 59.1% of 44 calls right.
- Süper Lig — 58.0% of 81 calls right.
- UEFA Champions League — 57.8% of 116 calls right.
Read worst
- Botola Pro — 23.7% of 38 calls right.
- Ligue 2 — 32.2% of 90 calls right.
- NB I — 34.4% of 64 calls right.
- 3. Liga — 35.0% of 40 calls right.
- Finnish Premier League — 35.6% of 59 calls right.
- K League 1 — 35.8% of 67 calls right.
- Liga MX — 39.7% of 116 calls right.
- Premier League — 40.0% of 80 calls right.
Every competition we have scored
All 60 of them, best-called first. The last column is the part that is about the model rather than about the football: how much ground it gained on a forecast that knew nothing and split every match three ways. A row can sit high on the hit rate and low on that one — a side or a league whose matches are easy to call flatters us — and where the two disagree, the last column is the one describing us.
| # | Competition | Predictions | Correct result | Exact score | Beat the baseline by |
|---|---|---|---|---|---|
| 1 |  Primera División | 29 | 72.4% | 10.3% | +18.1% |
| 2 |  HNL | 54 | 63.0% | 20.4% | +17.9% |
| 3 |  BundesligaAustria | 60 | 61.7% | 3.3% | +17.0% |
| 4 |  Pro League | 63 | 60.3% | 9.5% | +20.0% |
| 5 |  Serie AItaly | 90 | 60.0% | 13.3% | +16.5% |
| 6 |  Ligat Ha'al | 44 | 59.1% | 11.4% | +16.1% |
| 7 |  Süper Lig | 81 | 58.0% | 12.3% | +13.4% |
| 8 |  UEFA Champions League | 116 | 57.8% | 6.9% | +11.9% |
| 9 |  Jupiler Pro League | 80 | 57.5% | 10.0% | +16.9% |
| 10 |  Super League 1 | 54 | 57.4% | 5.6% | +13.7% |
| 11 |  Super Liga | 69 | 56.5% | 5.8% | +17.6% |
| 12 |  UEFA Nations League | 104 | 55.8% | 13.5% | +13.3% |
| 13 |  League One | 56 | 55.4% | 12.5% | +13.5% |
| 14 |  First League | 79 | 54.4% | 11.4% | +10.3% |
| 15 |  Premier LeagueEgypt | 48 | 54.2% | 14.6% | +7.2% |
| 16 |  Premier Soccer League | 58 | 53.4% | 15.5% | +8.7% |
| 17 |  Eliteserien | 87 | 52.9% | 10.3% | +11.6% |
| 18 |  UEFA Europa League | 122 | 52.5% | 8.2% | +16.7% |
| 19 |  Czech Liga | 94 | 52.1% | 11.7% | +2.6% |
| 20 |  Primeira Liga | 81 | 51.9% | 12.3% | +8.9% |
| 21 |  Ligue 1Algeria | 29 | 51.7% | 27.6% | +9.4% |
| 22 |  AFC Champions League Elite | 31 | 51.6% | 6.5% | +13.6% |
| 23 |  UEFA Europa Conference League | 242 | 50.8% | 5.8% | +7.6% |
| 24 |  BundesligaGermany | 63 | 50.8% | 11.1% | +6.9% |
| 25 |  Serie ABrazil | 142 | 50.0% | 9.2% | +8.9% |
| 26 |  Liga I | 100 | 50.0% | 13.0% | +7.3% |
| 27 |  Superliga | 72 | 50.0% | 11.1% | +6.0% |
| 28 |  Super LeagueSwitzerland | 66 | 50.0% | 7.6% | +7.2% |
| 29 |  Segunda División | 121 | 49.6% | 9.1% | +6.0% |
| 30 | La Liga | 99 | 49.5% | 13.1% | +8.4% |
| 31 |  Eredivisie | 90 | 48.9% | 7.8% | +4.2% |
| 32 | 1. Lig | 39 | 48.7% | 7.7% | +6.0% |
| 33 |  Erovnuli Liga | 37 | 48.6% | 2.7% | +8.4% |
| 34 |  Premier Division | 31 | 48.4% | 12.9% | +9.7% |
| 35 | Serie BBrazil | 44 | 47.7% | 25.0% | +4.6% |
| 36 |  Primera A | 63 | 47.6% | 9.5% | +6.9% |
| 37 |  Ligue 1France | 72 | 47.2% | 8.3% | +9.1% |
| 38 | 2. Bundesliga | 81 | 46.9% | 8.6% | +0.9% |
| 39 |  I Liga | 28 | 46.4% | 25.0% | +6.2% |
| 40 | Eerste Divisie | 37 | 45.9% | 8.1% | +4.6% |
| 41 |  Premiership | 66 | 45.5% | 10.6% | +5.7% |
| 42 |  Allsvenskan | 108 | 45.4% | 9.3% | +0.8% |
| 43 | Championship | 144 | 45.1% | 9.0% | +2.6% |
| 44 | Ekstraklasa | 103 | 44.7% | 10.7% | +8.6% |
| 45 |  Persian Gulf Pro League | 30 | 43.3% | 6.7% | +8.8% |
| 46 |  Stars League | 30 | 43.3% | 3.3% | +5.4% |
| 47 | League Two | 67 | 43.3% | 11.9% | +0.0% |
| 48 |  Major League Soccer | 257 | 42.0% | 10.5% | +2.1% |
| 49 |  Super LeagueChina | 60 | 41.7% | 3.3% | +2.9% |
| 50 | Serie BItaly | 90 | 41.1% | 7.8% | −1.8% |
| 51 |  Premier LeagueRussia | 73 | 41.1% | 9.6% | +2.3% |
| 52 |  Liga Profesional Argentina | 210 | 41.0% | 14.3% | +2.1% |
| 53 | Premier LeagueEngland | 80 | 40.0% | 12.5% | +3.5% |
| 54 |  Liga MX | 116 | 39.7% | 4.3% | −0.2% |
| 55 |  K League 1 | 67 | 35.8% | 6.0% | −2.4% |
| 56 |  Finnish Premier League | 59 | 35.6% | 15.3% | −6.6% |
| 57 | 3. Liga | 40 | 35.0% | 5.0% | −1.8% |
| 58 |  NB I | 64 | 34.4% | 6.3% | −0.4% |
| 59 | Ligue 2 | 90 | 32.2% | 4.4% | −9.0% |
| 60 |  Botola Pro | 38 | 23.7% | 5.3% | −2.2% |
The same question, club by club
A league is the sturdier sample, but the match in front of you is played by two clubs, not by a league — which is why that table uses a bar of 5. How well the model reads each club is the same record at the other level.
A statistical estimate, not betting advice. Nothing here predicts a future match; it records how the model has done so far.