Basketball
Basketball stats
Objective averages and our prognosis accuracy per competition. The percentages are PROGNOSIS ACCURACY, not profitability or betting value.
The % is the model's prognosis accuracy, not a return. The history is short (since late June) and grows over time, so leagues with few matches don't yet show a percentage.
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| League | Matches | Points/match | Winner accuracy | Points accuracy |
|---|---|---|---|---|
| Australia NBL1 South | 39 | 187.3 | 74% (59–85%)n=39 · medium sample | n=4insufficient sample |
| Australia NBL1 South Women | 39 | 154.8 | 72% (56–84%)n=39 · medium sample | n=1insufficient sample |
| Australia NBL1 East | 29 | 172.3 | 48% (31–66%)n=29 · low sample | n=1insufficient sample |
| Australia NBL1 East Women | 29 | 152.6 | 97% (83–99%)n=29 · low sample | n=1insufficient sample |
| Australia NBL1 West | 28 | 182.3 | 75% (57–87%)n=28 · low sample | n=1insufficient sample |
| Puerto Rico Superior Nacional | 25 | 185.2 | 56% (37–73%)n=25 · low sample | n=5insufficient sample |
| Australia NBL1 North | 23 | 189.1 | 87% (68–96%)n=23 · low sample | n=1insufficient sample |
| Australia NBL1 North Women | 22 | 151.8 | 59% (39–77%)n=22 · low sample | n=2insufficient sample |
| Australia NBL1 West Women | 22 | 163.4 | 73% (52–87%)n=22 · low sample | n=1insufficient sample |
| Uruguay Liga de Ascenso | 22 | 151.9 | 82% (62–93%)n=22 · low sample | – |
| Australia NBL1 Central | 20 | 191.7 | 80% (58–92%)n=20 · low sample | n=1insufficient sample |
| Australia NBL1 Central Women | 20 | 149.8 | 75% (53–89%)n=20 · low sample | – |
| New Zealand NBL | 20 | 180.6 | 70% (48–86%)n=20 · low sample | n=4insufficient sample |
| Australia Big V | 18 | 177.0 | 33% (16–56%)n=18 · low sample | – |
| Australia Big V Women | 17 | 142.9 | 88% (66–97%)n=17 · low sample | – |
| El Salvador LNB Segunda limited data | 12 | 140.5 | n=12insufficient sample | n=1insufficient sample |
| Vietnam VBA limited data | 12 | 172.4 | n=12insufficient sample | n=3insufficient sample |
| Dominican Republic LNB limited data | 11 | 178.7 | n=11insufficient sample | n=1insufficient sample |
| Uruguay Liga Women limited data | 11 | 130.1 | n=11insufficient sample | n=1insufficient sample |
| Chile Liga Nacional Women limited data | 9 | 130.4 | n=9insufficient sample | – |
| Chile LNB limited data | 9 | 156.8 | n=9insufficient sample | n=1insufficient sample |
| Mali Premiere Division limited data | 8 | 119.9 | n=8insufficient sample | n=1insufficient sample |
| El Salvador Liga Mayor limited data | 7 | 153.6 | n=7insufficient sample | n=3insufficient sample |
| Uganda Div 1 limited data | 7 | 115.0 | n=7insufficient sample | – |
| Uganda NBL Women limited data | 7 | 102.3 | n=7insufficient sample | – |
| Kenya Premier League Women limited data | 6 | 111.0 | n=6insufficient sample | – |
| Basketball Matches limited data | 5 | 145.8 | n=5insufficient sample | – |
| Canada EBL limited data | 5 | 174.4 | n=5insufficient sample | – |
| Rwanda National League limited data | 5 | 157.8 | n=5insufficient sample | n=2insufficient sample |
| Senegal Division 1 limited data | 5 | 114.6 | n=5insufficient sample | n=1insufficient sample |
| Senegal Division 1 Women limited data | 5 | 118.8 | n=5insufficient sample | – |
| Uganda NBL limited data | 5 | 122.2 | n=5insufficient sample | – |
| Brazil LBF Women limited data | 4 | 147.5 | n=4insufficient sample | – |
| Chile Liga SAESA limited data | 4 | 139.5 | n=4insufficient sample | n=1insufficient sample |
| Guatemala LMM limited data | 4 | 168.3 | n=4insufficient sample | – |
| Paraguay Primera limited data | 4 | 157.3 | n=4insufficient sample | n=1insufficient sample |
| Brazil Copa Sao Paulo limited data | 3 | 145.3 | n=3insufficient sample | – |
| Indonesia IBL limited data | 3 | 139.0 | n=3insufficient sample | n=1insufficient sample |
| Kenya Premier League limited data | 3 | 136.7 | n=3insufficient sample | – |
| Lebanon FLB limited data | 3 | 147.3 | n=3insufficient sample | – |
| Mali Ligue 1 Women limited data | 3 | 110.3 | n=3insufficient sample | n=2insufficient sample |
| Uganda Div 1 Women limited data | 3 | 126.7 | n=3insufficient sample | – |
| Nicaragua Torneo Carlos Ulloa limited data | 2 | 143.5 | n=2insufficient sample | – |
| The Asian Tournament limited data | 2 | 180.0 | n=2insufficient sample | n=1insufficient sample |
| Argentina La Liga Federal limited data | 1 | 102.0 | n=1insufficient sample | n=1insufficient sample |
| Belize Elite League limited data | 1 | 141.0 | n=1insufficient sample | – |
| Chile LNB Segunda limited data | 1 | 140.0 | n=1insufficient sample | – |
| Mexico CIBACOPA limited data | 1 | 137.0 | n=1insufficient sample | n=1insufficient sample |
| Morocco League limited data | 1 | 150.0 | n=1insufficient sample | – |
| Paraguay LNCF Women limited data | 1 | 110.0 | n=1insufficient sample | – |
| Rwanda National League Women limited data | 1 | 151.0 | n=1insufficient sample | – |
| Spain Liga ACB limited data | 1 | 192.0 | n=1insufficient sample | – |
| Syria League limited data | 1 | 162.0 | n=1insufficient sample | – |
Frequently asked questions
What does Over/Under points mean?
It is the game's total points against a line. Cross each team's pace and recent points for/against.
How does BetsTalent calculate probabilities?
With an in-house model: opponent-adjusted Elo and, in football, Poisson/Dixon-Coles with per-team attack and defence. In tennis, Elo by surface. No raw averages.