Basketball
Basketball stats
Objective averages and our prognosis accuracy per competition. The percentages are PROGNOSIS ACCURACY, not profitability or betting value.
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Formula 1
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 |
|---|---|---|---|---|
| Uruguay Liga de Ascenso | 112 | 154.4 | 73% (64–81%)n=112 · large sample | n=6insufficient sample |
| Chile LNB | 78 | 162.3 | 81% (71–88%)n=78 · medium sample | n=5insufficient sample |
| Puerto Rico Superior Nacional | 59 | 182.2 | 54% (42–66%)n=59 · medium sample | n=11insufficient sample |
| Australia NBL1 West | 53 | 188.6 | 74% (60–84%)n=53 · medium sample | n=2insufficient sample |
| Australia NBL1 South | 51 | 188.0 | 75% (61–85%)n=51 · medium sample | n=5insufficient sample |
| Australia NBL1 South Women | 50 | 156.7 | 72% (58–83%)n=50 · medium sample | n=3insufficient sample |
| Australia NBL1 East | 47 | 169.5 | 60% (45–72%)n=47 · medium sample | n=4insufficient sample |
| Australia NBL1 East Women | 45 | 152.2 | 87% (74–94%)n=45 · medium sample | n=1insufficient sample |
| Vietnam VBA | 43 | 174.7 | 84% (70–92%)n=43 · medium sample | n=6insufficient sample |
| El Salvador LNB Segunda | 41 | 148.0 | 83% (69–92%)n=41 · medium sample | n=6insufficient sample |
| Uruguay Liga Women | 41 | 128.7 | 71% (56–82%)n=41 · medium sample | n=4insufficient sample |
| New Zealand NBL | 40 | 181.9 | 70% (55–82%)n=40 · medium sample | 67% (42–85%)n=15 · low sample |
| Australia NBL1 North | 37 | 194.2 | 87% (72–94%)n=37 · medium sample | n=2insufficient sample |
| Australia NBL1 North Women | 37 | 163.5 | 65% (49–78%)n=37 · medium sample | n=2insufficient sample |
| Brazil FPB | 37 | 155.8 | 76% (60–87%)n=37 · medium sample | n=1insufficient sample |
| Paraguay Primera | 32 | 161.7 | 91% (76–97%)n=32 · medium sample | n=6insufficient sample |
| Rwanda National League | 29 | 168.4 | 72% (54–85%)n=29 · low sample | n=2insufficient sample |
| Australia NBL1 Central | 26 | 185.5 | 73% (54–86%)n=26 · low sample | n=2insufficient sample |
| Australia NBL1 Central Women | 26 | 148.1 | 73% (54–86%)n=26 · low sample | n=1insufficient sample |
| Brazil Paulista FPB U20 | 23 | 148.6 | 91% (73–98%)n=23 · low sample | n=4insufficient sample |
| Australia NBL1 West Women | 22 | 163.4 | 73% (52–87%)n=22 · low sample | n=1insufficient sample |
| Canada EBL | 22 | 183.1 | 77% (57–90%)n=22 · low sample | – |
| China NWBC Women | 22 | 142.7 | 73% (52–87%)n=22 · low sample | n=1insufficient sample |
| Uganda NBL | 22 | 137.3 | 68% (47–84%)n=22 · low sample | n=1insufficient sample |
| El Salvador Liga Mayor | 21 | 162.9 | 81% (60–92%)n=21 · low sample | n=4insufficient sample |
| Uganda NBL Women | 21 | 117.3 | 76% (55–89%)n=21 · low 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 | – |
| Lebanon FLB | 15 | 170.3 | 80% (55–93%)n=15 · low sample | n=2insufficient sample |
| Dominican Republic LNB limited data | 14 | 176.6 | n=14insufficient sample | n=1insufficient sample |
| El Salvador Liga Mayor Women limited data | 14 | 136.1 | n=14insufficient sample | n=1insufficient sample |
| Rwanda National League Women limited data | 14 | 134.8 | n=14insufficient sample | – |
| Mexico LNBP limited data | 13 | 171.7 | n=13insufficient sample | – |
| Kenya Premier League limited data | 11 | 133.0 | n=11insufficient sample | n=2insufficient sample |
| Chile Liga Nacional Women limited data | 10 | 134.6 | n=10insufficient sample | – |
| Mali Premiere Division limited data | 10 | 127.1 | n=10insufficient sample | n=1insufficient sample |
| Brazil LBF Women limited data | 9 | 137.3 | n=9insufficient sample | – |
| El Salvador Fesabal U21 limited data | 9 | 138.0 | n=9insufficient sample | n=1insufficient sample |
| Iraq Premier League limited data | 9 | 156.8 | n=9insufficient sample | n=5insufficient sample |
| Kenya Premier League Women limited data | 8 | 112.3 | n=8insufficient sample | n=1insufficient sample |
| Senegal Division 1 limited data | 7 | 118.1 | n=7insufficient sample | n=1insufficient sample |
| Senegal Division 1 Women limited data | 7 | 121.1 | n=7insufficient sample | – |
| Sports Based Games limited data | 7 | 121.0 | n=7insufficient sample | – |
| Uganda Div 1 limited data | 7 | 115.0 | n=7insufficient sample | – |
| Basketball Matches limited data | 6 | 147.5 | n=6insufficient sample | – |
| Belgium Top Division 1 limited data | 6 | 162.5 | n=6insufficient sample | n=1insufficient sample |
| Chile Liga SAESA limited data | 6 | 144.5 | n=6insufficient sample | n=1insufficient sample |
| Chile LNB Segunda limited data | 6 | 154.5 | n=6insufficient sample | – |
| Guatemala LMM limited data | 6 | 162.3 | n=6insufficient sample | – |
| Turkiye TBL limited data | 6 | 150.3 | n=6insufficient sample | – |
| Rwanda BLD2 limited data | 5 | 155.0 | n=5insufficient sample | n=1insufficient sample |
| Nicaragua Torneo Carlos Ulloa limited data | 4 | 165.5 | n=4insufficient sample | – |
| Australia NBL1 limited data | 3 | 181.3 | n=3insufficient sample | – |
| Australia NBL1 Women limited data | 3 | 139.3 | n=3insufficient sample | – |
| Brazil Copa Sao Paulo limited data | 3 | 145.3 | n=3insufficient sample | – |
| Brazil Paulista Women limited data | 3 | 129.0 | n=3insufficient sample | – |
| Indonesia IBL limited data | 3 | 139.0 | n=3insufficient sample | n=1insufficient sample |
| Mali Ligue 1 Women limited data | 3 | 110.3 | n=3insufficient sample | n=2insufficient sample |
| Syria League limited data | 3 | 163.0 | n=3insufficient sample | – |
| Uganda Div 1 Women limited data | 3 | 126.7 | n=3insufficient sample | – |
| Belize Elite League limited data | 2 | 139.5 | n=2insufficient sample | – |
| Brazil LDB U22 limited data | 2 | 160.0 | n=2insufficient sample | – |
| China CBA Summer League limited data | 2 | 152.0 | n=2insufficient sample | – |
| Korea WKBL limited data | 2 | 131.0 | n=2insufficient sample | n=1insufficient sample |
| Philippines NCAA limited data | 2 | 153.0 | n=2insufficient sample | – |
| Argentina La Liga Federal limited data | 1 | 102.0 | n=1insufficient sample | n=1insufficient sample |
| Brazil Camp.Carioca U22 limited data | 1 | 144.0 | 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 | – |
| Spain Liga ACB limited data | 1 | 192.0 | 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.