FIBA Europe Cup
Standings
FIBA Europe Cup basketball (EU-3)
Standings for 2007-2008 season
Rk | Team | % Victory | Gp | Gw | GL | Pts+ | Pts- | Pts+ /g | Pts- /g | Diff | Expected Winning % |
---|---|---|---|---|---|---|---|---|---|---|---|
1 | Barons | 72.7 | 11 | 8 | 3 | 851 | 812 | 77.4 | 73.8 | 3.6 | 65.8 |
2 | Belfius Mons-Hainaut | 70.0 | 10 | 7 | 3 | 760 | 692 | 76.0 | 69.2 | 6.8 | 78.6 |
3 | AEL Limassol | 70.0 | 10 | 7 | 3 | 785 | 736 | 78.5 | 73.6 | 4.9 | 71.0 |
4 | Tartu Ülikool Maks & Moorits | 63.6 | 11 | 7 | 4 | 861 | 825 | 78.3 | 75.0 | 3.3 | 64.4 |
5 | Samara | 62.5 | 8 | 5 | 3 | 611 | 605 | 76.4 | 75.6 | 0.8 | 53.4 |
6 | Ural-Great Perm | 55.6 | 9 | 5 | 4 | 681 | 657 | 75.7 | 73.0 | 2.7 | 62.2 |
7 | Zagreb CO | 55.6 | 9 | 5 | 4 | 689 | 698 | 76.6 | 77.6 | -1.0 | 45.5 |
8 | Khimik Yuzhny | 50.0 | 8 | 4 | 4 | 590 | 593 | 73.8 | 74.1 | -0.3 | 48.2 |
9 | Olympia Larissa | 50.0 | 6 | 3 | 3 | 400 | 416 | 66.7 | 69.3 | -2.6 | 36.7 |
10 | Cherkasy Monkeys | 50.0 | 6 | 3 | 3 | 465 | 507 | 77.5 | 84.5 | -7.0 | 23.1 |
11 | Lokomotiv Kuban | 33.3 | 6 | 2 | 4 | 432 | 438 | 72.0 | 73.0 | -1.0 | 45.2 |
12 | Cholet Basket | 33.3 | 6 | 2 | 4 | 430 | 461 | 71.7 | 76.8 | -5.1 | 27.5 |
13 | Banvit | 16.7 | 6 | 1 | 5 | 473 | 490 | 78.8 | 81.7 | -2.9 | 38.0 |
14 | Lappeenrannan NMKY | 16.7 | 6 | 1 | 5 | 483 | 506 | 80.5 | 84.3 | -3.8 | 34.4 |
15 | Spartak St.Petersb. | 16.7 | 6 | 1 | 5 | 436 | 458 | 72.7 | 76.3 | -3.6 | 33.5 |
16 | PAOK | 16.7 | 6 | 1 | 5 | 418 | 471 | 69.7 | 78.5 | -8.8 | 16.0 |
Standings glossary
Stats abbreviations
- Rk: rank
- % Victory: number of win / number of games played
- Gp: number of games played
- Gw: number of games won
- GL: number of games lost
- Pts+: total number of points scored by the team
- Pts-: total number of points scored by opposing teams
- Pts+ /g: total number of points scored by the team per game
- Pts- /g: total number of points scored by opposing teams per game
- Diff: difference between points scored and received per game
- Expected Winning %: through our basketball statistical database and the use of advanced stats, we are able to project a team’s win percentage which then allows us to project how many wins a team is expected to have. These projections are a unique way to understand whether a team has played better or worse than their record indicates.