Italy - LBA Serie A basketball (ITA-1)

Standings for 2010-2011 season

Rk Team % Victory Gp Gw GL Pts+ Pts- Pts+ /g Pts- /g Diff Expected
Winning %
1 OnSharing Siena OnSharing Siena 86.7 30 26 4 2514 2159 83.8 72.0 11.8 89.3
2 Acqua S.Bernardo Cantù Acqua S.Bernardo Cantù 73.3 30 22 8 2311 2132 77.0 71.1 5.9 75.4
3 EA7 Emporio Armani Milan EA7 Emporio Armani Milan 70.0 30 21 9 2409 2295 80.3 76.5 3.8 66.2
4 Scandone Avellino Scandone Avellino 56.7 30 17 13 2468 2378 82.3 79.3 3.0 62.6
5 Benetton Treviso Benetton Treviso 56.7 30 17 13 2311 2252 77.0 75.1 1.9 58.9
6 Virtus Segafredo Bologna Virtus Segafredo Bologna 50.0 30 15 15 2345 2358 78.2 78.6 -0.4 48.1
7 Pallacanestro Varese Pallacanestro Varese 50.0 30 15 15 2385 2430 79.5 81.0 -1.5 43.5
8 Banco di Sardegna Sassari Banco di Sardegna Sassari 50.0 30 15 15 2463 2512 82.1 83.7 -1.6 43.2
9 Virtus Roma Virtus Roma 46.7 30 14 16 2323 2262 77.4 75.4 2.0 59.1
10 Carpegna Prosciutto Pesaro Carpegna Prosciutto Pesaro 46.7 30 14 16 2186 2260 72.9 75.3 -2.4 38.6
11 Vanoli Cremona Vanoli Cremona 40.0 30 12 18 2384 2395 79.5 79.8 -0.3 48.4
12 JuveCaserta JuveCaserta 40.0 30 12 18 2395 2422 79.8 80.7 -0.9 46.1
13 Sutor Montegranaro Sutor Montegranaro 36.7 30 11 19 2314 2382 77.1 79.4 -2.3 40.1
14 Edilnol Biella Edilnol Biella 36.7 30 11 19 2393 2487 79.8 82.9 -3.1 36.9
15 BT Teramo BT Teramo 33.3 30 10 20 2253 2458 75.1 81.9 -6.8 22.9
16 Happy Casa Brindisi Happy Casa Brindisi 26.7 30 8 22 2194 2466 73.1 82.2 -9.1 16.4

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.