Italy - Serie A2 basketball (ITA-2)

Standings for 2007-2008 season

Rk Team % Victory Gp Gw GL Pts+ Pts- Pts+ /g Pts- /g Diff Expected
Winning %
1 Kleb Basket Ferrara Kleb Basket Ferrara 73.3 30 22 8 2414 2217 80.5 73.9 6.6 76.6
2 JuveCaserta JuveCaserta 66.7 30 20 10 2429 2256 81.0 75.2 5.8 73.6
3 Banco di Sardegna Sassari Banco di Sardegna Sassari 66.7 30 20 10 2452 2385 81.7 79.5 2.2 59.5
4 UNAHOTELS Reggio Emilia UNAHOTELS Reggio Emilia 63.3 30 19 11 2361 2214 78.7 73.8 4.9 71.0
5 Novipiù Monferrato Novipiù Monferrato 60.0 30 18 12 2360 2277 78.7 75.9 2.8 62.2
6 Vanoli Soresina Vanoli Soresina 60.0 30 18 12 2537 2485 84.6 82.8 1.8 57.2
7 Jesi Jesi 56.7 30 17 13 2477 2344 82.6 78.1 4.5 68.3
8 Giorgio Tesi Group Pistoia Giorgio Tesi Group Pistoia 56.7 30 17 13 2127 2131 70.9 71.0 -0.1 49.3
9 NTS Informatica Rimini NTS Informatica Rimini 53.3 30 16 14 2456 2371 81.9 79.0 2.9 62.0
10 Libertas Livorno Libertas Livorno 46.7 30 14 16 2341 2388 78.0 79.6 -1.6 43.1
11 ASD Pavia ASD Pavia 43.3 30 13 17 2419 2472 80.6 82.4 -1.8 42.5
12 Veroli Veroli 36.7 30 11 19 2305 2496 76.8 83.2 -6.4 24.8
13 Ristopro Fabriano Ristopro Fabriano 33.3 30 10 20 2348 2442 78.3 81.4 -3.1 36.7
14 Andrea Costa Imola Andrea Costa Imola 33.3 30 10 20 2226 2397 74.2 79.9 -5.7 26.3
15 Gema Montecatini Gema Montecatini 30.0 30 9 21 2233 2408 74.4 80.3 -5.9 25.9
16 Ignis Novara Ignis Novara 20.0 30 6 24 2305 2507 76.8 83.6 -6.8 23.7

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.