Italy - LBA Serie A basketball (ITA-1)

Standings for 1987-1988 season

Rk Team % Victory Gp Gw GL Pts+ Pts- Pts+ /g Pts- /g Diff Expected
Winning %
1 Pallacanestro Varese Pallacanestro Varese 76.7 30 23 7 2840 2661 94.7 88.7 6.0 71.2
2 EA7 Emporio Armani Milan EA7 Emporio Armani Milan 70.0 30 21 9 3055 2906 101.8 96.9 4.9 66.7
3 Acqua S.Bernardo Cantù Acqua S.Bernardo Cantù 66.7 30 20 10 2815 2678 93.8 89.3 4.5 66.7
4 JuveCaserta JuveCaserta 63.3 30 19 11 2927 2858 97.6 95.3 2.3 58.2
5 Carpegna Prosciutto Pesaro Carpegna Prosciutto Pesaro 60.0 30 18 12 2823 2760 94.1 92.0 2.1 57.8
6 Virtus Segafredo Bologna Virtus Segafredo Bologna 60.0 30 18 12 2695 2687 89.8 89.6 0.2 51.0
7 Akern Libertas Livorno Akern Libertas Livorno 50.0 30 15 15 2782 2700 92.7 90.0 2.7 60.3
8 Virtus Roma Virtus Roma 46.7 30 14 16 2776 2721 92.5 90.7 1.8 56.9
9 Allibert Livorno Allibert Livorno 46.7 30 14 16 2576 2589 85.9 86.3 -0.4 48.3
10 Reale Mutua Torino Reale Mutua Torino 46.7 30 14 16 2711 2740 90.4 91.3 -0.9 46.3
11 Umana Reyer Venezia (M) Umana Reyer Venezia (M) 46.7 30 14 16 2855 2919 95.2 97.3 -2.1 42.4
12 Benetton Treviso Benetton Treviso 40.0 30 12 18 2485 2555 82.8 85.2 -2.4 40.5
13 Firenze Firenze 36.7 30 11 19 2689 2801 89.6 93.4 -3.8 36.2
14 Jcoplastic Napoli Jcoplastic Napoli 36.7 30 11 19 2634 2746 87.8 91.5 -3.7 35.9
15 Rimadesio Desio Rimadesio Desio 33.3 30 10 20 2568 2684 85.6 89.5 -3.9 35.1
16 Basket Brescia Basket Brescia 20.0 30 6 24 3035 3261 101.2 108.7 -7.5 26.9

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.