Lithuania - NKL
Standings
Lithuania - NKL basketball (LIT-2)
Standings for 2019-2020 season
Rk | Team | % Victory | Gp | Gw | GL | Pts+ | Pts- | Pts+ /g | Pts- /g | Diff | Expected Winning % |
---|---|---|---|---|---|---|---|---|---|---|---|
1 | Suduva | 82.1 | 39 | 32 | 7 | 3350 | 2981 | 85.9 | 76.4 | 9.5 | 83.5 |
2 | Telsiai | 79.5 | 39 | 31 | 8 | 3294 | 2995 | 84.5 | 76.8 | 7.7 | 79.0 |
3 | Silute | 69.2 | 39 | 27 | 12 | 3337 | 3112 | 85.6 | 79.8 | 5.8 | 72.5 |
4 | Klaipėdos Neptūnas-Akvaservis | 56.4 | 39 | 22 | 17 | 3077 | 2973 | 78.9 | 76.2 | 2.7 | 61.7 |
5 | Zalgiris Kaunas II | 52.6 | 38 | 20 | 18 | 3202 | 3165 | 84.3 | 83.3 | 1.0 | 54.0 |
6 | Gargzdu | 50.0 | 38 | 19 | 19 | 3171 | 3113 | 83.4 | 81.9 | 1.5 | 56.4 |
7 | Vytis Sakiai | 50.0 | 38 | 19 | 19 | 3148 | 3120 | 82.8 | 82.1 | 0.7 | 53.1 |
8 | M Basket-Delamode | 48.7 | 37 | 18 | 19 | 3113 | 3130 | 84.1 | 84.6 | -0.5 | 48.1 |
9 | Palangos Kuršiai | 47.4 | 38 | 18 | 20 | 3168 | 3087 | 83.4 | 81.2 | 2.2 | 58.9 |
10 | Delikatesas | 43.6 | 39 | 17 | 22 | 3198 | 3168 | 82.0 | 81.2 | 0.8 | 53.3 |
11 | Vilniaus Perlas Energija | 41.0 | 39 | 16 | 23 | 3310 | 3449 | 84.9 | 88.4 | -3.5 | 36.1 |
12 | Moletu Ezerunas-Atletas | 40.5 | 37 | 15 | 22 | 2939 | 3100 | 79.4 | 83.8 | -4.4 | 32.3 |
13 | Jonavos CBet | 37.8 | 37 | 14 | 23 | 2907 | 3094 | 78.6 | 83.6 | -5.0 | 29.6 |
14 | Silales Lusis | 27.0 | 37 | 10 | 27 | 2815 | 3142 | 76.1 | 84.9 | -8.8 | 17.8 |
15 | Taurages | 21.1 | 38 | 8 | 30 | 2948 | 3348 | 77.6 | 88.1 | -10.5 | 14.6 |
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.