NBA team stats analysis and championship prediction¶

Motivation:¶

NBA is a very well-known sport league in United States, and it has Eastern and Western Conferences with 30 teams. When people watch NBA basketball games, they care about team scoring stats, game results, individual player abilities, and whether or not the team they support will make the playoffs and win the final champion. So I want to create a project to analyze the scoring and defensive strength of each team, make a ranking, and predict the final champion based on the ranking data.

Data source¶

load the data in the format of dataframe¶

In [13]:
import pandas as pd
df = pd.read_csv("2022-2023 NBA Player Stats - Regular.csv", delimiter=";", encoding="latin-1", index_col=0)
df
Out[13]:
Player Pos Age Tm G GS MP FG FGA FG% ... FT% ORB DRB TRB AST STL BLK TOV PF PTS
Rk
1 Precious Achiuwa C 23 TOR 33 9 23.0 4.0 8.2 0.489 ... 0.697 2.1 4.3 6.4 1.1 0.7 0.7 1.2 2.2 10.4
2 Steven Adams C 29 MEM 42 42 27.0 3.7 6.3 0.597 ... 0.364 5.1 6.5 11.5 2.3 0.9 1.1 1.9 2.3 8.6
3 Bam Adebayo C 25 MIA 52 52 35.3 8.6 15.7 0.546 ... 0.806 2.7 7.3 10.1 3.3 1.2 0.8 2.6 2.8 21.6
4 Ochai Agbaji SG 22 UTA 35 1 14.0 1.5 3.2 0.486 ... 0.625 0.6 1.0 1.6 0.5 0.1 0.1 0.3 1.4 4.1
5 Santi Aldama PF 22 MEM 52 18 22.0 3.4 7.0 0.486 ... 0.730 1.0 3.7 4.7 1.2 0.7 0.7 0.7 1.9 9.5
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
501 Delon Wright PG 30 WAS 26 2 22.1 2.1 5.1 0.417 ... 0.903 0.9 2.2 3.1 3.7 1.9 0.3 0.8 1.4 6.0
502 McKinley Wright IV PG 24 DAL 19 1 10.2 1.0 2.4 0.422 ... 0.600 0.3 1.1 1.4 1.7 0.4 0.2 0.6 0.9 2.4
503 Thaddeus Young PF 34 TOR 45 9 16.1 2.2 4.0 0.562 ... 0.692 1.4 1.9 3.4 1.5 1.1 0.1 0.8 1.8 5.0
504 Trae Young PG 24 ATL 50 50 35.5 8.5 19.8 0.431 ... 0.887 0.7 2.3 3.0 10.2 1.0 0.2 4.2 1.5 26.9
505 Ivica Zubac C 25 LAC 57 57 29.4 4.0 6.4 0.617 ... 0.702 3.3 6.9 10.2 1.0 0.4 1.3 1.7 2.9 10.2

553 rows × 29 columns

the feature that each column presents¶

  • Rk : Rank
  • Player : Player's name
  • Pos : Position
  • Age : Player's age
  • Tm : Team
  • G : Games played
  • GS : Games started
  • MP : Minutes played per game
  • FG : Field goals per game
  • FGA : Field goal attempts per game
  • FG% : Field goal percentage
  • 3P : 3-point field goals per game
  • 3PA : 3-point field goal attempts per game
  • 3P% : 3-point field goal percentage
  • 2P : 2-point field goals per game
  • 2PA : 2-point field goal attempts per game
  • 2P% : 2-point field goal percentage
  • eFG% : Effective field goal percentage
  • FT : Free throws per game
  • FTA : Free throw attempts per game
  • FT% : Free throw percentage
  • ORB : Offensive rebounds per game
  • DRB : Defensive rebounds per game
  • TRB : Total rebounds per game
  • AST : Assists per game
  • STL : Steals per game
  • BLK : Blocks per game
  • TOV : Turnovers per game
  • PF : Personal fouls per game
  • PTS : Points per game

These information of NBA players can show their individual role and ability and also the strength of whole team. You can also use this link to access the data.

Research approach¶

We will choose some features of players to analyze. By providing proper weight to each feature, we can score each team in several aspects based on the same standard, and also a comprehensive score for each team. By comparing the scores between different teams, we can create a ranking and predict the future final champion.