nba player trade

nba news link:

Suns acquire Kevin Durant from Nets in blockbuster deal

In the 2022-23 season, the most eye-catching transaction is the transfer of Kevin Durant from the Nets to the Suns. Every team wants to win the championship, and every team wants to buy stronger players to help them achieve this goal. The performance of every NBA player will be recorded as reference data, so how do these data work during the transfer?

I would like to analyze the 'elo' ranking of throughout the 2022-23 player stats in order to find out the reason behind the transfer.

dataset I would like to use

source:

2022-2023 NBA Player Stats

load and show dataset

columns stands for:

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

import pandas as pd
player_stats = '2022-2023 NBA Player Stats - Regular.csv'
df_player = pd.read_csv(player_stats, delimiter=";", encoding="latin-1", index_col=0)
df_player

	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

How will I generate the dataset?

The project will use this dataset to calculate the player's elo score and analyze its strength. At the same time, it will compare the elo score with the transaction price to reflect its role in the transfer.