NBA Data Analysis:

Do Birthdays Affect Player Performance?

-In this Dataset I will explore NBA Data from the past 5 seasons of games and try to find out if player performance is changes when it is a player's birthday

Background Info:

- This data takes a look at the top 12 players per team based on total minutes

I chose to do this to remove some outliers of players who may on rarely play or do not get consistent minutes

- This data will explore three main statistical categories

Minutes Played: The amount of minutes played has a big impact on cumulative statistics like points, rebounds, assists, ect.

PRASB: A self created stat that is the sum of points, rebounds, assists, steals, and blocks combined

FG_PCT: Field goal percentage tracks the percentage of made shots divided by total shots. It is a crucial stat in tracking player efficiency

- I chose to examine players stats on the day of their birthday

Interested in seeing if players will have a boost in confidence or a change in their minutes patterns

- I chose to examine players stats on the day after their birthday as well

People like to celebrate their birthdays

Interested in seeing if there is a birthday hangover effect that causes stats to drop off

Retrieving Data:

-I pulled in my data from the official NBA API for player and team statistics 'nba_api' and converted them to CSV files to be used in this notebook

-API stands for "Application Programming Interface"

Allows for users to make calls and receive live data in real time

-I used it to pull in 4 separate datasets

mpg_df: Dataset that displays overall minutes played and minutes per game for each NBA player within the past 5 years

top_12_players_per_team: Dataset that narrows down mpg_df to the top 12 players from each team based on minutes and season

all_game_logs: Dataset that shows box scores for every game played by a player from top_12_players_per_team

aggregated stats: Dataset that returns the players statistical averages for each of the 5 seasons

Installing/Importing Packages:

Reading in my CSV Files as Dataframes:

mpg_df["PLAYER_NAME"].count()

#look at the dataframes mpg_df.head()

top_12_players_per_team['PLAYER_NAME'].count()

top_12_players_per_team.head()

#Graph displaying top_12_players_per_team dataset and its 1,800 data points fig_1.show()

Dropping Duplicate Values:

Data Analysis:

First Step was to compile a dataframe of players stats on their birthdays and average them by season

bday_yearly_avgs.head()

Next we need gather the same players season averages to compare with:

Showing the % difference in games played on birthdays and their season averages

# Percent Difference Between Games Played on Birthdays and Regular Season Averages plot fig_2.show()

#Percent Difference Between Games Played Day After Your Birthdays and Regular Season Averages fig_3.show()

Lets compare Performances on Birthdays to their previous 10 game averages

Entire season averages might not be a perfect fit

Over the course of an entire season averages smooth out

Previous 10 game average might be a better fit to show if a birthday provides a significant jump in performance

Showing the % differences between games played on birthdays and previous 10 game averages

# Percent Difference Between Games Played on Birthdays and Previous 10 Game Averages plot fig_4.show()

# Percent Difference Between Games Played the Day After your Birthday and Previous 10 Game Averages fig_5.show()

Takeaways:

From these results it is tough to say if a players birthday affects their performance

The percent differences are within the normal variance of game-to-game statistics

More Research would need to be done

combined_df

What I would do going forward:

Separate players by age and see if maturity and experience possibly plays a role

Break down data by day of week. If the players birthday is on a weeknight they likely aren't celebrating until the weekend

Drill into specific patterns by team. Head coaches usually control minute patterns and may treat birthdays differently

Skills I applied from our Data Science Class:

Python Coding

Implementation of Python Packages

How to use an API

Data Cleaning

Data Visualizations

Statistical Analysis

Creation of a Web Application using Deepnote