Women's College Basketball Team Rosters

Women's College Basketball Team Rosters for the 2022-2023 Season

By Andy Kriebel [source]

About this dataset

The dataset consists of three files: wbb_years_cleaned.csv, wbb_rosters_2022_23.csv, and wbb_teams.csv.

The wbb_years_cleaned.csv file contains data on the count of women's college basketball teams and their rosters for each year. It focuses specifically on the 2022-2023 season and includes information such as the number of players in each team's roster and whether any players are on redshirt status.

The wbb_rosters_2022_23.csv file provides insights into the height distribution within women's college basketball teams during the 2022-2023 season. This file specifically examines how heights vary among players within a team.

Lastly, the wbb_teams.csv file offers detailed information about individual women's college basketball teams participating in the 2022-2023 season. It includes essential details like team names, Twitter handles, URLs to their profiles or websites, NCAA IDs (identification numbers), conference affiliations, and division affiliations.

This dataset is derived from sources curated by Derek Willis from Sports Data Analysis & Visualization class at Merrill College. The original visualization showcasing this dataset presents intriguing height distribution patterns across different women's college basketball teams during a specific period.

Overall, this dataset serves as a valuable resource for analyzing various aspects related to women's college basketball in terms of player demographics (such as height), positional roles played by individuals within a team hierarchy (primary position vs. secondary position), educational backgrounds (high schools attended and previous schools attended), hometowns, team affiliations at the conference and division level, and social media presence (Twitter handles)

How to use the dataset

Dataset Overview

The dataset consists of three CSV files: wbb_years_cleaned.csv, wbb_rosters_2022_23.csv, and wbb_teams.csv.

wbb_years_cleaned.csv

This file contains data on the count of women's college basketball teams and their rosters for each year, with a focus on the 2022-2023 season. It includes columns such as: - year: The year of the women's college basketball season. - count: The number of players in the roster for that particular year. - redshirt: Indicates whether a player is on redshirt status for that particular year.

wbb_rosters_2022_23.csv

This file contains data on the height distribution of women's college basketball teams in the 2022-2023 season. It includes columns such as: - team: The name of the women's college basketball team. - height_ft: The height of players in feet. - height_in: The height of players in inches. - total_inches: The total height of players in inches.

wbb_teams.csv

This file contains information about women’s college basketball teams in the 2022–2033 season, including team names, Twitter handles, URLs, NCAA IDs, conference/division affiliations, and more. It includes columns such as: - team_state: The state where the women’s college basketball team is located. - conference: The conference to which each team belongs.

Getting Started

• Download or import all three CSV files into your preferred data analysis or visualization tool, such as Python with pandas, R with dplyr, or Excel.

• Familiarize yourself with the available columns in each dataset. Refer to the provided Columns section for detailed information on each column's meaning and format.

• Determine your research questions or objectives based on the available data. Here are a few examples of what you can explore using this dataset:

  • Analyze the height distribution of women's college basketball teams in the 2022-2023 season.
  • Investigate how different conferences vary in team sizes and player redshirt status.
  • Examine the relationship between

Research Ideas

• Analyzing the distribution of player heights across women's college basketball teams can provide insights into the physical characteristics of players in different positions and divisions. This analysis can help identify trends and patterns in player recruitment and development.
• Comparing the height distribution of teams from different conferences or divisions can reveal any disparities or advantages in terms of team composition and playing style. This information can be useful for coaches, recruiters, and fans to understand how different factors influence team performance.
• By examining the secondary positions played by playe...

List of NBA Draft picks from 1947 to present Thanks to https://www.sports-reference.com/ for the data.

Updated to include high school picks, and overseas players (listed under college)

Context

I felt that Israeli players scoring Free throws in poor percentage. with this thought, I wanted to check several Free throws analysis: Israeli players vs. other nationalities, Israeli Leagues vs. other Leagues etc.

I didn't found any free datasets that could help me to do the analyze, so I decided to create one. as always happens to me, I created a dataset that stores a lot more than the first intention, that could support many analysis about basketball players & Leagues. I created a dataset that stores a lot more than my first intention, this dataset could support many analysis about basketball players and Leagues.

Content

This Dataset Includes: 1. Seasons 1999-2020 2. 49 Leagues, ~11K players details & stats per Season 3. Player Details: Birth Date, Height, Weight, Nationality, High School 4. Stats per Season: Scoring Stats, Free Throws, Rebounds, Blocks, Assists, Minutes, Games etc. What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

you can find the available season per league data in the Seasons per League - Dataset Information kernel the scraping source code is available in my GitHub repository

Acknowledgements

Data scraped from the wonderful site: basketball real gm Photo by Pexels on pixabay.com

Introduction

I've been really interested in plotting and visualizing different NBA trends throughout this Thanksgiving break. Recently, I have been wanting to fact-check a common axiom I hear around the NBA during draft season: the notion that *older* draft prospects tend to have have *lower* upside. This is such a widespread belief that it can be heard on all levels, from NBA fan discussion on r/nba, to media draft analysis, to even GMs speaking about their draft choices.

For this visualization, I calculated the age of every lottery pick in the NBA draft from 1995 - 2015. I started at 1995 since this was the first modern "prep-to-pro" year with Kevin Garnett jumping from high school to the NBA. I ended at 2015 since I don't think we can develop an accurate read on the career trajectory of draft picks chosen after 2015 yet.

For each age range, I plotted a boxplot to visualize the distribution of the players' career PER, WS/48, BPM, and VORP. Let me know if you prefer to see another stat included here - I just went with the ones that Basketball Reference had publicly available.

Data

Here is the link to my plot

Key Results and Conclusions

Minimal differences among 18-21 year old prospects

It seems that differences in "upside" among 18-21 year old prospects are largely contrived by our brain's intuition, since there do not appear to be any significant difference in performance or success in the NBA for 18-19 year olds when compared to 19-20 and 20-21 year olds. Although VORP shows that the best of the best players since 1995 have been those drafted at age 18-19, the variation in distribution of BPM, WS/48, and career PER data is much lower.

Thus, we should be a lot more careful when assigning more favorable grades to extremely young prospects because they don't seem to have markedly better careers when compared to their slightly older counterparts. (Example: The data shows that 20.8 year old Donovan Mitchell would not have any different upside than 18.9 year old Kevin Knox)

Lower Extreme values for 22+ year old prospects

Interestingly, it looks like the median production is not really affected by the age of the prospect selected at all. However, there are some clear differences in the extremes.

The collective distribution of 22 and 23 year old lottery prospects shows that they tend to have much lower upper quartiles and extreme values, thus the best-case scenarios for these types of players is not as exciting. Although this difference is not as pronounced for 18-21 year olds, there is a huge drop off in the upper extreme values when moving from the 21-22 year old range to the 22-23 range.

Contrary to many other contexts, the NBA draft is a lot more about the outliers than it is about the median selection - each team is gambling on their pick becoming a future Tim Duncan or Dirk Nowitzki, and a successful draft would mean finding a franchise player-level talent. Therefore, our final conclusion is that although there are minimal differences in upside when comparing prospects in the 18-21 age range, 22+ year old prospects tend to have markedly lower ceilings than their younger peers.

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Acknowledgements/Notes

• Data was scraped from basketball reference (player pages, draft pages, advanced stats pages) as well as wikipedia (specific dates of each draft for age calculation). Scraping was done using beautiful soup.
• Figures were processed using numpy/pandas and visualized in matplotlib.
• Sample sizes for each age range: