UW iSchool Course INFO 474: focused on designing and building visualizations to better understand and communicate about pressing issues.
Published
Edited
Nov 15, 2020
Importers
md`# Project Proposal`
md `## Domain of Interest
We would like look into NBA dataset. We are curious to see that if NBA players free-throw shooting percentage are correlated with theirs height.
`
md ` ### Why are you interested in this field/domain?
* Our team find a huge passions in sports. We want to see if a person height can affect theirs free throw shooting percentage, this is because free throw shooting require good shooting skills, we are curious if a person height can contribute to this!
`
md
`
### What other examples of data driven project have you found related to this domain (share at least 3)?
* #### https://prezi.com/f-kf5vvidflz/free-throw-percentage-vs-basketball-player-height/
* #### https://towardsdatascience.com/10-unique-visualizations-of-the-nba-b981cfdb78bf
* #### https://buckets.peterbeshai.com/app/#/playerView/201935_2015
`
md
`
### What data-driven questions do you hope to answer about this domain (share at least 3)?
* What are the top 10 players with highest and lowest free throw shooting percentage?
* Based on the 10 players with highest free throw percentage, what is their average height compare to the average height of the 10 players with lowest free throw percentage?
* Data can be biased, we also want to look deep into how consistant is the players, when looking at the 10 players with highest free throw shooting percentage, we want to see their avarage points of that season to see if majority of theirs points is based on free throw shooting?
`
md
`
### What concepts in your domain would be important to visually explain (more than 1)?
The concepts in our domain that we would like to highlight is the difference in free throw shooting percentage between the top 10 the bottom 10 of the dataset we collected, we would like to highlighted this and compare it to the height.
`
md `
# Finding Data
`
md
`
### Where did you download the data (e.g., a web URL)?
* #### https://data.world/jgrosz99/nba-player-data-1978-2016
* #### https://www.basketball-reference.com/leagues/NBA_2019_adj_shooting.html
* #### https://www.basketball-reference.com/leagues/NBA_2019_per_poss.html
`
md
`
### How was the data collected or generated? Make sure to explain who collected the data (not necessarily the same people that host the data), and who or what the data is about?
* The data is collected by JUSTIN GROSZ and was published on dataworld.com. The dataset is about NBA Player Data Per Season From 1978-2016.
* The second and third dataset come from The Sports Reference sites Baseball-Reference.com. Their Primary Aim is to answer users' questions with the easiest-to-use, fastest, and most complete sports statistics anywhere.
`
md
`
### How many (rows) and (columns) are in your data?
* 17,729 rows, 109 columns.
* 778 rows, 28 columns.
* 708 rows, 32 columns.
`
md
`
### What questions (from above) can be answered using the data in this dataset?
We can clearly filter the dataset to get easy answer for the top and bottom 10 players in terms of shooting percentage. We can also do the same thing to calculate average height for those players. We can also dive deep into one serson to analyze and compare it to previous seasons from the big dataset. From the per 100 possesion we trying to see who are the most effective players in the NBA in 2018-2019 season and what are their respective height.
`
render_data_table(data.slice(0, 10))
render_data_table(data1.slice(0, 10))
render_data_table(data2.slice(0, 10))
md `
# Appendix
`
data = d3.csvParse(await FileAttachment("nba_data.csv").text())
filtered_data = data.filter(d => d.Year == "2016")
data1 = d3.csvParse(await FileAttachment("2016-2017.csv").text())
data2 = d3.csvParse(await FileAttachment("per100@1.csv").text())
import {
render_data_table,
table_styles,
code_styles,
displayImage,
displayCaution
} from "@uw-info474/utilities"
d3 = require("d3")
Purpose-built for displays of data
Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
