This information visualization was created by Igal Klebanov during
Dr. Joel Lanir's course in Information Visualization @ University of Haifa.
Taking preprocessed data (*) of the 2016/17 NBA regular season,
adding player transactions reported by the media throughout the regular season,
and turning it into a rich yet simple Force Graph + Bullet Graph combo using d3.js, jQuery & bootstrap.
(*) Preprocessing included calculations of positional and league wide averages & maxes.
Graph depicts the transactions that took place during the season. Nodes
represent NBA teams active in the player market, while links represent a
single player's move between 2 teams. Moves are divided into trades
or contract moves (waived, signed, 10day, expired 10day).
Moves are divided into clusters by Month of move, and then by week of the month.
While interacting with the graph, one can view relations between moves that are
pretty hard to understand otherwise.
Below a single player is showcased with his seasonal stats divided into
3 categories: offense, defense/rebounding & playing time.
Each row shows the player's seasonal stat (dark orange), and potential
(if played 36 mins per game) in bright orange. The backgrounds shows
positional average stat, positional average per 36, and positional max.
Markers show the player's stat in teams he played for during the season.
Using this tool in combination with the graph, one can understand if
a player's moves affected his performance or playing time.
- Use mousewheel up/down to zoom in/out inside the graph's canvas.
- Click on links or nodes to filter data based on element clicked.
- Hover over links or nodes to highlight links based on hovered element.
- Click & Drag nodes to sticky them on the canvas.
- Hover over Colors in the legend to highlight links based on cluster.
- Click on Colors in the legend to filter out/in data based on cluster.
- After filtering, the rules will be appended to the bottom side of the canvas:
+ Click on filter rules to disable them.
- Use players search & list to easily highlight players and filter data.