Gist 6583ab0c6c2fb592d0c980d0fa48bbbd

100%

Gist 6583ab0c6c2fb592d0c980d0fa48bbbd

✓ Published0🌍 Public

Last edited Aug 21, 2021

Created on Aug 21, 2021

This visualization uses a force-directed simulation to lay out a network of fantasy football players, with each node’s size mapped to a player’s weight attribute from the starters.csv file. The chart reveals the relative heft of players like Wan-Bissaka and Son, contrasting them with lighter picks such as Maupay. Built with D3’s `d3.forceSimulation`, it applies collision and charge forces to space nodes, while `d3.drag` enables interactive repositioning. Node size is encoded via `d3.scaleSqrt`, and the canvas is redrawn each tick, making the weight distribution immediately apparent.

AI-generated description

Similar vizzes

Loading thumbnail…

Gist 10ad2f6c126f2280c720ff15922f5a5a

This visualization is a force-directed network diagram of a fantasy football team, mapping player connections through shared club or selection data. It shows each player as a node with a label, while weighted links between them are computed and rendered in real time. The code uses `d3.forceSimulation` with `forceLink`, `forceManyBody`, and `forceCenter` to arrange nodes, and `d3.geoPath` is not used—instead, links are drawn as SVG lines and nodes as circles. Player weights from the CSV file (e.g., 14.6 for Lacazette) are encoded via node radius, and the visualization updates smoothly with `transition.attrTween` on position changes. Data comes directly from the embedded `player_weights.csv` array, parsed into JavaScript objects for the simulation.

CConorAspell

87% match

Loading thumbnail…

Gist 8eb7f34550b636830b5384c026b0e102

This visualization uses a force-directed network to show fantasy football player transfers, mapping each player node by team and value while routing connections through a central hub. The simulation, built with `d3.forceSimulation`, applies custom `in_weight` and `out_weight` values to influence node positioning and link strength, with gameweek data drawn from a CSV. Player nodes are colored by `element_type` (position) and sized by `now_cost`, while the layout uses collision and charge forces to separate overlapping elements. The current view highlights a few high-cost midfielders and forwards, such as Foden and Haaland, with edge weights reflecting transfer momentum.

CConorAspell

87% match

Loading thumbnail…

Gist 2186477931c9166d8a5f26d5ebcd0e55

This visualization shows a simulated ranking of soccer players based on their fantasy-league value, where each player’s node weight reflects a blend of difficulty, form, and chance of playing. A force-directed layout using d3.forceSimulation positions the labeled nodes, while their size and color encode the computed weight. The chart draws directly from the CSV data in out_weights.csv, with player names as text labels. The code relies on D3’s selection and data-binding methods to bind the tabular rows to circle and text elements, and it uses the simulation’s tick event to update positions dynamically, though no explicit interaction or animation beyond the layout is implemented.

CConorAspell

86% match

Loading thumbnail…

Gist 08396e82bf7fe86a88d9a131f1a2654d

This visualization shows a force-directed network of football (soccer) players, with each node representing a player and edges likely indicating team or shared-match connections. The code uses `d3.forceSimulation` to calculate node positions and applies a custom weighting scheme to influence node size or opacity, though the rendering itself is not shown in the snippet. The weighting logic, defined in `out_weights.py`, modifies each player’s outgoing influence by subtracting fixture difficulty and form (scaled by 10), adding a small penalty for low play chance, and reducing weight for goalkeepers (element type 1). The data source is a pandas DataFrame of player statistics, and the visualization is part of an interactive dashboard built with JavaScript.

CConorAspell

85% match

Loading thumbnail…

Gist b541552eb18726aea9a989003304b013

This visualization shows a network of Premier League players as connected nodes, with each player’s node size and position determined by a custom weight calculation. The code, written in Python, uses a pandas DataFrame to compute an `in_weight` for each player by combining fixture difficulty, current form, and the chance of playing this round, with negative weights clamped to zero. The rendering approach is not shown in the code, but the visible structure suggests a force-directed or scatter-plot layout, likely built with a library like D3.js or Plotly. The data source is a player statistics table, with the weight influencing node prominence.

CConorAspell

82% match

Loading thumbnail…

Gist 39c7e6c0a5bdba5e4a29bcfc73c16c30

This visualization displays the current lineup of a Fantasy Premier League team, encoding each player’s form, cost, and position through a custom force-directed layout. It shows how player attributes relate to team selection and playing chances, with node size or color mapping to form and cost. The code uses d3.forceSimulation to position nodes, d3.forceLink for connections, and SVG paths with d3.arc for circular representations. Data comes from the current_team.csv file, which includes player names, now_cost, form, and chance_of_playing_this_round values. The rendering relies on D3’s scale functions and axis components to encode quantitative variables, while missing chance values are left blank.

CConorAspell

81% match

Loading thumbnail…

Gist 0ff68c82fce6408905fe7964eaf25be3

The visualization ranks ten Premier League midfielders by their current fantasy football weight, mapping each player’s name to a horizontal bar chart. It shows relative player value through bar length, with heavier weights (e.g., Harrison at 27.5) extending farther than lighter ones (e.g., Walcott at 21.5). The code uses D3.js’s `scaleBand` and `scaleLinear` to position and size the bars, while `d3.select` and `attr` methods bind the CSV data from `player_in_weights.csv` to SVG rectangles. Player names and weights are appended as text labels using `text` and `attr` calls, with the chart’s axes and gridlines drawn via `d3.axisBottom` and `d3.axisLeft`.

CConorAspell

81% match

Loading thumbnail…

Gist 57ca507364e5d0807c4fcbd14f2a6afd

This visualization presents a recommended fantasy football starting lineup and substitution strategy as a network diagram, using data from a JSON file. It shows the eleven starting players and four substitutes, with the captain highlighted, and a single recommended change from "Botman" to "Jonny" that reshapes the team. The code employs D3.js’s `d3.forceSimulation` to position player nodes and their connections, while player names are rendered as text labels, likely using SVG elements. The layout visually encodes the starting team’s structure and the tactical adjustment, though it does not animate the change; it merely states the recommendation.

CConorAspell

80% match