Finding "Memphis" : a rating-based player comparison visualisation
We designed and implemented a prototype visualization tool to compare the performance of foot- ball players. We introduced the interactive weighted attribute tree to make abstraction levels attribute that can hide the detail of the rating system and give the user the flexibility to build or modify their own rating system at run-time. To find similar players, we implemented automatic weight generation for a certain player to build a specific rating system to find similar players. In addition, we designed a player similarity graph, which combines the advantage of scatter plots and stacked bar charts. The user can easily locate a group of similar players and make selections for further comparison. For player comparison, we designed a player performance treemap that can compare players with player block size, order or color. We also designed a player comparison view to help the user compare all the visualizations appeared in the tool to make the final conclusion on which players are the better ones. We made some sample analysis of questions like ”Who are the best players in the whole season”, ”Which players have similar playing style like a target player” and ”Who is the best replacement of a target player” to show our tool can solve all the problems mentioned in 1.2.2. We also showed that our tool can provide a complete work-flow from browsing players to getting the final conclusion.