The 2014 World Cup has started and a flurry of neat data visualizations using World Cup data have been posted online. These include both static visualizations (images) and interactive data visualizations (self-service tools you can apply filters, hovers or sliders to interact with the data). Below are some data visualizations, applying the best practices of data visualizations and providing the user with the ability to find insight from the data rapidly and easily.

Difficulty of Schedule

Difficulty of Schedule

About: This static data visualization uses the 2014 FIFA rankings and the World Cup group matchups to convey the difficulty of schedule for each team.

What makes this a great visualization:

  • Uses a stacked bar chart to visualize the aggregation of the three games. Each component of the bar varies in length, which is based on the rank of each time (longer length denotes a better team. Notice there is no quantitative scale provided, but a consumer of this visualization can easily differntitate the difference.
  • Uses heatmap-like hue coloring with bar length to convey higher & lower ranked teams. The individual hue of each bar stack allows one to rapidly see the makeup of the strength of schedule. For example, Chile has the 6th most difficult schedule; however only two of the three games would be considered difficult.
  • Pre-sorted based on aggregate of strength of schedule, which is important in a static data visualization that cannot be interacted with. Therefore, one can easily identify the teams with the hardest and easiest schedules.

Distance Traveled by Teams

Distance Traveled by Teams during Group Stage

About: This static data visualization provides insight on how much the national teams will have to travel to between matches in the group stage at the 2014 World Cup in Brazil. Click for the source of the data visualization

What makes this a great visualization:

  • Uses a stacked bar chart to visualize the aggregation of the three games. Each component of the bar varies in length, which is based the killometers required to travel to the stadium for each match.
  • Nice annotation/definition of what "distance travelled means". It clarifies that the distance is from the basecamp and it includes the trip back.
  • Uses solid colors to denote each match, which helps to differentiate between the matches.
  • Pre-sorted based on aggregate of distance needed to travel, which is important in a static data visualization that cannot be interacted with. Therefore, one can easily identify the teams with the longest and shortest aggregate travel distance.
  • The quantitative scale is a nice addition, because it amplifies thousands of miles required travel by some teams. For example, the USA has to travel over 14,000 km!

World Cup Penalty Kick Success Location

Penalty Kick Success Location

About: This static data visualization uses historical World Cup data to provide the location of scored and saved penalty kicks. This data visualization amplifies the Freakonomics reasoning "that center of the goal is the most effective location" for a penalty kick.

What makes this a great visualization:

  • This visualization makes great use of using the rectangular goal background over a coordinate system showing points where the penalty kicks were aimed.
  • Green dots indentify scored (successful) penalty kicks. Conversely, hollow dots identify saved penalty kicks; while dark green dots symbolize penalty kicks that were off target. It is simple and clean to the data visualization consumer.
  • Immediate insight is gained by seeing the clusters in the far left, upper left, high center and far right of the data visualization. If one was tasked with figuring out the best place to locate a penalty kick, you can deduce that easily from this data visualization.

FiveThirtyEight’s World Cup Predictions

FiveThirtyEight’s World Cup Predictions

About: This interactive data visualization surfaces a model called SPI (Soccer Power Index) that is used to estimate the probability of success at the World Cup.
Click here for the details of the model.

Click here to interact with the data visualization.

What makes this a great visualization:

  • The data visualization is delivered in a self-service format to the user. There are no instructions provided and the insight is self-explanatory.
  • The delivery of this interactive data visualization amplifies how data visualizations can be used as effective tools in surfacing the results of complex models (such as SPI, where the number crunching is done somewhere else).
  • Animations, responsive insight delivery and color annotations using the nation flags highlight the professionalism on this data visualization.
  • The layout is very clean and printable.

Every goal scored in the World Cup, by minute

Every goal scored in the football World Cup, by minute

About: This interactive data visualization shows all the World Cup goals scored by minute. The visualization provides filters and hovers for additional insight.

Click here to interact with the data visualization.

What makes this a great visualization:

  • This interactive data visualization highlights many best practices on how to surface a large amount of data points effectively. Clusters can easily be seen of goals scored before each half. Surfacing these many data points is not easily done.
  • There are two quantitative scales provided with three qualitative regions (first half, second half, extra time) to split the data points cleanly for the visualization consumer.
  • Each data point uses different colors to highlight the different type of goal (normal, own goal, penalty), providing additional insight.
  • The interactivity is superb on this data visualization. Not only can the user hover over the data point and get a pop-up of the year/game/team scoring, but it also links to other goals during the same match. One can clearly see a quick "chronological score summary" of the match.