To navigate complex data effectively it helps to see not only where you are, but where are the promising places to go next. When the data has dimensional structure, it may be possible to provide "sight lines" showing what lies in each direction of possible movement.
For example, if you ever choose colors using hue, saturation and brightness sliders, you may have noticed a color continuum displayed along each slider. As you move any slider, the other two sliders' colors update in real time, to show the colors that are now directly accessible via those sliders. Thinking dimensionally, these displays are showing you what lies in each orthogonal direction from your current position inside the color cube. Perhaps you've had occasion to use older-style sliders that lack this feature, and noticed how much harder it is to get to the right color. Navigation feels blind by comparison.
Dimensional sight-lines are one of the techniques Tertl is bringing to its NBA Matchup Meter.
This in-house demo project tackles a subject of great interest to basketball fans: comparing how a team's players have performed in different combinations, depending on the opposition they face. Existing ways of comparing lineup performance stats (e.g. here and here) get overwhelmed by detail, because each five-player combination is counted independently. The NBA's tremendously rich play-by-play data has much more to tell, if we can navigate it better.
Some players on a lineup are more replaceable than others. Rather than looking at complete lineups, it's more useful to move fluidly among combinations of any number of players. Also, coaching strategy is all about matching up against the opposition. So combinations of interest should include players on both sides of a contest. Finally, to navigate these combinations, we display anticipatory information about the effect of adding or removing any player to or from the lineup of interest. This information can be thought of as sight-lines along up to 30 dimensions (one for each player on each 15-man roster). This approach looks promising, but we'll know more when we can try it out..
If you like basketball and software design, please check back to try out prototypes of Matchup Meter as they become available. We'll be collecting feedback to help evolve a design that is as intuitive and revealing as possible.