The concept of time is one example. We think and talk of time, a concept virtually without its own terminology, in terms of space and motion: Time goes by...Our best days are ahead of us...I'm looking forward to next season. The blathering talking heads on CNBC can't go 20 seconds without convulsively saying the phrase going forward whenever referring to the future.
Abstract sports concepts like win probability are no exception. We would all call 2-yard run on 4th and 1 a big play, even though it was anything but literally big. How would we characterize a 38-35 game? As a high-scoring game, of course. We are universally comfortable speaking about abstract concepts in terms of the metaphors of physical position, size and motion, and it's a window into how we think. That's why I'll take a graph over a table of numbers any day.
Tables have their advantages, but graphs are perfectly suited for comparing quantities and for detecting relationships between variables, two of the most common things we do around here. Here's a quick example. Consider team offensive Expected Points Added (EPA) for passing and rushing in 2010. I could present the information either as a table of numbers, like this:
Team Offense EPA
|Team||Pass EPA||Run EPA|
Or I could present the same information in a scatter plot, like this:
Right away, you can see where each team stands with relation to the others. You can see the averages, illustrated with the green lines. For example, NE, NO, IND, and GB all had very successful offenses this past season, but what set NE apart was their success in the running game. This is far more intuitively apparent in the graph than in the table.
Further, you can see relationships within the data that you might ever detect just by reading a table. You can immediately see that pass EPA is distributed much wider than run EPA. You might also notice the correlation between rushing and passing success.
The interactive features make the graph even more compelling. To see the exact numbers or to reveal obscured team abbreviations, hover your cursor over any team's data point. To filter any number of divisions, click on the legend below the main plot.
This is no different than the ones I posted in these two posts, highlighting team balance and correlations between a team's offensive performance and defensive performance. But the plan is to have graphs like this, updated weekly, available throughout next season. The hard work is done, so now I can feed any set of statistics into the meat-grinder and instantly produce a similar plot, whether it's WPA, or EPA, or SR, YPA, or simple yards. It could be team running/passing or team offense/defense. No longer would I have to paste the data into Excel, create the graph, save it, upload it, etc...
Here are some other examples that illustrate how a simple graph can sometimes convey information a much clearer way than a table of jumbled numbers and decimals. This next graph is team opponent-adjusted Success Rate from the 2002 season. Look at how the Buccaneers' defense stands out above the pack. Their Super Bowl opponent, the Raiders, had a scorching offense, but it was the Bucs' year.
The next graph is an example of a more conventional statistic, net passing yards per attempt (YPA) for the 2010 season. I've made defensive YPA appear negative to be consistent--better defenses are higher on the vertical axis. Aside from the snake-bitten (or Norv-bitten if you prefer) Chargers, you can see that the two Super Bowl contenders are the two furthest teams to the upper-right, signifying their strength in both offensive and defensive net pass efficiency.
There's no new analysis here, just a novel way of presenting the information. One of projects I'll be working on this off-season is creating handy visualizations like these and others.