In a couple weeks, the NFL will hold its annual Rookie Draft and as you can imagine, the NFL pundits are up to their eyeballs in draft analysis:

  • Who will get picked No. 1 overall?
  • What player with [your team] take?
  • And many more questions

It seems like anyone these days can conduct a mock draft and call themselves an expert. Seriously, just google “mock NFL draft 2017” and see how many wormholes you can get yourself into. It’s staggering and can be hard to make sense of.

Credit Where It’s Due

My dataviz colleague and fellow KC sports fan tweeted this at me which spurred the idea for this makeover and blog post:

Well, you can imagine where it went from there. Thanks Kevin for surfacing this.

Luckily for us sports nuts, the well-meaning folks over at SBnation, took it upon themselves to aggregate and analyze the top 73 mock drafts out there to help answer that second question above. I admire & commend them for their effort. So they got the data, analyzed it, and wanted to visualize the results. AWESOME! It’s always great to see data visualization being used in various mediums.

But in that aspect…

They need some guidance. There are some very talented writers & editors that work under the umbrella of SBNation. Seriously, if you’re a baseball and numbers fan, Beyond the Box Score is one of my favorite sports blogs out there. But I digress.

The original

Yesterday, they posted an article sharing the results of the analysis of 73 mock drafts from all the big networks and well-respected sports writers. You can check it out here.

The article is well laid out & informative. They even present the definitions & caveats to the reader right up front. This is all really great and helps the reader understand what they are about to visualize.

So you read the introduction and the definitions, you are now ready to find out who your team is going to pick in the first round. Yes, here we go! I’m a Chiefs fan so I scroll to find the Chiefs and I find this…

Screen Shot 2017-04-19 at 8.25.53 AM

My initial reaction:

Oh, pie charts, how I loathe thee. I don’t know why so many people choose the pie chart with the intention of making comparisons. This is unfortunate. Your brain has to do so much unnecessary work that it can cause frustration and confusion. We can do better

In the true spirit of doing a dataviz makeover. I’m going to break the original down into “what worked well” & “what can be improved.” Finally, I’m going to show you my take on it.

What works well

  • Starting at 12 o’clock the colors of the segments correspond to the vertical color legend
  • The “Other” category is the last segment (best practice)
  • Those players with highest % are called out below the chart for quick comprehension

What can be improved

  • There is no order to the segments (ascending/descending/alphabetic)
  • The colors are:
    • pointless and don’t add anything to the analysis
    • hard to read the values – (ex. the light gold color with the white text value hurts the eye to read)
  • Position is ignored

My Take

As we all know, our brains have a tougher time recognizing differences in angle while length is a lot easierMy take

My improvements

  • As we all know, the brain has a difficult time comparing based on angle or area, while length is much easier. For this reason, I created a horizontal bar chart.
    • This chart type also allows me to add the full text of the players’ name all of which will take some workload off the reader.
  • I excluded “Others” and only focused on those players with a probability % of >5%.
    • Remember this caveat was mentioned earlier in the original article
  • I sorted the players descending based on %
  • For the colors, I know that the binary colors of Chiefs are Red & Gold. I categorized the players based on Offensive/Defensive position.
    • I used those colors again in the title
  • I separated the position from the player into a separate column
    • this quickly allows the user to see position breakdown of the players
      • For example, while it looks like the Chiefs are likely to draft a linebacker, it also looks an offensive player is likely as there are 5 offensive players compared to 4 defensive players. And 3 of those 5 are QBs. A very interesting insight that would have taken much more time to glean from the original pie chart.

Prior to writing this up, I reached out to the author of the original article outline several of the improvements listed here. As of this writing, I have not heard back but will update this post depending on what feedback I get.

Conclusion

We live in a big, wide world and there are several people out there just getting started in adding data visualization to their work. This is a good thing. However, it’s also important that we provide honest, positive critiques in an effort to increase data literacy everywhere.

Thanks for reading.

Until next time!

Advertisements

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s