Fourth Down - what play do you call?

My viz has been selected as a finalist in Tableau's Elite 8 Iron Viz competition! Please vote for me via hashtag:    Tweet #Elite8John

The fourth down is the most critical possession in football - it is the team’s final play and they are left with three basic options: Go for a first down, kick a field goal, or punt the ball away. The most important variables when making this decision are how many yards are needed to get the first down and where they are positioned on the field. Of course, this all goes out the window when it comes down to the wire in the fourth quarter.

This first half of my Iron Viz submission illustrates what NFL coaches typically do when faced with a fourth down decision. Users can see clear trends in how the most common play changes based on these two variables. Note how certain scenarios are impossible (e.g. fourth and seven on the five yard line). Increase the pressure and switch to view only fourth quarter stats and see how much riskier the play calling becomes when the entire game is on the line.

The second half of my viz provides additional detail around expected outcomes of each of the three options to help guide the decision for your team. Maybe your field goal kicker has some extra distance in their leg compared to the NFL average or perhaps your punter has control issues resulting in many short punts. These factors all influence the calculus of which play is the right one to call.

Blue 42, Blue 42, Hut Hut Hike!

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Divvy Bikes - Data Challenge

Divvy, a bike sharing service in Chicago, recently announced a data challenge where they published approximately 750k rows worth of bike trip data in Chicago. They challenged people to build data visualizations that would reveal and showcase patterns in usage of the bike sharing service.

My submission is what I have titled the ‘Divvy Station Cockpit’. It is designed to provide an in-depth analysis of a selected station and can be used to help Divvy evaluate operations at existing stations and better plan for new ones. 

The dashboard features the following capabilities:

Major KPIs:
Average Daily Trips – How many trips depart from this station?
Relative Station Popularity – How does this station rank compared to others based on average daily departures?
Duration – When people take trips from this station, how long does it take them to reach their destination?
Duration Histogram – How are these trips distributed? Is there a cluster around a specific time in case of a common destination (e.g. Wrigleyville to the loop)
Day of First Trip – How long has this station been operating?
Weekly Trips Trend – Is traffic increasing or decreasing? Keep in mind, the data set starts in the summer and runs through December – brrrr!

Aggregated Daily Supply/Demand Curves
Throughout the day, bikes are coming and going from each Divvy station. In order to plan capacity and estimate traffic, this tool looks at the aggregated demand over the course each day of the week and can quickly identify how this station is commonly used. Is this a station where many commuters depart in the morning (increasing demand for bikes) but then return to in the evening (increasing supply)? Or is it a reverse commute when supply builds up in the morning and then is depleted in the evening? Alternatively, is the station more tourist-focused where demand is more consistent over the course day, but much higher on the weekends than weekdays? This tool can help Divvy operations plan the optimal allocation of bikes.

Frequent Destination Heat Map
Although nearby stations are unsurprisingly common destinations, occasionally there are clear trends where a riders flow to a common location further away. This heat map indicates which destinations are the most common for riders departing the selected station.

Ridership by Day and Member
For a quick look at the type of riders that are frequenting the station, we look at average daily ridership by member type and day of the week to get a better sense if it is used by subscription members or guests and when. This view can be used in conjunction with the Daily Demand Curve views to find stations that have a commuter usage pattern, but a larger share of Guests users, to target for marketing resources suggesting riders purchase a subscription membership.

I had a lot of fun with this viz and thanks to Divvy for making this data set available!

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