An Investigation into League Positions, the Rise of Goals and The Lost Art of Defending They say the Premier League is a completely different beast today than it was in the 90s. We talk about the ‘high-press,’ the ‘tactical evolution,’ and the ‘death of the 4-4-2,’ but I wanted to see if the data actuallyContinue reading “Three Decades of the Premier League”
Tag Archives: data visualization
Beyond Dual Axis Part 2: On Your Radar – A Tableau Radar Template Library
After my last blog, creating Hexagon Radar Charts, the next logical question is how do I do this for other polygons? With that being said Ill go ahead and leave this blog pretty short with the story, but provide you with calculations needed to create radar charts for a variety of different shapes. In thisContinue reading “Beyond Dual Axis Part 2: On Your Radar – A Tableau Radar Template Library”
Beyond Dual Axis Part 1: Creating Hexagon Radar Charts in Tableau with Map Layers
With the release of Multiple marks layer support in Tableau 2020.4, the possibilities for creating rich, complex visualizations have exploded. No longer limited to dual-axis hacks, advanced developers can now design intricate charts within a single worksheet. Whether you’re crafting a detailed table or pushing the boundaries of a classic chart, map layers open upContinue reading “Beyond Dual Axis Part 1: Creating Hexagon Radar Charts in Tableau with Map Layers”
Creating Radial Bar Charts in Tableau
In a culture where it seems like all we do is work, I thought it would be interesting to take a deep dive into the annual leave data offered by country. I guess I am not the only person interested in this topic, as I was lucky enough to win the the Tableau Public VizContinue reading “Creating Radial Bar Charts in Tableau”
Defying The Odds: How likely are we to see another team pull a ‘Leicester’ and win the EPL?
The 2015-2016 English Premier League (EPL) season resulted in one of the unlikeliest of champions in professional sports history, Leicester City F.C. Starting the season as the 14th ranked club from the previous season, they emerged as champions in direct contradiction to a 5000 to 1 odds against estimate by bookmakers. We conducted a simulation study to evaluate whether 5000:1 odds made sense. This simulation used the 20 seasons of EPL play to empirical estimates of the points expected from a match when different preseason ranked teams played. This provided a basis for simulating a season of 380 EPL games. The results of this simulation suggest that the 5000:1 odds were reasonable. In addition, the finish of a preseason #1 team (Chelsea) as #8 at the end of the season was almost as unlikely as a preseason #14 rank team emerging as champion. In addition to this simulation, an extensive descriptive analysis and data visualizations study was produced.