Game of Thrones Season 6 Recap

Bullet vs Gauge

The gauge chart is an oft-maligned chart type. It has a poor data-to-ink ratio and it's difficult to interpret accurately. But I think it has some merit. Firstly, it is a very familiar chart type that most people can interpret it consistently. After all, it has been used on car dashboards since the Model-T.

Gauges also have value when on a mobile dashboard displaying a single metric. The gauge uses more vertical real estate and it still difficult to interpret accurately. But I have found it is less confusing than a bullet chart in this instance. Bullet charts are hard to read because the origin point is confusing without multiple reference bars to compare. Furthermore, bullet charts are generally more confusing than gauges because they are new to most people. But, most people have seen a gauge and can consistently interpret the bands and reference points. Also a bullet charts require more horizontal real estate which is more valuable on a mobile device than a desktop since the aspect ratios are flipped.

Here is a side-by-side comparison of both chart types in a mobile dashboard in Tableau. See my review of the strengths and weaknesses below. Please let me know your thoughts below.

Trump Lies

The New York Times recently cataloged all of President Trump's lies since he took office. Here is breakdown of the topics of those lies over time.

200 Songs of Springsteen

Bruce Springsteen is awesome. His music is personally very important to me. So I wanted to make a viz to celebrate his amazing career. This analysis looks at patterns in his music by song including song loudness, valence, lyric sentiment, energy, acoustic levels, and popularity. You can also find similar songs to the ones you like at the bottom. Hope you enjoy.

Predicting QB Success in the NFL

Last year I wrote and submitted a paper for the MIT Sloan Sports Analytics Conference. While my abstract was accepted my paper was not. The title of my paper was Reducing Risk in the NFL Draft: Using Machine Learning Algorithms to Predict Success in the NFL. You can read the full paper here

In it I describe a decision tree model that predicts a college QBs success in the NFL. To train the model I used over 40 variables including college stats, school competitiveness, combine performance, and text mining of pro scouting reports. Ultimately, the final model used 4 variables: college win %, body mass index (BMI), college games started per season, and age. The final model was 88% accurate in predicting whether a college player would be a success or a bust in the NFL. This model can be used to predict whether the top prospects in this year's draft will be successful in the NFL.

Below is an interactive version of that final QB model.

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