Comfortably Numb(ers)

Let me preface this with NFL assets are ALWAYS risks. You need evaluate such in a probabilistic perspective, and that includes looking at every outcome to generate an expectation. The Love pick has a positive expected value (likely, without personally computing such), and teams should always pursue positive expected values for the QB position. Furthermore, this is the right strategy, even if it does not pay off for the Packers. The pick for Jordan love is very much the right type of strategy for a team to follow in finding a succession plan for a franchise QB. The worst position for a team to find a QB is stuck in the 15-30 range of draft position, never being able to grab a top 3 QB in a draft. Instead, the Packers chose to take a riskier prospect, give themselves time to develop and further project this player, and they have a chance to move on and consider an alternative route if he does not work out. The availability of an inconsistent prospect with elite highlights is akin to t

Project Overview: I used nflscrapR 2019 season data to organize teams' playcalling in normalized relative proportions, then used a clustering algorithm to categorize each team into one of five groups. Analysis and Report by Kevin Kraege found at @kevgk2 on Twitter Cluster numbers were generated arbitrarily, and not according to specific order of clustering. It may seem odd to refer to teams by their cities instead of team names, but I tried to remain consistent between my analysis and how the data is charted and graphed. For this project, I tend to gloss over the details of the mathematics and methods used, such as principal components, agglomerative clustering, silhouette method, or EPA. I advise you to research these yourself to find someone more qualified to explain them. Contact me on twitter if you wish for some help doing so. Data Specifications: Each play recorded fit into one of 12 groups. These groups are combinations of: three types of runs (end, guard, tackle),