New Year, New Data: Additions to the Passes and Patterns Knowledge Base

1. Abstract

An update on the developments affecting the various articles previously published in this space, covering both the analyses of other work and original research. Datasets for original research have been updated with 2018 data for both the CFL and U Sports. Previously written state of the art pieces that have seen new work in the field include a postscript to the original work to fold in the new research. Finally, original research previously done has been updated with the new data brought in.

The critical developments covered here include an American P(1D) (Pelechrinis and Papalexakis 2016b) model that shows non-linearity for 3rd down in a manner similar to that previously found in Canadian football (Clement 2018e, [g] 2018), and the development of a WP model for Canadian football (Thiel 2019).

You Play to Win the Game: Win Probability in American Football

1-Abstract

The conclusion of a three-part series discussing the three major fields of research in American football analytics; First Down Probability (P(1D)) (Clement 2018a), Expected Points (EP) (Clement 2018b), and Win Probability (WP). This chapter discusses the existing body of work regarding WP and various models derived to estimate it over the last 30 years. Development in the sophistication  of model-building techniques is an ongoing theme, and better understanding of measures of uncertainty are an emergent topic as competing models enter public view and analytic notions infiltrate the sporting lexicon.

Score, Score, Score Some More: Expected Points in American Football

1 – Abstract

A continuation of prior efforts to consolidate the body of knowledge in American football analytics (Clement 2018), this work discusses the development of Expected Points as an analytical model over the past decades, with a focus on results, statistical methods, data management techniques, and historiographical change.

The development of EP over the past forty years has shown development in statistical techniques employed, growing from linear approximations based on two data points to advanced smoothing techniques and non-linear fits. This growth is aided by the exponential growth in dataset sizes that has paralleled the rise in cheaply available computing.

Keep the Drive Alive: First Down Probability in American Football

1 – Abstract

An examination of the existing scholarship of First Down Probability (P(1D)) in American football. A fairly unambiguous question, this work reviews ten studies over the past 45 years, albeit mostly in the last decade. Results are generally consistent across sources that P(1D) decreases in linear proportion to distance-to-gain for 1st and 2nd downs, while different sources model 3rd down as being either a weakly fit linear relationship or a slight exponential fit.

4th down was not examined fully by any source because of insufficient data. What data points can be confidently placed seem very close to 3rd down, leading to discussion over whether 3rd down data can serve as a proxy for 4th down in decision-making models.