Kick it Away, Kick it Away, Kick it Away Now: The Analytics of Punting

1. Abstract

A study of the development of research on punting in football. Punting is shown to have improved steadily over time, by both measures of yardage and more sophisticated measures such as Expected Points (EP). The existing official statistics are criticized and being result-oriented without regard to the process or factors beyond the punter’s control. Better measures are suggested and a method of assessing punts relative to their expectation based on a large number of environmental and circumstantial factors to rate punters independently of their opportunities.

The Roman Numerals of Computing: An Object-Oriented Database of U Sports Football

1. Abstract

A redevelopment of the U Sports parser and calculator previously described (Clement 2018b) that was built using VBA, this time working in Python. Data is imported through Python’s csv package, and parsed using an object-oriented approach, creating games and plays as classes with attributes as appropriate to each. Further objects exist to support analysis on the parsed data, and the numpy package with its arrays allows for far faster calculation of results. Discussion of future work built on this restructured database includes examination of special teams, expected points (EP) and Win Probability (WP).

The Whole Ten Yards: P(1D) in the CFL

1-Abstract

P(1D) values across down and distance were calculated for CFL data across down & distance. & Goal situations were separated and viewed independently. Results were compared to results obtained using the same methods on U Sports data. CFL offenses generally follow the same trends as seen in U Sports in terms of P(1D), though CFL P(1D) is consistently ~5 percentage points higher than U Sports under the same conditions. 1st down follows the same linear trend, while 2nd down shows exponential decay, with the previously discussed “Stupidity Asymptote” at 10%. However, CFL teams are markedly less willing to attempt 3rd down conversions, causing a lack of usable data points for 3rd down. For & goal situations the disparity between CFL and U Sports P(1D) does not seem to be as prevalent.

Appendix to Going Pro: Developing a CFL Play-by-Play Database

6-Appendix 1: List of Games

The table below provides a complete list of all the games for which play-by-play data has been found and included in the database as of the date of publication. The games are listed chronologically by date played, oldest game first.

Going Pro: Developing a CFL Play-by-Play Database

1-Abstract

CFL play-by-play data was collected from the official website into a single database. Idiosyncrasies in the structure of the data required the development of a VBA HTML scraper into a database. The data was parsed with a method that parallels the U Sports database, making it available for future investigation.

Three Downs Away: P(1D) In U Sports Football

1-Abstract

A data set of U Sports football play-by-play data was analyzed to determine the First Down Probability (P(1D)) of down & distance states. P(1D) was treated as a binomial variable, and confidence intervals were determined iteratively until convergence at the 10-10 level. For each down, fitted regression lines were added to enable discussion of overall trends with respect to distance.

Only points with a minimum N of 100 instances were considered. 1st down trended linearly, bearing only points at 5-yard intervals. 2nd and 3rd downs followed an exponential decay fit. Special attention was given to the non-zero asymptotes of these functions, and their implications towards the nature of the game. A review of & Goal data failed to provide any deeper insight.

Due North: Analytics Research in Canadian Football

1-Abstract

A broad survey of the history of scholarship in Canadian football analytics as relates to in-game decision-making. Works discussed span from 1982 to 2016, and while most focus on CFL data there does exist some mention of Canadian university football research (then CIS, now known as U Sports). Eleven different works covering a variety of topics create the basis of modern analytics in Canadian football.