It’s Up and It’s Good: Field Goals in U Sports

1. Abstract

A continuation of a series of works developing the individual parts of a future third-down decision-making model using discrete individual models. Raw data for P(FG) shows that P(FG)GOOD declines linearly with increasing kick distance, as does EP(FG). Five different classification models were used to assess P(FG) based on various features relevant to field goal attempts (distance, elevation, temperature, wind, weather). The random forest model proved most effective, with the best correlation measures both by RMSE and R2. Distance remains the strongest and best predictor of P(FG), dwarfing all other factors.

It's a Game of Field Position: Punting in U Sports Football

1. Abstract

An analysis of the objective measures of punting in U Sports football,. This work looks at gross, net, and EP values of punts by yardline and compares them to one another. All measures are shown to have cubic fits. Punt spread is also examined as the difference between gross and net punting. Punts are heavily compressed with decreasing yardline below 50 yards, and conversely increase significantly in value with increasing yardline above 90 yards. While the first of these is expected, the second is unexpected. Further research is necessary, with the preliminary hypothesis that it stems from a selection bias of only teams that punt well choosing to punt from those situations.

Rain or Shine: Incorporating Weather Data into the U Sports Database

1. Abstract

METAR weather data was compiled for the seasons and locations of games within the existing U Sports database by scraping three different sources of archived data. Within the parser, METARs covering the dates and times of games were attached in a list to the game objects. Plays within each game were given an estimated real-world time, and each play was then assigned the METAR nearest to it. Future applications of weather data include its use in development of field goal models, examining the impact of weather on various existing analyses, and contextualizing future research.

Home Sweet Home: Football Stadia in Canada

1. Abstract

Data for all 43 stadia in the existing U Sports and CFL databases (Clement 2018a, [b] 2018) was accumulated, with various contextual features for each stadium, and incorporated into a Python dict to attach the appropriate stadium object to each game object. Stadium data includes geographic data, architectural data, and references to allow further development of weather data. The structure of the dict allows the addition of future attributes and stadia as required.

It’s Spelt “Rouge:” Expected Points in U Sports

1. Abstract

For an existing database of U Sports play-by-play data five different models were created to look at Expected Points in U Sports football. The results of these models are presented, in addition to the results of the raw data, allowing the models to be compared to the raw data and against one another. Furthermore, extensive correlation graphs are presented to allow models to be assessed based on their effectiveness in predicting EP. The multi-layer perceptron model proves itself to be the most effective across all scenarios, followed by the gradient boosting and k-nearest neighbours models. The random forest and logistic regression models proved poor estimators of EP and are contraindicated for future use.

Appendix 2 to New Year, New Data: Additions to the Passes and Patterns Database

9. Appendix 2: List of CFL Games

The table here gives a list of all the games added to the CFL database from the 2018 season.

Appendix 1 to New Year, New Data: Additions to the Passes and Patterns Database

8. Appendix 1: List of U Sports Games

The table here gives a list of all the games added to the U Sports database from the 2018 season.

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).

Given the Boot: The Analytics of Kickoffs

1. Abstract

The third piece in a series on the current state of affairs regarding analytical research into football’s kicking plays, this piece looks at kickoffs. The broad conclusions to draw from the corpus are that the rule changes regarding touchbacks have had a major impact in reducing the number of kickoff returns and negatively impacting the starting field position after a kickoff; and that onside kicks are underutilized. Their success rate is almost entirely dependent on whether or not the kicking team is obliged by game situation to attempt an onside kick, and non-necessary onside kicks have a far higher success rate, high enough that their use is currently an under-exploited area of opportunity.