Fresh Start: 2019 Play-by-Play Data in the Passes & Patterns Database

1. Abstract

New play-by-play data from the 2019 U Sports and CFL seasons was added to the Passes & Patterns Database. 125 games and 21,549 plays were added to the U Sports database, while 95 games and 14,025 plays were added to the CFL database. All data was cleaned and error-checked to fit with existing data format standards. A new scraper was developed in Python to streamline the CFL data collection process.

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.

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).

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).

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.

Appendix to It's the Data, Stupid: Developing a U Sports Football 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.

It's the Data, Stupid: Development of a U Sports Football Database

1-Abstract

A discussion of the development of a fully parsed database of U Sports football play-by-play data, including the sources and structures of available play-by-play data, examples of common errors in the data and methods to rectify them, and complete discussion of the code, structured as individual functions, used to parse the data into discrete columns. The code used is included within the discussion of each function.  The parsed data forms a relational database of plays that allow further research to be conducted at the individual play level. This database is believed to be the first of its kind for both U Sports and Canadian football in general.