Passes and Patterns: 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.

2. Introduction

With the U Sports and CFL play-by-play databases (Clement 2018a, [b] 2018) holding a great deal of data relating to on-field action, there is a desire to better contextualize this information. The most immediately available information is to now where the game is played. This knowledge brings with it a great deal of information, and allows a great deal more to be referenced. Football games in U Sports are typically played at the home field of the hosting team, with limited exception; the set of total venues is finite.

In all, 43 different stadia were found that have hosted a U Sports or CFL game during the timeframe of our twin databases (2002-2018). While some of these have hosted hundreds of games, others have hosted very few, but the list includes every stadium for every game in the current database.

3. Stadium Info

Data regarding stadia was compiled from a number of sources and compiled into Table 1. By organizing the information according to stadium, each game can simply reference the stadium in which it was played, and get consistent information

Stadium Name

City

Prov

Home

Surface

Cap

GPS

Orientation

Elev

Airport

Full EZ

TZ Code

isDome

Oland Stadium

Antigonish

SFX

FieldTurf

4000

45.6166

-61.9949

359

195

CYYG

FALSE

America/

Halifax

FALSE

Huskies Stadium

Halifax

SMU

FieldTurf

5000

44.6311

-63.5795

343

CYHZ

FALSE

America/

Halifax

FALSE

Raymond Field

Wolfville

ACA

FieldTurf

3000

45.0920

-64.3674

346

CYHZ

FALSE

America/

Halifax

FALSE

MacAulay Field

Sackville

MTA

Natural Grass

2500

45.8977

-64.3733

281

CYQM

FALSE

America/

Moncton

FALSE

Coulter Field

Lennoxville

BIS

FieldTurf

2200

45.3652

-71.8411

148

CYSC

TRUE

America/

Toronto

FALSE

Stade de l'Universite

Sherbrooke

SHE

Natural Grass

3359

45.3745

-71.9307

271

252

CYSC

FALSE

America/

Toronto

FALSE

CEPSUM

Montreal

MON

FieldTurf

5100

45.5090

-73.6114

133

CYUL

TRUE

America/

Toronto

FALSE

Concordia Stadium

Montreal

CON

FieldTurf

4000

45.4579

-73.637

CYUL

TRUE

America/

Toronto

FALSE

Percival Molson Stadium

Montreal

MCG

ALS

FieldTurf

23420

45.5101

-73.5808

CYUL

FALSE

America/

Toronto

FALSE

Stade TELUS

Quebec

LAV

FieldTurf

12817

46.7837

-71.2796

321

CYQB

FALSE

America/

Toronto

FALSE

MNP Park

Ottawa

CAR

FieldTurf

3500

45.3885

-75.6941

337

CYOW

TRUE

America/

Toronto

FALSE

Gee-Gees Field

Ottawa

OTT

FieldTurf

3000

45.4160

-75.6651

CYOW

FALSE

America/

Toronto

FALSE

Old Richardson Memorial Stadium

Kingston

QUE

Natural Grass

8000

44.2276

-76.5163

356

CYGK

FALSE

America/

Toronto

FALSE

New Richardson Memorial Stadium

Kingston

QUE

FieldTurf

8000

44.2276

-76.5163

356

CYGK

TRUE

America/

Toronto

FALSE

Varsity Stadium

Toronto

TOR

Polytan

Ligaturf

5000

43.6670

-79.3972

344

111

CYTZ

FALSE

America/

Toronto

FALSE

York Lions Stadium

Toronto

YRK

FieldTurf

3700

43.7764

-79.5119

345

199

CYYZ

TRUE

America/

Toronto

FALSE

Ron Joyce Stadium

Hamilton

MAC

FieldTurf

6000

43.2660

-79.9170

358

CYHM

TRUE

America/

Toronto

FALSE

Guelph Alumni Stadium

Guelph

GUE

FieldTurf

Revolution

8500

43.5350

-80.2265

317

336

CYYZ

FALSE

America/

Toronto

FALSE

Warrior Field

Waterloo

WAT

FieldTurf

Duraspin PRO

5400

43.4742

-80.5496

342

CYKF

TRUE

America/

Toronto

FALSE

University Stadium

Waterloo

WLU

FieldTurf

6000

43.4701

-80.5301

332

CYKF

FALSE

America/

Toronto

FALSE

TD Waterhouse Stadium

London

WES

FieldTurf

8000

42.9998

-81.2738

249

CYXU

FALSE

America/

Toronto

FALSE

University of Windsor Stadium

Windsor

WIN

FieldTurf

2000

42.2982

-83.0630

180

CYQG

FALSE

America/

Toronto

FALSE

Investors Group Field

Winnipeg

MAN

BBO

FieldTurf

33500

49.8078

-97.1430

227

CYWG

TRUE

America/

Winnipeg

FALSE

University of Manitoba Stadium

Winnipeg

MAN

Natural Grass

5000

49.8067

-97.1462

332

227

CYWG

FALSE

America/

Winnipeg

FALSE

Griffiths Stadium

Saskatoon

SKH

FieldTurf

6171

52.1270

-106.6298

355

502

CYXE

FALSE

America/

Regina

FALSE

Mosaic Stadium

Regina

REG

RRI

FieldTurf

33427

50.4505

-104.6330

576

CYQR

TRUE

America/

Regina

FALSE

Mosaic Stadium at Taylor Field

Regina

REG

RRI

FieldTurf

33350

50.4526

-104.6242

315

576

CYQR

TRUE

America/

Regina

FALSE

McMahon Stadium

Calgary

CGY

STA

FieldTurf

33650

51.0703

-114.1214

335

1099

CYYC

TRUE

America/

Edmonton

FALSE

Foote Field

Edmonton

ALB

PureGrass

3500

53.5035

-113.5304

669

CYEG

TRUE

America/

Edmonton

FALSE

Thunderbird Stadium

Vancouver

UBC

PolyTan Turf

3411

49.2544

-123.2455

331

CYVR

TRUE

America/

Vancouver

FALSE

Swangard Stadium

Burnaby

SFU

Natural Grass

5288

49.2786

-122.9222

103

323

CYVR

TRUE

America/

Vancouver

FALSE

TD Place

Ottawa

RED

FieldTurf

24000

45.3981

-75.6834

CYOW

TRUE

America/

Toronto

FALSE

Setters Place

Red Deer

Natural Grass

52.2680

-113.8334

855

CYQF

FALSE

America/

Edmonton

FALSE

Westhills Stadium

Victoria

VIR

FieldTurf

1718

48.4430

-123.5236

274

CYYJ

TRUE

America/

Vancouver

FALSE

Rogers Centre

Toronto

ARG

AstroTurf

53506

43.6415

-79.3891

345

CYYZ

TRUE

America/

Toronto

TRUE

Commonwealth Stadium

Edmonton

ESK

Shaw

Sports Turf

56302

53.5596

-113.4761

657

CYEG

FALSE

America/

Edmonton

FALSE

BMO Stadium

Toronto

ARG

Natural Grass

30991

43.6332

-79.4185

344

CYTZ

TRUE

America/

Toronto

FALSE

Olympic Stadium

Montreal

ALS

FieldTurf

56040

45.5579

-73.5515

350

CYUL

TRUE

America/

Toronto

TRUE

BC Place

Vancouver

LNS

FieldTurf

54320

49.2766

-123.1118

CYVR

TRUE

America/

Vancouver

TRUE

Tim Hortons Field

Hamiton

TIC

FieldTurf

24000

43.2521

-79.8300

305

CYHM

TRUE

America/

Toronto

FALSE

Moncton Stadium

Moncton

FieldTurf

10000

46.1085

-64.783

CYQM

FALSE

America/

Moncton

FALSE

Ivor Wynne Stadium

Hamilton

TIC

AstroPlay

29600

43.2521

-79.8300

107

CYHM

TRUE

America/

Toronto

FALSE

CanadInns Stadium

Winnipeg

BBO

AstroPlay

29533

49.8901

-97.1972

231

CYWG

TRUE

America/

Winnipeg

FALSE

Table 1 Summary of Canadian Football Stadia

Each stadium has the following attributes:

Name: The name of the stadium. For certain stadia a decision has been made to standardize the name, as the stadium may have had many names. Here the choice has been to favour the name best associated with the stadium. In some cases, such as Kingston’s Richardson Memorial Stadium, a stadium was demolished and rebuilt with the same name. Here “new” and “old” have been attached to the stadium name to distinguish between the two.
City: The city in which the stadium is located. In cases where the stadium may technically reside in a suburb the name of the greater metropolitan area has been use.
Province: The province in which the stadium is located, with the two-letter mailing code.
Home Teams: The U Sports and CFL teams which consider the stadium as their home stadium. This includes former stadia from which teams have moved on.
Surface: The playing surface. This is the most recent surface, as many stadia have been resurfaced over the years.
Capacity: The seating capacity of the stadium when it was most recently in use as a football stadium.
GPS: The altitude and longitude coordinates of the stadium in decimal degrees. These are taken from Google maps (“Google” n.d.) from as close to midfield as possible.
Orientation: The orientation of the field in degrees as defined by measuring two points along the sideline as far from each other as possible (“Distance Direction Calculating between Two Location on the Planet Directly on the Map” n.d.). The north-pointing bearing has been used, by convention. This is most valuable for determining the effects of wind, particularly regarding crosswinds, which can be determined by knowing the wind speed direction, the field orientation, and rudimentary trigonometry. The orientation of some fields was found to have already been calculated and publicly available (“CFL Stadium Field Alignment on CFLdb Razzle Dazzle” n.d.).
Elevation: The elevation of the field above sea level in metres (“Google Maps - Find GPS Coordinates, Longitude, Latitude, Altitude” n.d.). Elevation affects air pressure, and thinner air is known to have an effect on ball flight, especially field goals (Clement 2018c). Elevation and thinner air also affect player endurance.
Airport: The ICAO airport code of the nearest major airport to cite weather data. Major airports are typically the most sophisticated weather stations in an area, and produce regular standardized weather statements that can be used to determine local weather at gametime.
Full EZ: A boolean to flag whether the field has full end zones or whether the corners ac clipped, such as by a 400m track. End zones with the corners cut out leave less room for offenses, especially to attempt fade passes in the back corners.
TZ Code: The Olson time zone code of the stadium, to allow for equivalence between different stadia, time zones, and daylight savings (“IANA — Time Zone Database” n.d.). This is important to normalize times to UTC, since any weather data will be given in UTC, and to be able to compare which games may have occurred concurrently despite being in different time zones.
isDome: A boolean to identify whether the stadium is a domed stadium, with its attendant impact on the weather conditions. Domes are kept at constant temperatures, with no wind, regardless of the outside weather.

4. Incorporating Data into Parser

In order to be able to efficiently identify the stadium in which the game was played, the game statements in the data had to be standardized to allow us to slice the string rather than hunt for the presence of all possible stadium names within the text. Furthermore, extraneous data such as the city and province in which the game was played could be removed because that information is implied with the stadium where the game was played. All game statements now follow the format [HOME] vs. [AWAY] YYYY-MM-DD HHMM [STADIUM], where [HOME] and [AWAY] are the three-letter abbreviations of the teams, and [STADIUM] is the name of the stadium. The date format was also clarified to include the kickoff time. Game locations were confirmed from the same sources from which the play-by-play data was derived (U Sports n.d.; OUA n.d.; RSEQ n.d.; CWUAA n.d.; AUS n.d.)

A new class of objects, stadium, was created to hold all the data of each stadium in its attributes. Text data are held as strings, numerica data are held as floats, with the following exceptions:

homeTeams is held as a list of strings, as multiple teams may consider the same stadium to be home.
GPS is held as a list, with latitude and longitude as the two elements, much as a pair of coordinates.

From here a Python dict, stadia, was created, using the stadium name as the key, and the corresponding stadium object as the value.

Within the game object a new attribute, game_stadium, was created. This takes the string slice from the game statement that holds the stadium name and finds the appropriate object within the stadia dict, and assigns that object to game_stadium.

5. Conclusion

All methods that now look through the database have access to the attributes of the stadium by referencing game.stadium.attribute, which will prove useful, for example, to models of field goal probability. Future developments of the database will involve scraping weather data for the appropriate dates and locations of the stadia. The ability to reference weather stations will allow the database to incorporate weather data on a game and play basis, to further develop models.