Measuring Possession in the American Hockey League

Updated: November 15, 2013 at 7:34 am by Josh W

Possession is a performance metric that has been well tracked, documented and analyzed by those in the hockey analytics community. The simple premise is if you have the puck more, you are more likely to score and less likely to be scored against.  Strong possession teams have shown to consistently do well over the long term. While the NHL has a plethora of data, other hockey leagues barely track a small percentage of what the NHL makes available. 

Read on to see my attempt at a proxy for possession in the American Hockey League…

As most AHL teams are affiliated with the National Hockey League, the NHL will often place developing young prospects on their AHL farm teams. If we know which teams are strong possession teams, we can likely figure out which NHL teams have better prospects, and in the long run we can guess who has a bright future. 

One of the main proxies for possession is Fenwick – the summation of shot attempts for both teams at even strength (less blocked shot), either represented as a differential or a percentage. Teams over 50% are strong possession teams, while teams under are usually struggling. Nicholas Emptage compared the Fenwick Close % to Shots For % of all NHL teams over the last five years and found an r correlation of 0.925. Over a large sample size Shots For % and Fenwick Close % become quite close.

To create a proxy for possession in the AHL, I looked at all games that have been played so far this year (up to 13 November), added up each teams Shots For and Shots Against and calculated their Shots For %. I executed this with a Python script so during the season I can continue to update these numbers.

This means there are some obvious limitations. These are currently based on a small sample size, since most teams are only 12 games into the AHL season. These numbers also include special teams (not just even strength) and don’t take account score effects. So this a very rough proxy for the possession numbers we usually use for the NHL.


Team Shots For Shots Against Possession
Texas Stars 537 376 58.82%
Syracuse Crunch 429 338 55.93%
Worcester Sharks 323 265 54.93%
San Antonio Rampage 494 409 54.71%
Wilkes-Barre/Scranton Penguins 375 327 53.42%
Albany Devils 391 349 52.84%
Toronto Marlies 345 308 52.83%
Grand Rapids Griffins 414 379 52.21%
Providence Bruins 433 417 50.94%
Abbotsford Heat 491 476 50.78%
Adirondack Phantoms 406 394 50.75%
Manchester Monarchs 445 432 50.74%
St. John’s IceCaps 461 450 50.60%
Bridgeport Sound Tigers 364 360 50.28%
Hershey Bears 333 335 49.85%
Charlotte Checkers 369 373 49.73%
Lake Erie Monsters 344 348 49.71%
Binghamton Senators 417 425 49.52%
Portland Pirates 285 299 48.80%
Springfield Falcons 321 339 48.64%
Hamilton Bulldogs 404 428 48.56%
Utica Comets 334 357 48.34%
Rockford IceHogs 500 551 47.57%
Milwaukee Admirals 321 355 47.49%
Oklahoma City Barons 403 454 47.02%
Chicago Wolves 364 415 46.73%
Hartford Wolf Pack 370 425 46.54%
Iowa Wild 299 354 45.79%
Rochester Americans 365 454 44.57%
Norfolk Admirals 376 521 41.92%


While these numbers are very much likely to change we can see who has been controlling possession in the beginning of the AHL season.  Some teams, such as Bridgepoort, likely have poor possession numbers, under a small sample size, due to being blown out after playing three games in 48 hours against a rested home team.  Utica was a bit surprising as I expected them to be much worse given their starting results of 2-8-1-1 (but a 96% PDO won’t help). 

I hope to update these numbers once every few weeks until I create a site where they are always updated (An AHL BehindTheNet if you would). Eventually I would also like to look at other leagues, including the SHL, SHL-2 and Finnish Liiga as they have some data that could be used for a proxy metric as well.