The 2018/19 Scottish Premiership season was one in which goalkeepers contributed heavily to its story-lines.

Alan McGregor made a heralded returned to Rangers, replacing Wes Foderingham as the club’s number one, while also being involved in several controversial moments. Scott Bain replacing Craig Gordon as Celtic’s number one was a clear example of how some managers now value a keeper’s distribution skills at least as much as their shot-stopping abilities. Hearts, Hibs and Kilmarnock all brought in new goalkeepers at the start of the season, St. Mirren recruited successfully during it, and at Motherwell Trever Carson was unfortunately side-lined from December onward due to illness.

Last summer I did a season review of goalkeepers for Modern Fitba, looking solely at shot-stopping. This season, thanks to the data provided by ORTEC sport, we can expand that to also involve aerial command and distribution. Will the numbers back up the ‘accepted’ narratives about which goalkeepers performed well and which ones struggled?

This first part will analyse what most fans will probably see as a goalkeeper’s number one job: shot-stopping.

Expected Saves

For a long time, the one major goalkeeping statistic was how many clean sheets a keeper had achieved through a season. It’s a woefully flawed stat category and tells us very little about the quality of a goalkeeper. Save percentage is better: how many saves a goalkeeper made compared to have many shots on target they faced will give us a lot more information about their abilities. But it is still flawed as it contains no information about where shots were taken from and how big a chance that attempt on goal came from.

Step forward Expected Goals. Used primarily to measure the quality of the chances an outfield players gets to, it can easily be reversed to fit a goalkeeper metric: how big were the chances a keeper faced and how many saves was he ‘expected’ to make from such chances? A more detailed explanation of how a xG model for goalkeepers is set up can be found both in last season’s review and this introductory piece, but the main difference from a standard xG model is that all shots that did not hit the target are removed.

For example, if a type of chance is scored 1 in every 4 attempts, it has a standard xG of 0.25 (as it is converted 25% of the time). However, remove all the shots that did not hit the target and you might find that those attempts that did resulted in a goal 50% of the time. So the xG in a goalkeeper model would for the same type of chance be 0.50.

As with all stat categories, there are still flaws here: the major one is that the ORTEC data does not tell us anything about the quality of the shot taken, which will obviously impact on the difficulty of any save. But a metric where we measure Goals Against (GA) compared to the Expected Goals Against (xGA), will tell us a lot more about a goalkeepers shot-stopping ability than Save Percentage or (shudder) amount of clean sheets. This metric is often also referred to as Expected Saves

Explanation out of the way, let’s check the results.