written by – Rangers Report
Dedicated followers of this site will know that we have been playing around with the idea of measuring production based on how many minutes a team actually possesses the ball. This ongoing experiment is based on ideas that North Yard Analytics’ Dan Altman discussed in his short series of videos on YouTube.
As you may know, when looking at offensive production, most advanced stats are broken down to per 90 minutes averages. But, in reality a team only sees so much of the ball in a 90 minute match. In the Scottish Championship, like every other league, there is a real spectrum of how much possession each team has in each game.
Is it fair to compare the offensive production of players from Dumbarton to players from Hibs or Rangers? Hypothetically, a player on Dumbarton only has a chance to produce offensively in 44% of the match (or roughly 48 minutes). Meanwhile, a player on Rangers has the advantage of being on the ball for 66% of the match (or 73 minutes). If a player has five goals for Dumbarton & another has five for Rangers & they both have played the same amount of minutes – which production is the most impressive? The underlying numbers would suggest that it may actually be the player from Dumbarton (for the record, no players from Dumbarton has more then two non-penalty goals).
This is not an attempt to devalue players who are producing on high possession teams, rather it is an attempt to find some diamonds in the rough, while simultaneously shining the spotlight on particularly strong performances from those top end teams.
After months of experimenting with some ideas, this is an attempt to hone in on a series of stats that projects how much production players would have if their team possessed the ball for the full 90 minutes. Yes, the full 90. It is a trip to imaginationland to level the playing field while analyzing statistics.
Given that this imaginary scenario measures production as if a team possessed the ball for a full 90 minutes….let’s call the following stats iGoals per 90 & iShots per 90 & so on.
So, how does all of this work? The idea is to use possession statistics to project how many minutes a player’s team actually has the ball. For example, Jason Holt has played 1,100 minutes this season & Rangers has averaged a 66% possession rate in his games. Hence, hypothetically Rangers have had the ball for 726 minutes of Holt’s time on the pitch. Holt has scored six goals this season which translates to a real goals per 90 average of 0.49 — 6 divided by (1100/90). But if you apply those goals to the aforementioned 726 minutes it ends up being 0.74 iGoals per 90 minutes — 6 divided by (726/90).
The following lists the iGoals per 90 leaders in the Scottish Championship. I was pretty liberal with minutes played, only excluding a few players who had yet to play 200+ minutes.
James Keatings & John Baird are also the leaders in (real) goals per 90 minutes. Keatings is averaging 0.95 (real) goals per 90 minutes & Baird is averaging 0.65 per 90. They both play on strong possession teams but their success rate is impressive nonetheless. James Keatings has scored six non-penalty goals in 568 minutes playing for a Hibs side that averages 56% possession in the games that he has played – that projects to a scintillating 1.71 iGoals per 90.
The Livingston duo of Jordyn Sheerin & Jordan White are examples of the ‘diamonds in the rough’ that this stat can spotlight. The two rank fourth & eighth, respectfully, in goals per 90 minutes but round out the top four in iGoals per 90. The fact that they are producing on a team that struggles to dominate possession suggests that they are producing at a high rate, despite not seeing the ball as much as their peers from Rangers, Hibs & Falkirk.
Now, let’s apply this same concept to points per 90 (goals+assists) or in this case iPoints per 90.
Now, iShots per 90.
The results function as a means of validating performances on strong teams from players like Baird, Keatings, Martyn Waghorn, & Jason Cummings. Simultaneously, the stock of players on poorer teams rises as their production is placed on a level playing field as their peers on stronger teams.
Ultimately, this post is meant to serve as an introduction to these iStats & further analysis will be coming in the next several weeks & months as the season progresses.
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