Capped: Projecting Player Salaries (Part One)

Alexander MacLean



This week's Capped begins a series looking into how to project player salaries.


A happy new year to everyone. Hopefully you all enjoyed the break, but now it is back to work, and we don’t miss a week here either. With 2018 upon us, I looked back through the past year of the Capped column, and feel that some of the weekly pieces may have gotten a little off topic, away from the salary and value focus. Now don’t go thinking that all of the fantasy related thoughts and analysis is going to go away, but there are a few specific salary topics that I will be sprinkling in more often.

Outlining the Project

To start off, we are going to outline how to begin to project player salaries. In cap leagues, one of the toughest things to do is analyze the value of a player on an expiring contract. Everyone has their own way to value players versus their current contracts, whether it be dividing points per dollar, by groupings of each million, or spreadsheets breaking down each stat per dollar. However, when it comes to projecting player salaries, the best we really have is our own guesses, usually based off comparisons, and preconceived notions of different players. Over the last few months I have started to compile a bit of an input software, modeling contract projections in an unbiased way. The project is still in its infancy, but it has provided a very good thought project thus far. I have learned quite a bit from it, and at this point have it projecting skater cap hits within about 10% and contract length within 16% (or an average of one-year deviance). Ideally, it can be optimized down to about five percent for each number, but the base thought train is the biggest part, the second half of this is just tinkering with numbers.

The project raised some good questions though, mainly revolving around “what actually influences how much a player makes?”. The base of it comes down to how much value a player can provide above replacement level. Comparing the scoring, to the defence, to leadership and toughness, what has the biggest pull, and with what kind of ratios? Additionally, how do other factors affect the contract, such as age, family, and even the tax rate for each city? Let’s just say it’s a work in progress, and in the meantime, we’ll try to get you some tips for simplifying the process.


The Process

The biggest thing to take away this week, is where to get the baseline for a projection. A projection has to start somewhere, and if it is an unbiased projection we are seeking, it can’t be comparison based (the eye test), it has to be done statistically. Where does that lead us for a player contract? Well it leads right to the salary cap. Each player on a team uses up a certain percentage of a team’s total salary space, with the better players getting a larger chunk of the pie. As a result, by factoring each player based on what they bring to the table, we can split the pie up equally, getting a much clearer picture of what each player deserves.

What we have to remember, is that this is an inexact science. There will always be other variables since no two players are the same. Someone may want to come back and play for their hometown, whereas another player may want to go to the Stanley Cup favourite. Certain players may look for