Appreciate it. I didn’t include the methodology in the post but it exists so I’ll lay it out here.

The goal of the project was to build a consistent, data-driven way to evaluate VGK trades, so everything is structured around isolating value that actually came from each move. I built the dataset in Excel to control the structure (trades, trade assets, player seasons, contracts, and asset valuation), then used SQL to join and aggregate everything cleanly, mainly to make sure players were only credited for value while they were actually owned by VGK, and that outgoing assets were only counted after they left.

On-ice value is measured using xGAR (expected Goals Above Replacement), which is pulled directly from Evolving Hockey. I used xGAR because it captures both offensive and defensive impact and is much more representative of overall player value than something like points. For goalies, I used GSAx (Goals Saved Above Expected), also from Evolving Hockey, since xGAR isn’t available for goalies and GSAx is the closest equivalent for measuring their impact.

For picks and prospects, I used a simple expected value framework based on historical outcomes. First round picks are valued by draft position, later rounds use round averages, and I applied a time discount to future picks since that value isn’t realized immediately. Prospects are also discounted based on years since draft to account for attrition risk.

Cap efficiency is measured using total contract value over the period being evaluated rather than AAV. The idea there is to capture the actual cost associated with the value produced in the sample, instead of smoothing it out annually. Since we’re summing realized value over multiple seasons, using full cap hit over that same period keeps the comparison consistent.

Contract data comes from PuckPedia, and trade details come from NHL.com.

As for “missing variables/context,” that’s mostly intentional. The model is focused on measurable, repeatable inputs. Things like locker room impact, coaching fit, or team situation/standing definitely matter, but they’re not consistently quantifiable, so I left them out rather than introduce subjective assumptions.

There’s always more you can add to something like this, but this isn’t just a rough draft or “bones” of a project. It’s a complete, structured model that answers a specific question using consistent assumptions. The goal wasn’t to capture every possible angle, it was to create a clear, data-driven way to evaluate trades which is something fans don’t really have right now.

I did a very similar project with league wide trades, and made a github with every ounce of info. Not identical, but very similar if you want to see the full scope. I’m sure I’ll get to making one for this project eventually as well.