Net Goals 101 · component 7 of 11
Suppression
Suppression credits a skater for making opponents worse: the lower danger of their shots with him on the ice, and how often those shots go in, with his goalie held fixed. This page works one real season through from the raw events to the number on the card. Every figure is recomputed from the model’s own data and checked against the published value.
Step 01 · Shot quality allowed
What opponents’ shots are worth with him on the ice
Suppression is Playmaking’s mirror. The same shot-quality regression has a coefficient for every opponent on the ice for an attempt: how much more (or less) dangerous the shooters’ attempts are with him defending than their own records predict. Negative is good. Spurgeon’s even-strength coefficient is −0.00338 xG per attempt against, rank 2 of 2828; on the kill −0.00190. The stingiest at even strength:
- Clayton Stoner −0.0034
- Jared Spurgeon −0.0034
- Paul Gaustad −0.0033
- Shea Weber −0.0031
- Jonas Brodin −0.0031
Step 02 · Price it
Chances taken away, in net goals
On every opponent attempt while he was on the ice he is credited minus his coefficient times the number of defenders who shared the charge for it (5 at 5-on-5, 4 on a 5-on-4 kill), times the on-ice price of an expected goal against: 0.2093 net goals at even strength, 0.1638 on the kill.
| Attempts against | Σ defenders | Coefficient | Price | Credit | |
|---|---|---|---|---|---|
| Even strength | 898 | 4,568 | −0.003384 | 0.2093 | +3.235 |
| Penalty kill | 216 | 872 | −0.001904 | 0.1638 | +0.272 |
| Shot-quality credit | +3.507 |
Step 03 · Finishing allowed
Goals against above expected
The second part asks whether shots on goal go in less often than their price with him on the ice. It is a regression on every shot on goal with the shooter and the defending goalie in the model, so a skater is not credited for his goalie’s saves, and it is shrunk very hard (30,000 shots). It is real but small. The credit is priced at the goalie’s standings value, 0.9776 net goals per goal:
| Shots on goal | Coefficient | Price | Credit | |
|---|---|---|---|---|
| Even strength | 552 | −0.000480 | 0.9776 | +0.259 |
| Penalty kill | 151 | +0.000035 | 0.9776 | −0.005 |
| Finishing-allowed credit | +0.254 |
Step 04 · Zero-sum
Who pays for it
The shot-quality credit comes out of the shared defensive columns: on every attempt against, each defender on the ice (him included) pays the price times the sum of the defenders’ coefficients, out of EV defense or the penalty kill. The finishing-allowed credit comes out of the defending goalie’s GSAx, which is where the model had booked every goal against versus expected. For Spurgeon:
| Column | Season total |
|---|---|
| Suppression credit (quality + finishing) | +3.761 |
| paid from his EV defense | −1.039 |
| paid from his penalty kill | −0.113 |
Re-centred on the league per game played:
Step 05 · Position
Measured against the average defenceman
After every other step, each component is re-centred once more, separately for forwards and for defencemen, so the average defenceman in 2025-26 is exactly zero in every column. The games-weighted mean suppression of all defencemen that season was +0.00019 net goals per game, so 79 games of it come off:
Step 06 · Result
Per 84 games
Ratings are quoted per 84 games, one full schedule, so a part season and a full one sit on the same scale.
Recomputed +4.08; his 2025-26 row on the player card and the Players table shows +4.1. They match.
Step 07 · Context
How unusual is this?
| Percentile | 5th | 25th | Median | 75th | 95th | Best |
|---|---|---|---|---|---|---|
| Suppression NG/84 | −1.6 | −0.6 | +0.0 | +0.6 | +1.5 | +4.1 |
Skaters with 40 or more games in 2025-26 (612 players). Jared Spurgeon ranks 1 of 612.
Suppression is already shrunk inside its two regressions, so careers need almost no second prior.
The numbers on this page were rebuilt on 2026-10-08 from the same files the cards are built from. The model behind every step is described on Net Goals 101; definitions are in the Reference.