icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
Washington Capitals
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | — | USHL | 20 | 7 | 7 | 89.9% | 3.20 | 1 | 0.9980 | 83.3% |
| 2020-21 | — | USHL | 3 | 0 | 3 | 89.9% | 3.03 | 0 | 0.9980 | 89.7% |
| 2019-20 | — | OJHL | 24 | 12 | 8 | 90.0% | 3.10 | 2 | 0.9700 | 87.3% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Canisius | D1 | 24 | 11 | 10 | 90.3% | 2.73 | 1 |
| 2024-25 | American International | D1 | 25 | 9 | 14 | 91.3% | 2.80 | 1 |
| 2023-24 | Sacred Heart | D1 | 19 | 8 | 10 | 89.8% | 3.03 | 0 |
| 2022-23 | Quinnipiac | D1 | 8 | 0 | 0 | 84.0% | 3.06 | 0 |
Historical goalies with similar age-adjusted SVe profiles who went on to play NCAA D1.
| Name | Junior League | Junior SV% | Adj SVe | College | NCAA SV% | NCAA GAA |
|---|---|---|---|---|---|---|
| Marcus Brännman | USHL | 90.3% | 83.4% | Providence | 75.0% | 4.01 |
| Samuel Urban | USHL | 89.9% | 83.4% | Arizona State | 90.4% | 3.53 |
| Axel Mangbo | USHL | 89.0% | 82.3% | Vermont | 85.7% | 5.00 |
| Noah Grannan | USHL | 89.1% | 82.9% | Penn State | 88.5% | 3.40 |
| Melvin Strahl | USHL | 90.3% | 83.0% | Michigan | 92.2% | 2.27 |
Historical goalies with similar age-adjusted SVe profiles who went on to play NCAA D2/D3.
| Name | Junior League | Junior SV% | Adj SVe | College | Div | SV% | GAA |
|---|---|---|---|---|---|---|---|
| Brandon Shantz | NA3HL | 89.7% | 82.6% | Plymouth State | D3 | 92.1% | 3.00 |
| Carson Ironside | AJHL | 87.8% | 84.2% | Albertus Magnus | D3 | 89.3% | 2.76 |
| Clément Labillois | SJHL | 88.9% | 84.4% | Assumption | D2 | 93.6% | 2.08 |
| Jackson Fellner | SJHL | 89.9% | 85.7% | Alvernia | D3 | 92.4% | 2.68 |
| Chad Lowe | USPHL-Premier | 93.5% | 86.9% | SUNY Morrisville | D3 | 90.2% | 4.99 |
How to read this: NCAAe and D3e factors convert a player's junior PPG into expected NCAA scoring at the D1 or D3 level. Harder conferences → lower projected PPG for the same player. A strong junior player (e.g. USHL 0.90 PPG) will project much higher in NESCAC than Big Ten because the D3 scoring environment is lower-difficulty.
Strength factor: conferences above 1.0 are harder than average; below 1.0 are easier. The formula is: Base NCAAe PPG ÷ Conference Strength = Projected PPG.