icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | — | USHL | 38 | 13 | 20 | 89.3% | 3.06 | 2 | 0.9987 | 85.7% |
| 2020-21 | — | USHL | 25 | 11 | 9 | 90.0% | 2.76 | 1 | 0.9987 | 89.9% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Cornell | D1 | 7 | 4 | 1 | 93.3% | 1.54 | 0 |
| 2024-25 | Cornell | D1 | 5 | 2 | 0 | 95.8% | 0.86 | 1 |
| 2023-24 | Cornell | D1 | 2 | 0 | 2 | 86.3% | 3.67 | 0 |
| 2022-23 | Cornell | D1 | 5 | 1 | 1 | 85.2% | 3.10 | 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 |
|---|---|---|---|---|---|---|
| Melvin Strahl | USHL | 90.3% | 86.4% | Michigan State | 92.2% | 2.27 |
| Axel Mangbo | USHL | 89.0% | 85.5% | Vermont | 85.7% | 5.00 |
| Chase Clark | USHL | 89.9% | 86.4% | Quinnipiac | 84.0% | 3.06 |
| Francis Boisvert | CCHL | 92.0% | 86.9% | St. Lawrence | 89.7% | 3.50 |
| Marcus Brännman | USHL | 90.3% | 86.6% | Providence | 75.0% | 4.01 |
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 |
|---|---|---|---|---|---|---|---|
| Carson Ironside | AJHL | 87.8% | 86.2% | Albertus Magnus | D3 | 89.3% | 2.76 |
| Jackson Fellner | SJHL | 89.9% | 86.4% | Alvernia | D3 | 92.4% | 2.68 |
| Clément Labillois | SJHL | 88.9% | 85.2% | Assumption | D2 | 93.6% | 2.08 |
| Cal Wilcox | NCDC | 89.6% | 87.9% | Suffolk | D3 | 89.1% | 4.18 |
| Ford DeLoss | USPHL-Premier | 92.0% | 87.3% | Stevenson | D3 | 50.0% | 3.85 |
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.