icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2023-24 | — | NCDC | 8 | 4 | 3 | 89.7% | 3.95 | 0 | 0.9400 | 80.4% |
| 2021-22 | — | NAHL | 2 | 0 | 1 | 78.6% | 7.17 | 0 | 0.9843 | 79.6% |
| 2020-21 | — | NA3HL | 14 | 3 | 8 | 91.4% | 3.63 | 0 | 0.9400 | 85.9% |
| 2019-20 | — | USHL | 2 | 0 | 1 | 84.8% | 6.00 | 0 | 0.9983 | 84.7% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Concordia | D3 | 11 | — | — | 82.8% | 6.60 | 0 |
| 2024-25 | Augsburg | D3 | — | — | — | — | — | — |
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 |
|---|---|---|---|---|---|---|
| Andrew Oke | OHL | 87.6% | 80.8% | Lake Superior State | 87.9% | 3.95 |
| Lucas Massie | USHL | 87.9% | 81.1% | Union | 80.5% | 5.73 |
| Mason Kucenski | USHL | 84.9% | 78.3% | St. Lawrence | 82.6% | 8.00 |
| Connor Hasley | USHL | 86.7% | 79.7% | Bentley | 91.0% | 2.95 |
| Aaron Matthews | NCDC | 90.7% | 81.4% | Providence | — | — |
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 |
|---|---|---|---|---|---|---|---|
| Bodee Weiss | BCHL | 87.9% | 80.7% | Aurora | D3 | 87.9% | 2.93 |
| Nolan Hildebrand | BCHL | 89.3% | 81.9% | Babson | D3 | 87.8% | 3.82 |
| Dolan Gilbert | NA3HL | 91.0% | 81.2% | Concordia | D3 | 81.0% | 9.00 |
| Nolan Hildebrand | AJHL | 87.4% | 82.5% | Babson | D3 | 87.8% | 3.82 |
| Cameron Hrdlicka | SJHL | 89.6% | 83.0% | Concordia | D3 | 89.0% | 3.97 |
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.