icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | — | NAHL | 35 | 17 | 13 | 90.8% | 2.80 | 2 | 0.9843 | 81.0% |
| 2021-22 | — | NAHL | 14 | 9 | 2 | 90.4% | 2.62 | 1 | 0.9843 | 86.8% |
| 2020-21 | — | NA3HL | 8 | 2 | 2 | 90.7% | 2.55 | 0 | 0.9400 | 85.3% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Augsburg | D3 | 10 | — | — | 89.0% | 3.45 | 0 |
| 2024-25 | Augsburg | D3 | 23 | 8 | 14 | 90.5% | 3.28 | 2 |
| 2023-24 | Northern Michigan | D1 | — | — | — | — | — | — |
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 |
|---|---|---|---|---|---|---|
| Cullen DeYoung | NAHL | 91.0% | 81.0% | Sacred Heart | 94.3% | 1.73 |
| Teagan Kendrick | BCHL | 91.5% | 80.5% | Sacred Heart | 92.0% | 2.42 |
| Thomas Heaney | NAHL | 90.0% | 80.4% | UConn | 86.7% | 4.39 |
| Ethan Robertson | NAHL | 91.5% | 81.8% | Canisius | 90.4% | 3.25 |
| Kayden Hargraves | NAHL | 91.5% | 81.8% | Augustana | 92.0% | 2.72 |
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 |
|---|---|---|---|---|---|---|---|
| Nick Howard | USPHL-Premier | 94.7% | 81.0% | Saint Anselm | D2 | 90.6% | 2.70 |
| Kyle Kozma | NAHL | 89.0% | 81.5% | Trine | D3 | 91.5% | 2.17 |
| Alexei Masanko | OJHL | 92.5% | 81.6% | Manhattanville | D3 | 91.3% | 3.27 |
| Sami Molu | CCHL | 92.5% | 81.5% | Norwich | D3 | 94.3% | 1.62 |
| Tripp Clarke | USPHL-Premier | 93.7% | 80.6% | Manhattanville | D3 | 75.0% | 4.09 |
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.