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 | 29 | 11 | 12 | 91.9% | 2.41 | 3 | 0.9843 | 82.5% |
| 2021-22 | — | NAHL | 37 | 15 | 16 | 90.6% | 3.13 | 0 | 0.9843 | 87.4% |
| 2020-21 | — | NAHL | 12 | 3 | 6 | 89.9% | 3.34 | 1 | 0.9843 | 88.5% |
| 2019-20 | — | USHL | — | — | — | — | — | — | 0.9980 | — |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Air Force | D1 | 7 | 1 | 3 | 89.4% | 3.69 | 0 |
| 2024-25 | Air Force | D1 | 3 | 1 | 0 | 87.5% | 3.09 | 0 |
| 2023-24 | Air Force | D1 | 1 | 0 | 0 | 100.0% | — | 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 |
|---|---|---|---|---|---|---|
| Ryan Keane | NAHL | 92.7% | 82.8% | UConn | 100.0% | — |
| Charles-Edward Gravel | BCHL | 93.0% | 82.6% | Mercyhurst | 90.7% | 3.22 |
| Andrew Takacs | NAHL | 91.8% | 82.0% | Colgate | 81.8% | 4.32 |
| J.J. Cataldo | NAHL | 91.8% | 82.3% | Army | 92.2% | 2.41 |
| Eli Pulver | BCHL | 92.4% | 81.7% | Minnesota | 95.8% | 1.00 |
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 |
|---|---|---|---|---|---|---|---|
| Sebastian Woods | NCDC | 90.6% | 81.9% | Manhattanville | D3 | 91.4% | 3.07 |
| Ryan Crowshaw | NAHL | 89.9% | 82.7% | Arcadia | D3 | 94.6% | 1.75 |
| Ford DeLoss | NCDC | 90.0% | 82.1% | Stevenson | D3 | 88.9% | 3.00 |
| Blake Carlson | USPHL-Premier | 94.7% | 82.0% | Framingham State | D3 | 91.9% | 3.26 |
| Noel Olsonawski | MJHL | 91.2% | 82.3% | Concordia | D3 | 92.8% | 2.05 |
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.