icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | — | NAHL | 37 | 14 | 18 | 92.0% | 2.86 | 0 | 0.9843 | 87.0% |
| 2020-21 | — | SM-Liiga-Jr | 11 | — | — | 89.3% | 2.82 | — | 0.9600 | 85.7% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Alaska Fairbanks | D1 | 25 | 13 | 9 | 91.9% | 2.41 | 1 |
| 2024-25 | Alaska Fairbanks | D1 | 13 | 7 | 3 | 92.4% | 2.04 | 0 |
| 2023-24 | Alaska Fairbanks | D1 | 7 | 4 | 1 | 93.1% | 1.77 | 2 |
| 2022-23 | Alaska Fairbanks | D1 | 1 | 0 | 0 | 83.3% | 5.56 | 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 |
|---|---|---|---|---|---|---|
| John Roberts | NAHL | 92.5% | 87.2% | Northern Michigan | 100.0% | — |
| Max Lacroix | NAHL | 91.4% | 87.0% | Boston University | 87.2% | 3.59 |
| Tyler Krivtsov | NAHL | 90.8% | 86.3% | Alaska Anchorage | 89.2% | 3.12 |
| Carter McPhail | NAHL | 93.1% | 87.8% | Ferris State | 88.6% | 3.74 |
| Quentin Miller | BCHL | 92.3% | 86.9% | Denver | 91.6% | 2.39 |
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 |
|---|---|---|---|---|---|---|---|
| Riley Mastowski | EHL | 89.9% | 87.4% | Franklin Pierce | D2 | 89.4% | 3.80 |
| Peter Sterling | NAHL | 90.7% | 88.0% | Endicott | D3 | 93.3% | 1.63 |
| Ryan Henry | NCDC | 91.6% | 87.9% | Nichols | D3 | 92.9% | 2.39 |
| Mason Meyer | USPHL-Premier | 92.8% | 85.0% | Rivier | D3 | 91.4% | 2.07 |
| Andrew Albano | OJHL | 91.7% | 85.8% | Norwich | D3 | 82.4% | 3.00 |
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.