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 | 40 | 26 | 8 | 91.2% | 2.18 | 4 | 0.9843 | 88.3% |
| 2021-22 | — | SHL | — | — | — | — | — | — | 1.0100 | — |
| 2021-22 | — | SuperElit | 23 | 11 | 11 | 88.7% | 3.34 | 1 | 0.9600 | 83.7% |
| 2021-22 | — | SHL-J20 | 23 | 11 | 11 | 88.7% | 3.34 | 1 | 0.9600 | 83.7% |
| 2020-21 | — | SuperElit | 1 | 0 | 1 | 87.5% | 5.26 | 0 | 0.9600 | 84.0% |
| 2020-21 | — | SHL-J20 | 1 | 0 | 1 | 87.5% | 5.26 | 0 | 0.9600 | 84.0% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2023-24 | Lake Superior State | D1 | 4 | 1 | 1 | 83.3% | 4.83 | 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 |
|---|---|---|---|---|---|---|
| Joshua Kotai | SJHL | 93.5% | 88.0% | Augustana | 90.4% | 3.32 |
| Ryan Manzella | NAHL | 90.4% | 87.7% | Michigan Tech | 90.6% | 2.48 |
| Logan Neaton | BCHL | 91.4% | 87.3% | UMass Lowell | 86.9% | 3.85 |
| Oliver Auyeung-Ashton | BCHL | 90.9% | 87.4% | Northern Michigan | 93.0% | 2.67 |
| Bryant Marks | NAHL | 89.7% | 87.0% | Alaska Anchorage | 94.1% | 1.33 |
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 |
|---|---|---|---|---|---|---|---|
| Chad Lowe | USPHL-Premier | 93.5% | 86.9% | SUNY Morrisville | D3 | 90.2% | 4.99 |
| Cal Wilcox | NCDC | 89.6% | 86.7% | Suffolk | D3 | 89.1% | 4.18 |
| Kyle Ozgun | NAHL | 90.2% | 89.4% | Skidmore | D3 | 92.5% | 2.30 |
| Jackson Fellner | SJHL | 89.9% | 85.7% | Alvernia | D3 | 92.4% | 2.68 |
| Mason Jones | NCDC | 91.1% | 89.1% | Misericordia | D3 | 91.2% | 3.24 |
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.