icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2018-19 | — | CCHL | 45 | 31 | 11 | 92.0% | 2.19 | 3 | 0.9700 | 84.8% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2023-24 | Robert Morris | D1 | 13 | 3 | 4 | 92.6% | 3.13 | 1 |
| 2022-23 | St. Lawrence | D1 | 1 | 0 | 0 | 93.8% | 3.70 | 0 |
| 2021-22 | St. Lawrence | D1 | 1 | 0 | 1 | 91.4% | 3.10 | — |
| 2020-21 | St. Lawrence | D1 | 1 | 0 | 1 | 81.0% | 4.09 | 0 |
| 2019-20 | St. Lawrence | D1 | 15 | 0 | 12 | 89.7% | 3.50 | 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 |
|---|---|---|---|---|---|---|
| Kolby Matthews | BCHL | 90.0% | 85.4% | RIT | 93.3% | 1.69 |
| Melvin Strahl | USHL | 90.3% | 83.0% | Michigan | 92.2% | 2.27 |
| Melvin Strahl | USHL | 90.3% | 83.0% | Michigan State | 92.2% | 2.27 |
| Bryant Marks | NAHL | 89.7% | 87.0% | Alaska Anchorage | 94.1% | 1.33 |
| Troy Kobryn | NCDC | 92.2% | 83.6% | Merrimack | 89.3% | 3.30 |
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 |
|---|---|---|---|---|---|---|---|
| Clément Labillois | SJHL | 88.9% | 84.4% | Assumption | D2 | 93.6% | 2.08 |
| Carson Ironside | AJHL | 87.8% | 84.2% | Albertus Magnus | D3 | 89.3% | 2.76 |
| Ford DeLoss | USPHL-Premier | 92.0% | 85.8% | Stevenson | D3 | 50.0% | 3.85 |
| Jackson Fellner | SJHL | 89.9% | 85.7% | Alvernia | D3 | 92.4% | 2.68 |
| Cal Wilcox | NCDC | 89.6% | 86.7% | Suffolk | D3 | 89.1% | 4.18 |
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.