icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2019-20 | — | EHL | 10 | 7 | 3 | 90.4% | 2.66 | 1 | 0.9400 | 85.0% |
| 2018-19 | — | EHL | 24 | 13 | 9 | 89.3% | 2.56 | 2 | 0.9400 | 85.0% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2023-24 | Wentworth | D3 | — | — | — | — | — | — |
| 2022-23 | Wentworth | D3 | 1 | 0 | 1 | 73.3% | 12.00 | — |
| 2021-22 | Wentworth | D3 | 5 | 1 | 4 | 80.9% | 5.03 | — |
| 2020-21 | Wentworth | D3 | — | — | — | — | — | — |
| 2019-20 | Wentworth | D3 | 11 | 4 | 6 | 92.3% | 3.00 | 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 |
|---|---|---|---|---|---|---|
| Albin Boija | SHL-J20 | 92.1% | 85.8% | Maine | 91.6% | 2.01 |
| Brayden Gillespie | OHL | 88.1% | 85.5% | Union | 91.5% | 2.19 |
| Freddie Halyk | AJHL | 91.0% | 87.5% | Denver | 88.3% | 2.91 |
| Kevin Reidler | USHL | 90.2% | 87.9% | Nebraska Omaha | 92.0% | 2.74 |
| Max Weilandt | USHL | 88.7% | 86.0% | Northern Michigan | 87.0% | 3.43 |
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 |
|---|---|---|---|---|---|---|---|
| Zach Dosan | NA3HL | 92.1% | 86.7% | Gustavus Adolphus | D3 | 72.5% | 6.60 |
| Michael Paterson-Jones | USPHL-Premier | 92.0% | 88.9% | Wilkes | D3 | 89.5% | 3.08 |
| Nevin Tardif | USPHL-Premier | 90.4% | 87.8% | Worcester State | D3 | 89.8% | 3.54 |
| Tyler Fromolz | NA3HL | 92.0% | 86.3% | Marian | D3 | 88.3% | 3.85 |
| Ryan Nolan | NAHL | 88.9% | 90.6% | St. Olaf | D3 | 93.5% | 2.31 |
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.