icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2023-24 | — | AJHL | 34 | 20 | 9 | 92.5% | 2.40 | 4 | 0.9700 | 86.0% |
| 2022-23 | — | AJHL | 14 | 8 | 5 | 91.4% | 2.92 | 0 | 0.9700 | 88.1% |
| 2022-23 | — | NAHL | 4 | 0 | 3 | 88.7% | 3.85 | 0 | 0.9843 | 88.2% |
| 2021-22 | — | NAHL | 20 | 12 | 5 | 91.3% | 2.63 | 0 | 0.9843 | 94.0% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Harvard | D1 | 34 | 16 | 16 | 91.0% | 2.72 | 1 |
| 2024-25 | Harvard | D1 | 16 | 5 | 8 | 90.9% | 2.77 | 1 |
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 |
|---|---|---|---|---|---|---|
| Rorke Applebee | BCHL | 90.2% | 86.5% | Lake Superior State | 90.7% | 3.00 |
| Liam Beerman | USHL | 91.2% | 85.7% | Lindenwood | 90.3% | 3.24 |
| Carson Dorfman | NAHL | 90.1% | 86.6% | RPI | 100.0% | — |
| Malachi Klassen | MJHL | 91.7% | 85.4% | Holy Cross | — | 3.04 |
| Mathis Rousseau | QMJHL | 90.5% | 84.7% | Maine | 89.6% | 2.59 |
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 |
|---|---|---|---|---|---|---|---|
| Eric Daniel | USPHL-Premier | 92.0% | 85.7% | King's | D3 | 86.9% | 6.55 |
| Tyler Sayger | NCDC | 89.4% | 86.1% | King's | D3 | 90.5% | 4.40 |
| Jeff Zero | NA3HL | 95.0% | 85.9% | Elmira | D3 | 100.0% | — |
| Kannon Flageolle | NOJHL | 91.3% | 84.9% | Suffolk | D3 | 89.7% | 3.99 |
| Duncan Rolleman | USPHL-Premier | 92.5% | 86.2% | Wilkes | D3 | 90.0% | 3.84 |
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.