icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | — | BCHL | 33 | 24 | 7 | 92.4% | 2.62 | 2 | 0.9990 | 87.4% |
| 2020-21 | — | NAHL | 17 | 4 | 8 | 90.6% | 2.87 | 1 | 0.9843 | 89.2% |
| 2020-21 | — | USHL | 1 | 0 | 0 | 66.7% | 7.73 | 0 | 0.9983 | 66.6% |
| 2019-20 | — | NAHL | 19 | 9 | 6 | 91.6% | 2.58 | 2 | 0.9843 | 90.2% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2023-24 | Dartmouth | D1 | 30 | 13 | 8 | 91.0% | 2.58 | 2 |
| 2022-23 | Dartmouth | D1 | 30 | 5 | 23 | 89.9% | 3.07 | 3 |
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 |
|---|---|---|---|---|---|---|
| Owen Say | BCHL | 92.3% | 87.2% | Mercyhurst | 90.3% | 3.39 |
| Matt Ladd | NAHL | 92.2% | 87.4% | Canisius | 86.8% | 3.97 |
| William Gramme | NAHL | 91.9% | 86.9% | Wisconsin | 95.9% | 1.13 |
| Noah Pak | OJHL | 94.5% | 86.1% | Yale | 87.2% | 3.86 |
| Quentin Sigurdson | NAHL | 92.3% | 87.9% | Northeastern | 92.0% | 2.13 |
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 |
|---|---|---|---|---|---|---|---|
| Antoine Robidoux-Hurtubise | EHL | 90.6% | 87.2% | Misericordia | D3 | 84.5% | 5.01 |
| Joseph Giacobbo | CCHL | 91.8% | 87.2% | Utica | D3 | 95.3% | 0.90 |
| Dylan Grover | OJHL | 90.4% | 87.4% | St. John Fisher | D3 | 91.3% | 2.94 |
| Kolby Thornton | AJHL | 91.3% | 87.2% | Aurora | D3 | 91.5% | 3.36 |
| Sam Edmands | USPHL-Premier | 93.4% | 86.5% | Post | D2 | 93.9% | 2.10 |
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.