icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2022-23 | — | BCHL | 31 | 14 | 12 | 90.8% | 2.85 | 1 | 0.9990 | 89.2% |
| 2021-22 | — | BCHL | 38 | 18 | 15 | 91.4% | 2.74 | 2 | 0.9990 | 96.0% |
| 2020-21 | — | BCHL | 7 | 4 | 2 | 89.0% | 3.12 | 0 | 0.9990 | 88.9% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Dartmouth | D1 | 11 | 8 | 3 | 89.9% | 2.12 | 2 |
| 2024-25 | Dartmouth | D1 | 17 | 5 | 9 | 89.5% | 2.70 | 0 |
| 2023-24 | Dartmouth | D1 | 4 | 0 | 2 | 86.0% | 3.93 | 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 |
|---|---|---|---|---|---|---|
| Grant Riley | BCHL | 90.7% | 89.0% | Northeastern | 82.4% | 3.06 |
| Aksel Reid | NAHL | 91.1% | 90.5% | Union | 80.0% | 2.77 |
| Connor McDonough | NAHL | 90.9% | 90.6% | Ferris State | 25.0% | 26.21 |
| Carson Cherepak | MJHL | 92.1% | 89.2% | RPI | 89.6% | 3.11 |
| Freddie Halyk | AJHL | 91.0% | 86.7% | Denver | 88.3% | 2.91 |
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 |
|---|---|---|---|---|---|---|---|
| Michael Paterson-Jones | USPHL-Premier | 92.0% | 88.7% | Wilkes | D3 | 89.5% | 3.08 |
| Ryan Kenny | USPHL-Premier | 93.5% | 89.3% | Stevenson | D3 | 89.6% | 2.95 |
| Marko Belak | MJHL | 90.3% | 91.2% | Gustavus Adolphus | D3 | 87.8% | 3.08 |
| Ryan Nolan | NAHL | 88.9% | 91.0% | St. Olaf | D3 | 93.5% | 2.31 |
| Connor Carbo | EHL | 89.3% | 90.3% | Wentworth | D3 | 92.3% | 3.00 |
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.