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 | 41 | 26 | 12 | 91.4% | 2.74 | 3 | 0.9990 | 88.7% |
| 2021-22 | — | BCHL | 42 | 28 | 9 | 90.9% | 2.53 | 5 | 0.9990 | 91.5% |
| 2020-21 | — | USHL | 21 | 4 | 9 | 83.1% | 4.45 | 0 | 0.9983 | 83.0% |
| 2019-20 | — | USHL | 1 | 1 | 0 | 93.1% | 2.00 | 0 | 0.9983 | 92.9% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Ferris State | D1 | 13 | 1 | 10 | 88.1% | 4.09 | 0 |
| 2024-25 | North Dakota | D1 | 10 | 3 | 4 | 89.4% | 2.80 | 1 |
| 2023-24 | North Dakota | D1 | 7 | 5 | 1 | 90.5% | 2.51 | 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 |
|---|---|---|---|---|---|---|
| Jason Grande | NAHL | 92.5% | 89.6% | Bentley | 89.0% | 2.63 |
| Joshua Kotai | SJHL | 93.5% | 89.3% | Augustana | 90.4% | 3.32 |
| Ryan Manzella | NAHL | 90.4% | 88.3% | Michigan Tech | 90.6% | 2.48 |
| Kolby Matthews | BCHL | 90.0% | 87.5% | RIT | 93.3% | 1.69 |
| William Håkansson | NAHL | 91.2% | 89.0% | Lake Superior State | 83.3% | 4.83 |
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 |
|---|---|---|---|---|---|---|---|
| Nick Anderson | USPHL-Premier | 92.6% | 87.8% | Nichols | D3 | 87.4% | 4.17 |
| Mason Jones | NCDC | 91.1% | 90.0% | Misericordia | D3 | 91.2% | 3.24 |
| Ford DeLoss | USPHL-Premier | 92.0% | 87.3% | Stevenson | D3 | 50.0% | 3.85 |
| Cal Wilcox | NCDC | 89.6% | 87.9% | Suffolk | D3 | 89.1% | 4.18 |
| Kyle Ozgun | NAHL | 90.2% | 90.3% | Skidmore | D3 | 92.5% | 2.30 |
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.