icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | — | NAHL | 42 | 27 | 11 | 92.1% | 2.09 | 5 | 0.9843 | 86.7% |
| 2018-19 | — | USPHL-Premier | 14 | 4 | 7 | 91.6% | 3.20 | 1 | 0.9400 | 97.0% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Trinity | D3 | 19 | — | — | 91.5% | 2.21 | 1 |
| 2024-25 | Trinity | D3 | 25 | 16 | 7 | 92.5% | 1.97 | — |
| 2023-24 | Trinity | D3 | 30 | 25 | 4 | 94.6% | 1.19 | — |
| 2022-23 | Trinity | D3 | 19 | 13 | 5 | 93.4% | 1.41 | — |
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 |
|---|---|---|---|---|---|---|
| Croix Kochendorfer | NAHL | 92.0% | 86.6% | Robert Morris | 90.6% | 2.95 |
| Lassi Lehti | NAHL | 92.0% | 87.0% | Alaska Fairbanks | 83.3% | 5.56 |
| Klayton Knapp | NAHL | 92.1% | 86.5% | Minnesota Duluth | 90.7% | 2.67 |
| Brandon Perrone | NAHL | 91.2% | 85.4% | Alaska Anchorage | 87.2% | 3.66 |
| Callum Tung | BCHL | 91.7% | 85.8% | UConn | 93.3% | 2.01 |
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 |
|---|---|---|---|---|---|---|---|
| Korbinian Lutz | EHL | 91.0% | 86.4% | Nichols | D3 | 88.8% | 4.09 |
| Andrew Albano | OJHL | 91.7% | 85.8% | Norwich | D3 | 82.4% | 3.00 |
| Blake Hazelton | USPHL-Premier | 93.8% | 85.6% | Concordia | D3 | 90.3% | 3.97 |
| Stefan Carney | NAHL | 90.2% | 87.6% | New England | D3 | 92.6% | 1.83 |
| Cameron Manley | NCDC | 91.7% | 86.8% | Elmira | D3 | 93.0% | 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.