icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2023-24 | — | NAHL | 21 | 8 | 8 | 92.0% | 2.67 | 0 | 0.9843 | 86.6% |
| 2023-24 | Omaha Lancers | USHL | 2 | 0 | 2 | 80.3% | 7.58 | 0 | 0.9980 | 71.5% |
| 2022-23 | — | USHL | 30 | 12 | 12 | 89.6% | 3.01 | 2 | 0.9980 | 86.0% |
| 2021-22 | — | NAHL | 38 | 16 | 16 | 90.8% | 2.79 | 1 | 0.9843 | 97.0% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Robert Morris | D1 | 7 | 0 | 3 | 88.4% | 4.39 | 0 |
| 2024-25 | Robert Morris | D1 | 23 | 7 | 12 | 90.6% | 2.95 | 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 |
|---|---|---|---|---|---|---|
| Klayton Knapp | NAHL | 92.1% | 86.5% | Minnesota Duluth | 90.7% | 2.67 |
| Tomas Anderson | NAHL | 93.7% | 87.7% | Niagara | 89.0% | 3.20 |
| Brandon Perrone | NAHL | 91.2% | 85.4% | Alaska Anchorage | 87.2% | 3.66 |
| Carson Dorfman | NAHL | 90.1% | 84.7% | RPI | 100.0% | — |
| Lassi Lehti | NAHL | 92.0% | 87.0% | Alaska Fairbanks | 83.3% | 5.56 |
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 |
|---|---|---|---|---|---|---|---|
| Cameron Manley | NCDC | 91.7% | 86.8% | Elmira | D3 | 93.0% | 2.10 |
| Korbinian Lutz | EHL | 91.0% | 86.4% | Nichols | D3 | 88.8% | 4.09 |
| Dylan Gorman | EHL | 90.7% | 87.2% | Manhattanville | D3 | — | — |
| Carson Poulin | OJHL | 92.5% | 86.0% | Lake Forest | D3 | 89.7% | 3.18 |
| Ayden Pierce | NCDC | 91.1% | 86.7% | Misericordia | D3 | 93.2% | 2.87 |
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.