icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2024-25 | — | NAHL | 7 | 3 | 2 | 88.3% | 3.48 | 0 | 0.9843 | 85.3% |
| 2023-24 | — | NAHL | 18 | 10 | 2 | 90.9% | 2.95 | 0 | 0.9843 | 91.3% |
| 2022-23 | — | NAHL | 1 | 1 | 0 | 88.9% | 4.00 | 0 | 0.9843 | 92.4% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | St. Norbert | D3 | 4 | — | — | 92.4% | 2.00 | 0 |
| 2024-25 | St. Norbert | D3 | 7 | — | — | 93.2% | 1.73 | 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 |
|---|---|---|---|---|---|---|
| Kristoffer Eberly | USHL | 89.3% | 84.6% | Ohio State | 91.1% | 2.98 |
| Julian Molinaro | AJHL | 91.6% | 85.8% | Northern Michigan | 95.7% | 1.81 |
| Jackson Unger | WHL | 89.9% | 85.7% | Colorado College | 91.1% | 2.53 |
| Jake Barczewski | USHL | 90.4% | 86.0% | Canisius | 90.5% | 2.92 |
| Josh Fleming | QMJHL | 90.8% | 85.6% | Penn State | 92.4% | 2.61 |
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 |
|---|---|---|---|---|---|---|---|
| Collin Lemanski | NA3HL | 93.6% | 86.1% | St. Norbert | D3 | 100.0% | — |
| Dylan Dewatcher | OJHL | 86.3% | 85.2% | Western Connecticut | D3 | 85.8% | 5.91 |
| Caleb Chabot | CCHL | 89.1% | 86.6% | Albertus Magnus | D3 | 93.4% | 1.79 |
| Owen Carlson | NA3HL | 92.1% | 84.2% | Lawrence | D3 | 90.2% | 3.41 |
| Sean Guerin | NOJHL | 90.2% | 84.5% | Southern New Hampshire | D2 | 90.7% | 3.28 |
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.