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 | 24 | 7 | 13 | 89.0% | 3.68 | 0 | 0.9843 | 83.2% |
| 2020-21 | — | USPHL-Premier | 7 | 4 | 3 | 89.6% | 3.43 | 0 | 0.9400 | 84.2% |
| 2020-21 | — | MJHL | 7 | 6 | 0 | 91.9% | 2.42 | 0 | 0.9700 | 89.1% |
| 2019-20 | — | MJHL | 24 | 13 | 6 | 89.6% | 2.96 | 0 | 0.9700 | 86.9% |
| 2019-20 | — | AJHL | 2 | 0 | 0 | 81.8% | 6.41 | 0 | 0.9700 | 79.3% |
| 2018-19 | — | OJHL | 1 | 0 | 0 | 85.7% | 4.83 | 0 | 0.9700 | 86.3% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Trine | D3 | 16 | — | — | 91.7% | 2.09 | 0 |
| 2024-25 | Trine | D3 | 14 | — | — | 88.3% | 2.66 | 0 |
| 2023-24 | Trine | D3 | 23 | — | — | 89.7% | 2.33 | 0 |
| 2022-23 | Trine | D3 | 16 | 8 | 5 | 91.5% | 2.17 | — |
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 |
|---|---|---|---|---|---|---|
| Jacob Zacharewicz | NAHL | 89.2% | 83.4% | Brown | 86.8% | 4.19 |
| Luke Lush | AJHL | 91.9% | 83.0% | Sacred Heart | 91.5% | 2.11 |
| Daniel Hauser | WHL | 91.3% | 83.7% | Wisconsin | 90.0% | 2.49 |
| Thomas Heaney | NAHL | 90.0% | 84.2% | UConn | 86.7% | 4.39 |
| Ryan Snowden | USHL | 89.8% | 82.7% | Ohio State | 90.5% | 2.52 |
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 |
|---|---|---|---|---|---|---|---|
| Nikolas Trakakis | MJHL | 87.9% | 83.1% | Chatham | D3 | 93.4% | 2.45 |
| Blake Bjella | USPHL-Premier | 91.5% | 83.1% | Worcester State | D3 | 87.0% | 3.54 |
| Graham Burke | NAHL | 86.2% | 82.8% | Hobart | D3 | 95.1% | 1.00 |
| Anthony Bonaldi | USPHL-Premier | 90.6% | 82.7% | Nichols | D3 | 81.8% | 7.78 |
| Josh Nadler | AJHL | 89.5% | 84.2% | Hamilton | D3 | 68.8% | 12.03 |
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.