icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2020-21 | — | NAHL | 21 | 13 | 4 | 93.1% | 2.18 | 1 | 0.9843 | 91.6% |
| 2019-20 | — | NAHL | 20 | 3 | 13 | 90.6% | 3.54 | 1 | 0.9843 | 89.2% |
| 2018-19 | — | OJHL | 10 | 2 | 7 | 87.8% | 4.41 | 0 | 0.9700 | 86.1% |
| 2018-19 | — | AJHL | 3 | 3 | 0 | 94.0% | 1.00 | 1 | 0.9700 | 92.7% |
| 2018-19 | — | USHL | 1 | 0 | 0 | 88.9% | 5.66 | 0 | 0.9980 | 88.4% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2023-24 | Notre Dame | D1 | — | — | — | — | — | — |
| 2022-23 | Notre Dame | D1 | — | — | — | — | — | — |
| 2021-22 | Notre Dame | D1 | 1 | — | — | — | — | — |
| 2019-20 | Union | D1 | 4 | 1 | 1 | 84.8% | 4.40 | 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 |
|---|---|---|---|---|---|---|
| Owen Bartoszkiewicz | USHL | 88.9% | 88.1% | Minnesota | 88.3% | 3.72 |
| Aku Koskenvuo | SM-Liiga-Jr | 89.7% | 88.3% | Harvard | 87.5% | 3.56 |
| Jackson Irving | USHL | 88.8% | 87.2% | UMass | 100.0% | — |
| Daniel Moor | USHL | 87.9% | 85.9% | Princeton | 85.2% | 2.83 |
| Martin Holtet Lundberg | SHL-J20 | 90.3% | 84.8% | Ferris State | 83.6% | 5.17 |
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 |
|---|---|---|---|---|---|---|---|
| Brody Haynes | NCDC | 92.5% | 92.3% | Elmira | D3 | 94.8% | 1.22 |
| Nevin Tardif | USPHL-Premier | 90.4% | 88.0% | Worcester State | D3 | 89.8% | 3.54 |
| Cooper Rautenstrauch | NCDC | 90.6% | 93.8% | Colby | D3 | 93.4% | 2.08 |
| Cooper Rautenstrauch | NAHL | 88.9% | 94.0% | Colby | D3 | 93.4% | 2.08 |
| Zach Dosan | NA3HL | 92.1% | 87.8% | Gustavus Adolphus | D3 | 72.5% | 6.60 |
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.