icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | W | L | SV% | GAA | SO | SVe Factor | Age-Adj SV% |
|---|---|---|---|---|---|---|---|---|---|---|
| 2024-25 | Acadie-Bathurst Titan | QMJHL | 53 | 29 | 20 | 90.8% | 3.08 | 2 | 0.9938 | 85.6% |
| 2023-24 | Acadie-Bathurst Titan | QMJHL | 37 | 13 | 19 | 88.5% | 3.69 | 1 | 0.9938 | 86.6% |
| 2022-23 | — | QMJHL | 40 | 12 | 21 | 90.3% | 3.60 | 1 | 0.9938 | 91.6% |
| 2021-22 | — | QMJHL | 3 | 1 | 2 | 77.8% | 6.01 | 0 | 0.9938 | 81.8% |
| Season | School | Div | GP | W | L | SV% | GAA | SO |
|---|---|---|---|---|---|---|---|---|
| 2025-26 | Penn State | D1 | 20 | 10 | 7 | 92.4% | 2.61 | 2 |
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 |
|---|---|---|---|---|---|---|
| Merek Pipes | MJHL | 92.4% | 86.4% | Union | 66.7% | 15.52 |
| Matthew DellaRusso | NAHL | 89.6% | 86.1% | Northeastern | 66.7% | 21.30 |
| Julian Molinaro | AJHL | 91.6% | 85.8% | Northern Michigan | 95.7% | 1.81 |
| Liam Beerman | USHL | 91.2% | 85.7% | Lindenwood | 90.3% | 3.24 |
| Kristoffer Eberly | USHL | 89.3% | 84.6% | Ohio State | 91.1% | 2.98 |
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 |
|---|---|---|---|---|---|---|---|
| Brady Quackenbush | EHL | 88.2% | 86.1% | Salve Regina | D3 | 88.5% | 2.01 |
| Topher Chirico | SJHL | 90.8% | 86.2% | Wentworth | D3 | 91.6% | 3.05 |
| Jake Horoho | USPHL-Premier | 92.0% | 86.0% | Middlebury | D3 | 89.4% | 3.02 |
| Aidan Richardson | USPHL-Premier | 92.3% | 86.3% | Lebanon Valley | D3 | 90.5% | 3.10 |
| Hunter Virostek | AJHL | 90.1% | 86.9% | Albertus Magnus | D3 | 75.0% | 6.00 |
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.