icepath.io · Hockey Analytics
icepath.io · Hockey Analytics
| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2016-17 | Drayton Valley Thunder | AJHL | 4 | 0 | 0 | 0 | 0.000 | — | — | — | — |
| 2017-18 | Okotoks Oilers | AJHL | 56 | 9 | 27 | 36 | 0.643 | 0.2156 | 0.2182 | 0.5949 | 0.6020 |
| 2018-19 | Okotoks Oilers | AJHL | 59 | 14 | 32 | 46 | 0.780 | 0.2615 | 0.2519 | 0.7215 | 0.6951 |
| 2019-20 | Okotoks Oilers | AJHL | 52 | 25 | 46 | 71 | 1.365 | 0.4580 | 0.4580 | 1.2635 | 1.2635 |
| 2022-23 | Grant MacEwan Univ. | USports-M | 24 | 8 | 3 | 11 | 0.458 | 0.2418 | 0.2353 | 1.3433 | 1.3075 |
| 2023-24 | Grant MacEwan Univ. | USports-M | 14 | 1 | 6 | 7 | 0.500 | 0.2638 | 0.2428 | 1.4655 | 1.3489 |
| 2024-25 | Grant MacEwan Univ. | USports-M | 25 | 11 | 13 | 24 | 0.960 | 0.5065 | 0.4419 | 2.8138 | 2.4549 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Colorado College | D1 | NCHC | — | 16 | 1 | 0 | 1 | 0.062 |
| 2020-21 | Colorado College | D1 | NCHC | — | 10 | 0 | 0 | 0 | 0.000 |
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.