| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2014-15 | Bonnyville Pontiacs | AJHL | 1 | 0 | 0 | 0 | 0.000 | — | — | — | — |
| 2015-16 | — | AJHL | 49 | 8 | 20 | 28 | 0.571 | 0.1916 | 0.2086 | 0.5288 | 0.5758 |
| 2016-17 | Brooks Bandits | AJHL | 60 | 34 | 32 | 66 | 1.100 | 0.3689 | 0.3837 | 1.0179 | 1.0587 |
| 2020-21 | Shanghai Dragons | KHL | 32 | 2 | 1 | 3 | 0.094 | 0.2345 | 0.2345 | 1.2924 | 1.2924 |
| 2021-22 | Shanghai Dragons | KHL | 35 | 7 | 10 | 17 | 0.486 | 1.2143 | 1.4535 | 6.6923 | 8.0103 |
| 2022-23 | Shanghai Dragons | KHL | 60 | 15 | 15 | 30 | 0.500 | 1.2500 | 1.3959 | 6.8894 | 7.6936 |
| 2023-24 | Shanghai Dragons | KHL | 53 | 10 | 14 | 24 | 0.453 | 1.1320 | 1.2008 | 6.2390 | 6.6184 |
| 2024-25 | Shanghai Dragons | KHL | 46 | 6 | 9 | 15 | 0.326 | 0.8153 | 0.8408 | 4.4932 | 4.6337 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2019-20 | Union | D1 | ECAC | JR | 20 | 3 | 4 | 7 | 0.350 |
| 2018-19 | Union | D1 | ECAC | SO | 35 | 5 | 10 | 15 | 0.429 |
| 2017-18 | Union | D1 | ECAC | FR | 33 | 1 | 5 | 6 | 0.182 |
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.