| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2016-17 | Dinamo Minsk | KHL | 24 | 1 | 5 | 6 | 0.250 | 0.6250 | 0.6796 | 3.4447 | 3.7457 |
| 2018-19 | Dinamo Minsk | KHL | 44 | 0 | 3 | 3 | 0.068 | 0.1705 | 0.1706 | 0.9397 | 0.9400 |
| 2019-20 | Dinamo Minsk | KHL | 61 | 1 | 8 | 9 | 0.147 | 0.3687 | 0.3687 | 2.0324 | 2.0324 |
| 2020-21 | Avangard Omsk | KHL | 45 | 1 | 1 | 2 | 0.044 | 0.1110 | 0.1110 | 0.6118 | 0.6118 |
| 2021-22 | Avangard Omsk | KHL | 42 | 0 | 6 | 6 | 0.143 | 0.3573 | 0.2987 | 1.9690 | 1.6463 |
| 2022-23 | Dynamo Moskva | KHL | 59 | 5 | 4 | 9 | 0.152 | 0.3812 | 0.2881 | 2.1013 | 1.5883 |
| 2023-24 | Dynamo Moskva | KHL | 48 | 0 | 5 | 5 | 0.104 | 0.2605 | 0.1823 | 1.4357 | 1.0049 |
| 2024-25 | Dynamo Moskva | KHL | 59 | 1 | 14 | 15 | 0.254 | 0.6355 | 0.4260 | 3.5025 | 2.3481 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2013-14 | Cornell | D1 | ECAC | SR | 32 | 1 | 3 | 4 | 0.125 |
| 2012-13 | Cornell | D1 | ECAC | JR | 22 | 0 | 0 | 0 | 0.000 |
| 2011-12 | Cornell | D1 | ECAC | SO | 24 | 1 | 7 | 8 | 0.333 |
| 2010-11 | Cornell | D1 | ECAC | FR | 34 | 1 | 6 | 7 | 0.206 |
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.