Best Noibat counters in Pokémon GO
This table displays a list of best Noibat counters in Pokémon GO with their Fast Attacks, Charged Attacks, DPS (damage per second), TDO (total damage output), faints, TTW (time to win), and score. The list is sorted by the score, which is calculated based on the DPS and TDO.
Simulation settings
Extreme↓
3-Star Raid (3600 HP)↓
Best performing move types
47.0%
26.0%
15.0%
4.5%
2.5%
2.0%
1.5%
1.0%
0.5%
Psycho Cut 
Lock-On 
Noibat type chart
When fighting Noibat, keep in mind the following that Flying and Dragon-type Pokémon are weak to Ice, Dragon, Fairy, and Rock type moves. They take reduced damage from Bug, Fighting, Fire, Water, Grass, and Ground type moves.
 Ice | 256.0% |
|---|---|
 Dragon | 160.0% |
 Fairy | 160.0% |
 Rock | 160.0% |
 Grass | 39.1% |
|---|---|
 Ground | 39.1% |
 Bug | 62.5% |
 Fighting | 62.5% |
 Fire | 62.5% |
 Water | 62.5% |
Type chart shows the percentage (%) of damage taken from an incoming attack of a particular type.
About the results
Our guide provides detailed information on recommended Pokémon and moves that are most effective against Noibat in Raid Battles. Whether you're looking for the best counters to take Noibat down quickly with high DPS, or the tankiest counters that can withstand its attacks, our guide has something for every trainer.
The top-ranked Noibat counter is Shadow Mamoswine, followed by Shadow Weavile, Baxcalibur, and Galarian Standard Darmanitan. The table also includes Mega Rayquaza, Mega Gardevoir, Mamoswine, and Mega Glalie, among others. Each Pokemon's move type is indicated by an icon beside the move name.
The most effective move-types to use against Noibat are:
| Typing | Usage % |
|---|---|
| Ice | 47.0% |
| Dragon | 26.0% |
| Rock | 15.0% |
| Fairy | 4.5% |
| Normal | 2.5% |
| Psychic | 2.0% |
| Electric | 1.5% |
| Water | 1.0% |
| Bug | 0.5% |
About our ranking methodology
When calculating the best counters for any Pokémon, our simulator takes into account various factors, such as the defender's typing and average DPS against each attacker, the weather's influence, energy left over from using charge moves, Shadow Pokémon attack and defense stat changes, and more. During the initial phase of simulations, we calculate DPS and TDO for each attacker that is currently available in the game, and then we continue to rank them.
We use a ranking method developed by a Reddit user named /u/Elastic_Space, which is described in detail in this Reddit post. It is a fairly complicated, but very well thought-out mathematical model for predicting simulation results without actually running the simulations. It also correlates with field data almost too well not to be used.
Our Time to Win (TTW) and Faint numbers are also estimated, and should be taken with a grain of salt. Since we do not account for factors like Friendship and Mega damage boost, they will differ from actual experience in the field.