This table displays a list of best Shadow Carvanha 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.
Best performing move types
Shadow Carvanha type chart
When fighting Shadow Carvanha, keep in mind the following that Water and Dark-type Pokémon are weak to Bug, Electric, Fairy, Fighting, and Grass type moves. They take reduced damage from Dark, Fire, Ghost, Ice, Steel, Water, and Psychic type moves.
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 Shadow Carvanha in Raid Battles. Whether you're looking for the best counters to take Shadow Carvanha down quickly with high DPS, or the tankiest counters that can withstand its attacks, our guide has something for every trainer.
The top-ranked Shadow Carvanha counter is Mega Sceptile, followed by Mega Blaziken, Terrakion, and Mega Gardevoir. The table also includes Mega Rayquaza, Kartana, Shadow Machamp, and Keldeo (Ordinary), among others. Each Pokemon's move type is indicated by an icon beside the move name.
The most effective move-types to use against Shadow Carvanha are:
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.