Counters de Senna
Los mejores campeones de LoL para sinergizar y contrarrestar a Senna
305,764 Senna Counters y emparejamientos analizados
We computed our best and worst Senna counters using information pulled from 305.764 ranked LoL rounds. We have determined those champs with the highest and lowest rates of victory when facing her. Furthermore, we found which champions can serve as valuable allies to to get more wins.
As shown above, Zyra is the strongest to beat Senna with a 55.7% win fraction against her. In a close second and third place, Swain and Brand are the next largest counters to Senna. They have win rates of 54.3% and 54.3%, respectively. You should not take her into a round where any of these champions has already been chosen.
Mejores counters de Senna
Peores counters de Senna
Conversely, if you are playing against Alistar, you can anticipate doing much better. Senna counters them with a much higher win rate of 52.2%. Equally, you can expect to do very well when battling Lulu and Thresh. They have the next worst win rates versus Senna.
If you are looking for the best champ pairings for your own allies to help improve your win rate, look at the champ synergies below.
Sinergias de Senna
Maestro YiVictorias: 51%
Tahm KenchVictorias: 51%
Regardless of her lane, she does best when paired with Ziggs. This pairing typically enhances her chances of winning by a few percent. Ashe and Maestro Yi are also superb champs to pair with.
If you are looking to learn more about a particular Senna counter, as well as the greatest builds to use in that matchup, please click the related row in the tables above. If the specific Senna counters you are looking for aren't shown below, you can view all potential counters at the link above.
Additionally, if you want to get Senna synergies and counters for a specific skill level, please feel free to pick a different division from the dropdown located above.
Cada semana revisamos millones de partidas de League of Legends sacadas directamente de los servidores de Riot. Analizamos los datos usando tanto estadísticas convencionales como algoritmos avanzados de aprendizaje automático para obtener los counters de Senna más precisos.