Is there a faster way to get the index of the maximum value and keep count?

Question

Is there a faster way to get the count of each team having the highest score?

Input

# Random scores per player per round (each player corresponds to an integer from 0 - 100) 
scores_per_round = np.random.rand(10_000,100)

# 100,000 random teams of 8
teams = np.array([random.sample(list(range(100)), 8) for _ in range(100_000)])

Desired Output

# Count of top scores per team key being the index of the team in the teams array and the value being the amount of wins.
{
  0: 20,
  1: 12,
  ...
}

Currently I loop through the rounds, add up each teams score and then grab the maximum values index using np.argmax and store the count in a dictionary.

import random
from collections import defaultdict

win_count = defaultdict(int)

# Random scores
scores_per_round = np.random.rand(10_000,100)

# 100,000 random teams of 8
teams = np.array([random.sample(list(range(100)), 8) for _ in range(100_000)])

# Loop through and keep track of teams wins
for round in range(10_000):
    win_count[np.argmax(np.sum(np.take(scores_per_round[round], teams), axis=1))] += 1