Consider the power rankings of Missouri and Oklahoma State. How is it possible that Oklahoma State has a higher power ranking than Missouri?

% Define teams
teams = {'Missouri', 'Oklahoma State'};
% Define metrics
wins = [10, 8]; % Total wins
losses = [2, 3]; % Total losses
sos = [0.75, 0.85]; % Strength of schedule (SoS)
recent_performance = [0.6, 0.9]; % Recent performance (normalized [0, 1])
point_diff = [120, 150]; % Cumulative margin of victory
% Assign weights to metrics
weights = [0.3, 0.2, 0.25, 0.25]; % Weights for Wins, SoS, Recent Performance, Point Diff
% Normalize the metrics
wins_norm = wins / max(wins);
losses_norm = 1 - (losses / max(losses)); % Fewer losses = higher score
sos_norm = sos / max(sos);
point_diff_norm = point_diff / max(point_diff);
% Compute the power ranking scores
scores = weights(1)*wins_norm + weights(2)*losses_norm + ...
    weights(3)*sos_norm + weights(4)*point_diff_norm;
% Display results
for i = 1:length(teams)
    fprintf('%s Power Ranking Score: %.2f\n', teams{i}, scores(i));
end

Missouri Power Ranking Score: 0.79
Oklahoma State Power Ranking Score: 0.74

% Visualize metrics comparison (Optional)
metrics = [wins_norm; losses_norm; sos_norm; point_diff_norm]';
bar(metrics, 'grouped');
xticklabels(teams);
legend({'Wins', 'Losses', 'Strength of Schedule', 'Point Differential'});
title('Normalized Metrics Comparison');
ylabel('Normalized Score');

Answer 1

[Manikanta Aditya]

Hi @Oksana,

The power ranking scores for Missouri and Oklahoma State can be influenced by several factors, even if Missouri has more wins.

Let's break down the metrics and weights used in your calculation:

1. Wins: Missouri has more wins (10 vs. 8).
2. Losses: Missouri has fewer losses (2 vs. 3).
3. Strength of Schedule (SoS): Oklahoma State has a higher SoS (0.85 vs. 0.75), indicating they played tougher opponents.
4. Recent Performance: Oklahoma State has a better recent performance (0.9 vs. 0.6).
5. Point Differential: Oklahoma State has a higher cumulative margin of victory (150 vs. 120).

The weights assigned to these metrics are:

• Wins: 0.3
• Losses: 0.2
• SoS: 0.25
• Recent Performance: 0.25

Here's the breakdown of the normalized metrics and the final power ranking scores:

% Define teams
teams = {'Missouri', 'Oklahoma State'};

% Define metrics
wins = [10, 8]; % Total wins
losses = [2, 3]; % Total losses
sos = [0.75, 0.85]; % Strength of schedule (SoS)
recent_performance = [0.6, 0.9]; % Recent performance (normalized [0, 1])
point_diff = [120, 150]; % Cumulative margin of victory

% Assign weights to metrics
weights = [0.3, 0.2, 0.25, 0.25]; % Weights for Wins, SoS, Recent Performance, Point Diff

% Normalize the metrics
wins_norm = wins / max(wins);
losses_norm = 1 - (losses / max(losses)); % Fewer losses = higher score
sos_norm = sos / max(sos);
point_diff_norm = point_diff / max(point_diff);

% Compute the power ranking scores
scores = weights(1)*wins_norm + weights(2)*losses_norm + ...
         weights(3)*sos_norm + weights(4)*recent_performance;

% Display results
for i = 1:length(teams)
    fprintf('%s Power Ranking Score: %.2f\n', teams{i}, scores(i));
end

Missouri Power Ranking Score: 0.74
Oklahoma State Power Ranking Score: 0.71

% Visualize metrics comparison (Optional)
metrics = [wins_norm; losses_norm; sos_norm; recent_performance]';
bar(metrics, 'grouped');
xticklabels(teams);
legend({'Wins', 'Losses', 'Strength of Schedule', 'Recent Performance'});
title('Normalized Metrics Comparison');
ylabel('Normalized Score');

In this case, Oklahoma State's higher strength of schedule, better recent performance, and higher point differential contribute significantly to their overall power ranking score, even though Missouri has more wins. The weights assigned to each metric also play a crucial role in determining the final scores.

I hope this helps and clarifies your doubt.