            if games[i]['result_team1'] != '':
                expected.append(float(games[i]['probability_team1']))
                expected.append(float(games[i]['probability_team2']))
                actual.append(float(games[i]['result_team1']))
                actual.append(float(games[i]['result_team2']))

        mse = np.square(np.subtract(expected,actual)).mean()
        if mse < best[1]:
            best[0] = t
            best[1] = mse
    print(f'Best t: {best[0]}')
    print(f'Best RSE: {best[1]}')
