Although the model has OK calibration, it’s pretty underconfident at the low end, and pretty overconfident at the high end.
In a calibration context, I would think that this is underconfident at both ends: when it predicts a win, it is more likely to win than it thinks and when it predicts a loss it is more likely to lose than it thinks.
My reasoning was that at the low end, the prediction is too high, and at the high end, the prediction is too low, and calling “too high” and “too low” the same type of error would be a bit weird to me. But I can see it your way too.
In a calibration context, I would think that this is underconfident at both ends: when it predicts a win, it is more likely to win than it thinks and when it predicts a loss it is more likely to lose than it thinks.
My reasoning was that at the low end, the prediction is too high, and at the high end, the prediction is too low, and calling “too high” and “too low” the same type of error would be a bit weird to me. But I can see it your way too.