When you see prediction explanations on the leaderboard, DataRobot intentionally picks extreme predictions (some high, some low) to use as examples. In that situation, it's natural to expect that the most influential features for those predictions will be pointing in the same direction. The highest predictions likely have more features that are +, and the lowest predictions likely have more features that are -.
However, if you are looking at prediction explanations for more typical rows, there are likely mixed signals being sent; some features might be + while others might be - for a given row. This is something that naturally arises from the data and the models that result. The final prediction takes all the features into account.