They ran the model on historical data. Of 4 a priori known P&Ds, the model detected 2 of them. To say that they've got accuracy of a 50% or "no better than a coin toss", ignores all of the non-P&D events that it correctly didn't identify as a P&D. If you did a coin toss, you would flip the coin much more than 4 times.
You really would also want to have some sort of loss function, or at the very least a general idea of whether you most need to avoid false positives or false negatives. If it was most important to avoid incorrectly saying it's NOT a P&D, then this is not good performance. If you need it to nearly always avoid saying something which is not a P&D (and thus might be a great investment opportunity), but given that you want to avoid as many as you can, 50% might be good.
They ran the model on historical data. Of 4 a priori known P&Ds, the model detected 2 of them. To say that they've got accuracy of a 50% or "no better than a coin toss", ignores all of the non-P&D events that it correctly didn't identify as a P&D. If you did a coin toss, you would flip the coin much more than 4 times.