Not only are Vantagescore and FICO different algorithms, but any given score algorithm may be “calibrated” differently, so the same apparent risk at 650 on one score is at a different number on another score.

The machine learning goal of the models is to rank-order customers by probability of future default by using information in the reports. That is the “predictiveness of the score” goal.

Next, the scores are transformed onto the scale that is actually observed and reported numerically, and there is arbitrary freedom here. Generally they are approximately linear in log risk odds, so that Score = A + B * log(non defaults/defaults). The A and B are arbitrary, and also change in an economic cycle (mostly A). Different calibration means different score for the same predicted risk (predicted ratio of good customers to default customers).

No reason that Vantagescore is calibrated the same as any FICO score, and different subversions may also be calibrated differently.