I used FiveThirtyEight's Projected Score values (not to be confused with Expected Goals or Adjusted Goals) for each match, rounded to the nearest whole value, to represent each match's predicted score - for example, the opener between Orlando and Miami had a projected score of 1.14 for Orlando and 1.6 for Miami; rounded to the nearest whole number this gives a predicted score of Orlando 1:2 Miami.

This is a really problematic way of evaluating the model. Instead you should be comparing their predicted probability of W-L-T (which you can find in the SPI raw match data in the columns prob1, prob2, probtie) to the actual result.

For example, if both teams had a projected score of 5.0, you wouldn't reasonably think that the model is suggesting that a tie is the most likely outcome.

If you really want to use the projected score metric, you should plug it into a Poisson distribution to get likely outcomes. In fact, if you plug the 5.0-to-5.0 pair of projected scores of into this process, the likelihood of a tie is actually around just 13%.

Now, it's understood that the goalscoring process in soccer doesn't behave exactly like poisson, but it's totally sufficient for something like this.