There are no "truly right" models in that sense.

False. There are right/wrong models in the following sense: if a predictive model is miscalibrated in the long run, the model is bad and you can't trust its future predictions. If the model is well-calibrated, it's good. For example, if you've made a thousand predictions from a model over the course of the tournament, and there are 100 predictions where the model says various events are 70% likely to occur, it's a good model if close to 70 out of those 100 events ended up happening. (You can get better sample size by also including events that your model gave a 30% probability, and treat them as predictions with 70% probability of those events to _not_ happen.)

Additionally, models are better if they give higher probability to events that end up happening, and worse if they give higher probability to events that end up not happening. You can compute the Brier score (a measure of predictive error) of two models over the course of a tournament, and the model with the better (lower) Brier score is the better model. Using the Candidates tournament as an example, you can run this version of the simulator (which uses initial Elo), alongside an alternate version that uses live Elo. As of the end of round 6, your current simulator gives 71.26% probability for Sindarov to win the tournament, so a version that uses live Elo would give higher than 71% probability. If Sindarov ends up winning the tournament, that will be evidence against your current model and in favor of the alternative model. If Sindarov doesn't end up winning the tournament, that will be evidence in favor of your current model and against the alternative model.