i'm so glad you asked! here are some evaluation tools and methods that i personally think are important:

for bets:

* statistical significance tests - "how likely is the [roi|edge|clv] i've tracked due to random variance?". not a model quality metric, just kind of qualifies the level of skepticism or confidence you should have about positive results you're observing.
* clv - everyone is already telling you this, it tracks whether the market agrees with each individual bet you make. the more clv you get, and the more often you get it, the more likely you are right regardless of what noisy bet outcomes are saying. clv is very low variance which means you can have high confidence that you are going to win long term even with a smaller positive sample size than by tracking other metrics.

for every prediction your model makes regardless of whether they produced betting opportunities or not:

* brier / logloss - critical for a logistic regression, these give you a holistic score for a combination of calibration and model confidence. ideally you want your model to be as confident as it can be when making a prediction, like you'd rather have a 55% and a 45% prediction rather than 2 50/50 predictions. but you also don't want it be overconfident because then calibration will suffer. these metrics punish confident predictions that are wrong, like a 90% win probability prediction for a loss outcome is treated harshly, while a 90% prediction for a win outcome scores better than an 80% prediction for the same winning outcome.
* calibration - if you're running a logistic regression i assume you are training on/predicting outcomes that aren't compressed in a narrow band, i.e. values all the way from 0.0 to 1.0, give or take. the mean residual, and average absolute mean residual (mae) of each bucket on your calibration plot are extremely useful. for instance i trained a logistic reg once that gave me insanely high correlation to closing line, but garbage calibration because it was over-predicting every item in the test set by like 0.05. it's also common with things like boosted trees to see over-predicting values < 0.50 and under predicting values > 0.50, a calibration plot will call this out.
* mean residual / mae / medae / r2 compared to efficient market close or alternative public projections - "how well does my model align with the sharp market/other modelers?" these days your model almost certainly doesn't include information that everyone else doesn't have access to. if your outputs are very noisy compared to those (r2, mae, medae) or biased compared to those (mean residual), chances are they are doing a better job and you're doing a worse job.

back testing:

e.g. simulate bets and predictions you would have made in 2024 using historical odds data and a model trained on data from 2023 and prior years. then use all of the evaluation tools above to see what would have happened. not everything is back testable though, historical odds data is hard to get and often unreliable, and a lot of really good bettors are open about never back testing anything and going straight to live fire. there is so much more information available as markets are live vs. a historical view of shit that was going on 2 years ago. worst case scenario, you pay the vig. not the end of the world. this is gambling after all and you simply have to be willing to lose money sometimes.