How Good Are Those Probabilities on the Apple TV+ Broadcasts?

That’s a mouthful, so let’s take an example. Let’s say, for the sake of argument, that the count-based model gave a batter a 30% chance of reaching base. If the nVenue probability was higher than that, I had them bet “reach base.” If it was lower, they bet “don’t reach base.” The payout is simple: if the model “bets” on reaching base, the payout is 0.7 if the player reaches base (1-0.3), and -0.3 if the player doesn’t reach base. It’s the same for every pitch; the “payout” is either one minus the odds (for a positive result) or the negative of the odds (for a negative result).

You could rinse my model by gambling against it this way. All you have to do is bet on good outcomes when the batter/pitcher matchup favors the hitter (a platoon advantage, say, or a great hitter against a bad pitcher) and vice versa when the opposite is true. It should be incredibly easy to beat this count-only prediction.

In practice, the count-only prediction drubbed nVenue’s displayed probabilities. Over those 2,075 pitches, the nVenue prediction did slightly worse than breakeven when gambling against the count-only predictions, losing 11 units across the entire set. On the other hand, when the count-only predictions got to gamble against nVenue’s posted number, it racked up a score of 134.8 units.