Hit Model Experimentation
Choose one batter to get a hit in tomorrow’s games. You can choose any batter you’d like. This game is simple. This game is called Beat the Streak. This game is hard. The trick is that you need to do it 56 times in a row. As of this writing, there have been 743 games played and 27,440 at-bats recorded in 2022. You can only choose one per day, however, and currently, the leader of the Beat the Streak challenge has chosen 29 consecutive hitters to hit. They are just over halfway there. I enjoy playing this game, but it can be really demoralizing, and I’m just doing it from my couch, relying on other people to get a hit. I can’t imagine what it’s like to have a stadium full of people yelling at you and then actually trying to get a real hit. I’ve written a few times about a model I’m using to aid in the choosing of my daily picks, but I’m starting to wonder if there’s a better way. It would be nice to just look at one or two metrics that help decide, rather than running a daily model, merging matchups, and splicing in park factors. In this post, I’ll walk through an experimental process that simply asks the question, what 2022 season statistics from the leaderboards here at FanGraphs can help us choose likely hitters.
