KBO Model Day 5

In general, the KBO takes Mondays off, so there was no action overnight. However, with the COVID-delay to the season, it seems like they will be playing Mondays going forward.

But today, I needed a break. First, the recap from Sunday:

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Got crushed. There was some crazy action, like multiple huge comebacks IN THE SAME GAME. Bad days happen, so I’m not going to dwell. But I figured I’d describe my process until the model gets fully automated.

First, I manually grab each matchup for the day and put them into a Google Sheet that for now is the model. The sheet does a lookup on the Fangraphs ZiPS full season win projections to get the baseline win expectancy. I then apply my proprietary KBO home field advantage to the home team, then calculate the new expected win expectancy for each team in each matchup.

Next, I grab the market prices for each team in each matchup. The sheet then calculates the following:

  • Implied odds based on the market prices
  • Adjusted win % based on market implied odds
  • The delta between my win expectancy and that adjusted odds*
  • Which team the model favors more against the adjusted win %*

I ignore the results of these calculations until I eye-ball test the pitchers. I do this by going to MyKBOStats, selecting each matchup, then each pitcher, which I then lookup on Fangraphs. Since I’m looking at just pitchers, I try to compare the last 3 years of FIP, to see which pitcher seems better. This isn’t perfect because a lot of pitchers only have KBO stats, but some are washed out MLB players, who’s projections are based on MLB expectations.

I’ll color code each team based on which pitcher I like better.

  • Yellow for both if it’s a wash
  • Lighter to darker shades of green for pitchers I like more
  • Lighter to darker shades of red for pitchers I like less

I like to compare the pitching matchups “blind” to the model so that I don’t over value the starter for a team the model likes more so that my assessments are a bit more objective. Then, I compare the starter evaluations to the model’s recommendations. I’ll nudge recommendations based on the net pitching assessment.

Finally, after weighing how strongly the model recommends a team along with the pitching matchup, I’ll determine my bet size. The stronger the net recommendation, the larger the bet.

*Starting with Wednesday’s games, I’ll be comparing my win expectancy with the market implied odds. These are the “break even” odds of the bet, and are a better judge of value for the bet. The way I have been doing it, the adjusted odds and my odds always added up to 1, which meant wherever the model found value on one team, it found an inverse value on the other side. However, I should not be ignoring the “juice”. One of the plays I mad for Tuesday’s games have a negative value for BOTH sides when comparing to the breakeven percentages. This will help me weed out games that I should avoid playing. This is how my original MLB model worked as well.