KBO Model Day 6: Next Steps

Just wanted to jot some quick notes to start the day. I may come back later as time allows.

First, recap of today’s action:

  • 2-3 (thanks to a walkoff homerun by the Dinos in the 10th a few minutes ago)

Yesterday, I adjusted the spreadsheet that is standing in for the model to compare my win percentages against the breakeven odds of the bets, rather than the market implied win percentages. I did so after placing today’s bets. Had I done som earlier, I only would have placed 4 wagers, because the Dinos had a -0.75% value against the breakeven percentage. That would have made me 1-3.

I duplicated my current sheet and made a few cleanups:

  • Made sure my new homefield advantage constant was being used
  • Calculated edge by delta between my win expectancy and the breakeven percentages
  • Like my MLB model, I added a bet sizing function based on edge size
  • Added some better calculations for spitting out what the play is, what the risk and to win numbers are
  • Calculating results based on who won each match up
  • Applied all of the above back to the beginning of the season to get a look at how the more “mature” model would have performed to date.

Here is a comparison between what’s happened to date, and the more mature model:

ModelActual To DateOriginal ModelMature Model
Win %31.58%37.04%31.82%
Total Plays223124

None of that is great. The original model is more aggressive, both making more plays and betting higher, flat amounts. So while the ROI on it seems best, it’s down more than either reality or the revised model.

The big take away here for me is that the real secret sauce in the model should be the bottoms up player based adjustments. I need to work on that before going any further.

To that ends, on Sunday I expanded my scraper to grab the rosters from each team from MyKBOStats and just dumped them into a text file for now so that I wouldn’t have to keep scraping. I’ve saved the following attributes:

  • Team Name
  • Position group (Pitchers, Catchers, Infielders, Outfielders)
  • Anglicized name
  • MyKBOStats player page URL
  • Whether the player is on the active roster or not
  • Korean name

Now that I have a database, I’ll need to figure out how to fetch stats and build statistical profiles. MyKBO has some cursory stats, but Fangraphs or even some sites in Korean will likely be a better source.

Future potential ideas:

  • How is homefield advantage tracking this year? I know it’s small sample size, but the stadiums are empty and well, just the entire world is different this year. Might give me an indicator on the potential impact of playing in empty stadiums for upcoming sports.