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July 17, 2023 MLB Baseball Betting Cards details:
No Model Changes.
Other notes:
No Model Changes, but there is an unresolved issue with the current parameters. I have a hard cap on eROI as I’m saying my model get’s ahead of itself sometimes with the adjustor and sometimes when it gets too good to be true I need to back off. The current parameters are 20% for negative odds and 30% for positive odds. The unresolved part is how to assess when the parameters been breached.
I use 5 books currently and I line shop each bet selection to get the best one, but what if the line shopping then kicks the eROI over the edge? I don’t like the idea of the cap eROI as it goes against the model’s theory of taking advantage of the WP% edge, but based on backtested data this season its proven out so far that my model breaks (becomes unprofitable) when it gets too high.
I currently have 3 ways to go about enforcing the cap eROI limit:
- Line shop and if the best line breaches then the bet is removed.
- The highest line of the 5 books assessed will be the base to compare eROI, but I will then bet with the best offered line.
- Or I average the lines of the 5 books and use that as the base for eROI.
I don’t like option 1 at all as that penalizes finding great value. I’m also currently in between options 2 and 3, but there really is not much of a difference as the variations are often rather small and I line shop to get that final edge.
With that said you will see the STL game today has a forced unit adjustment as the Edge parameter removed the bet originally, but after further assessment of the lines I decided it was valid as 4 of the 5 books offered the line at -110 which would place the bet just under 20% eROI using the the -110 or -109.6 of all 5 books averaged. This is something I’ll ponder further as there should not be many cases where things get this close.
The Boston/Oakland game was close also, but Boston was around -155 on most books and I would’ve needed to see -170 or worse to make the eROI acceptable. This is counterintuitive as I’d be looking for worst odds, but again I don’t want to bet these games as the backtested data has shown high eROI picks in my model to be unprofitable. So for now the safe play is to just avoid these games until I find a profitable solution.
*See Glossary for details to help explain terms and other recent model changes.
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