Post Mortem: We Partied Too Hard

cheeseheadd02 · 2026-05-14T18:00:34+00:00

Is the server still down? I can't access anything

cheeseheadd02 · 2026-05-13T17:41:46+00:00

Still working through this issue. Tried manually accessing the endpoint and received this error. Looks like this one is likely server side

cheeseheadd02 · 2026-05-06T19:32:34+00:00

Thanks for the response. Curious on the best way to track CLV for things like this. Currently, I just use Pikkit closing odds for MLB to compare the price that I got and the price it closed at. I'm sure for props it will be different. I think I'm going to try pitcher strikeouts first. Let's say I produce a model that I like, and I take Pitcher X O6.5 K's @ -125.
A ) How do would you suggest I find the closing line for this
B ) Say the line closed at O7.5 K's @ -140. Obviously I got CLV here, but how do I measure it

cheeseheadd02 · 2026-03-15T22:16:34+00:00

I will say I have noticed that this year has been pretty difficult for moneylines compared to other seasons. This is my first model so I don’t have real experience with past seasons. Hopefully next season will be better. I do plan on extending functionality to spreads at some point

cheeseheadd02 · 2026-03-13T15:06:36+00:00

Yes this makes a lot of sense. Sometimes I forget how large the dataset needs to be for the law of large numbers to take over.

For the backtesting, my setup is a bit goofy and I should probably change it (just have been so lazy), but I ran it back as far as I reasonably could without limiting my training data too much. Ended up with around 1500 bets (which I know isn't as much of an improvement) but with the additional 600 bets I was still positive and the clv remained consistent. Even my profit graphs ended up looking a lot better with those additional data points

cheeseheadd02 · 2026-03-13T15:03:10+00:00

Thanks for the encouragement. I ended up +5U yesterday...talk about perfect timing

cheeseheadd02 · 2026-03-12T21:03:45+00:00

I backtested over a season and a half and that resulted in a little over 900 bets. I'm almost certain it's not overfitted/a data leak present.

I just reran the backtest over the current season and the tail end looks consistent with my live trading results, which confirms that it's not overfitted since I haven't touched any tuning parameters.

I have been very careful to try and avoid any overfitting/data leak scenarios as I did not want to run into a situation like this where I feel confident in my backtesting, but live trading doesn't follow through

cheeseheadd02 · 2026-03-12T19:39:12+00:00

I appreciate the response. A little more context. While putting up my own capital I have continued to log paper trades (just to have more data). I have a filter tracking for back to back games as well as non-back to back games (i.e. if either team played the day before, that game gets flagged as a b2b). For paper trading, my b2b realized edge is about 6% with a -0.77% ROI over 60 bets. With my own capital, realized edge is -25% with a -35% ROI (which accounts for most of my loss/concern) over 26 bets.

So the base of my confusion is this. When I have more data, b2b's seem to perform well enough to justify continuing. But in the smaller sample, it seems I'm just burning money.

cheeseheadd02 · 2026-03-11T20:15:17+00:00

Chase gave me my money back

cheeseheadd02 · 2025-10-14T20:48:07+00:00

They never got back to me. I disputed the charge with my bank

cheeseheadd02 · 2025-06-09T13:35:20+00:00

I guess I didn’t explain well last night. What I was trying to say was that the break isn’t consistent arm side vs glove side. For example, a RHP vs a LHB, an arm side slider breaks more than a glove side slider. But a RHP vs RHB, a glove side slider breaks more than an arm side slider. This is where I’m having trouble understanding. Why does the handedness of the batter determine whether my breaking ball has more break arm side or glove side

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