u/clong1991

I just got a 2018 Dodge Challenger GT from Carvana in Destroyer Gray. I had a 2015 SXT years ago but wanted to level up this time and get the AWD GT model. I took it for a PPI to give the car a once over. Came back with oil leak, transmission leak and all rusty rotors, which for the most part, Carvana implied would be probably covered by their insurance, even the wear and tear parts. But the biggest red flag was my mechanic said that it looked like it had been completely (and poorly) repainted. The color matched the original window sticker and CARFAX. I thought the paint job looked strange, kind of wavy but I wasn’t sure if it was just how that paint color looked because I’d never seen destroyer gray specifically before.

I posted a picture and wanted anyone else’s opinion. Does it look like it was repainted or does that color just have an odd looking texture that reflects in the light like that normally? You can kind of see what I’m talking about above the driver side wheel in the first pic. Second pic is a close up of the hood. Am I bugging or does it look repainted? Is there anyway way to compare and know for sure?

Between this and all the other issues, I’m probably going to return or exchange it anyway but just figured I’d try and get someone else’s opinion.

Quick context: I've been iterating on MLB strikeout prop models since early April. This is the 4th version I've put live and easily the most promising, running since mid-May. Every new version gets backtested by replaying it against bets I'd already placed that meet the new version's criteria, so each one is scored on real, already-settled outcomes rather than a clean-room sim. The honest caveat, before someone beats me to it: that replay only sees lines a prior version already chose to bet, so it's selection-biased toward the old picks. It's a sanity gate, not proof; the live forward (5/20/26 onward) sample is the real test. Solo, fully in production: 4 scans/day, every bet logged, public dashboard. Free, no signup, not selling anything. I'd rather this sub find the hole now than later. I've hit a wall looking for any real improvements at this point and want to continue moving forward if there are any real holes or opportunities to do so.

The model, briefly:

Per-start strikeouts as Negative Binomial (NB2, Var = μ + α·μ²) with a per-pitcher dispersion α, so a metronome like Logan Webb gets a tighter distribution than a max-effort guy like Hunter Greene (currently recovering, no 2026 data yet). α is MLE per pitcher, empirical-Bayes shrunk toward a global prior for small samples.
Mean (μ) from gradient-boosted stages on Statcast + gamelog features, ~2yr half-life weighting.
Probabilities get a Beta calibration pass. The 80% intervals get conformal recalibration so empirical coverage actually lands near 80%.
Bet when model-implied prob vs book-implied prob diverge by 5%+. Quarter-Kelly stored, displayed at 1x.

Numbers (738 settled bets since 5/20): +11% ROI, 43% win rate on a plus-money book (break-even ~40%), CLV +3% and beating the close on 97% of bets. Being straight about it: this ran around +25% early and has regressed toward +11% as the sample grew, which is what you'd expect. I treat +11% as real-but-soft, not a fixed long-run rate.

The thing I most want to argue about: my per-start K MAE (~1.9) is statistically tied with the sportsbook's closing line (the book is arguably a hair sharper), and I only beat "predict the league average every start" by ~3.6%. So the model is NOT more accurate than the market at the mean. Whatever edge exists lives in the distribution shape, the per-pitcher variance, and finding mispriced odds, not in nailing the number. CLV says the edge is real; the mean accuracy says I'm not smarter than Pinnacle. How do you validate a "distributional" edge when your point forecast just matches the market? Is CLV enough, or am I fooling myself?

Pain points I'd genuinely take input on:

Subgroup-inflated edges. Sometimes the model's biggest "edges" cluster in a subgroup where it's systematically off, so the edge is partly an artifact of the misprediction rather than real value, and those bets underperform. For people who've hit this: do you neutralize it in the model (recalibrate by subgroup) or at the betting layer (filter/down-weight the suspect group), and how do you decide which? And how do you reliably tell it apart from just overfitting to a bad stretch?
Retrain cadence. I'm actively testing weekly vs biweekly vs monthly retrains to see which actually holds up out of sample, and I haven't landed on one yet. For anyone running a model in production: what do you trigger retrains on, fixed calendar, a drift detector, or a performance trigger? And has anyone found a drift signal that genuinely predicts degradation rather than just firing on in-season noise? Curious what's worked and what's been a false alarm.
Per-start count benchmarks. I can't find public benchmarks for per-start K count MAE/RMSE (only season-total projection RMSE). If anyone has a "this is good" baseline, I'd love it.

Android check: the dashboard is a Next.js app I've tested almost entirely on iPhone. If you're on Android, I'd appreciate a gut check: does it load fast, do the tables render and scroll right, any dark-mode or layout weirdness? A screenshot of anything broken would be gold.

Link in the comments. Roast away.

Destroyer Gray Color

My MLB strikeout model can't out-predict the closing line. It still profits. Where's the hole? Real Edge or variance?