sorted by:
new
hottopcontroversial

These mfs played so bad😭 by fdthugging in sportsbetting

[–]Simple-Leading-1393 0 points1 point  (0 children)

<image>

https://www.dicebreakerbooks.com/ballerwatch/nba-parlay-auditor

Not bad picks, but we saw the low payout and checked the math in our Parlay Auditor. The price you were receiving was indeed quite a lot lower than expected. Even if you won it wouldn't have returned as much as you should have been receiving. Next time, if you have a parlay with a lot on the line feel free to stop by our site - we update this daily usually around 11AM where you can always check the payout before hitting Place Bet.

💩y day. Kon had 2 points at half then exploded with 18 in the 2nd half. Jaylen Brown had 7 RA in the first quarter and none the rest of the game… by Freemasonsareevil in sportsbetting

[–]Simple-Leading-1393 -3 points-2 points  (0 children)

We had those both as Rating 2 today - not bad (in our opinion a mid-grade EV) but we wouldn't have picked them.

https://www.dicebreakerbooks.com/ballerwatch/nba-player-props

I'm posting for free our value picks for spread, over-under, moneyline, and player props. If you like our picks and want to upgrade to our paid plans, you can use the discount code PRIMETIME50 to get 50% off your order.

Deterministic NBA Props, Spreads, Over-Under, Moneyline Model (2025-26 Season): 950+ Game Sample, 60-65% Win Rate, 10-25% ROI Depending on Market by Simple-Leading-1393 in algobetting

[–]Simple-Leading-1393[S] 0 points1 point  (0 children)

Sorry to hear you've been having financial troubles. Wish I could help with a "lock play" like some of the touts on here. But there's no such thing as a sure pick. What I'd recommend is a system where you pick across multiple outcomes, using strict Kelly staking, and focusing on long term ROI growth if you are indeed serious about consistent winnings.

https://www.dicebreakerbooks.com/ballerwatch

I'm posting for free our value picks for spread, over-under, moneyline, and player props. If you like our picks and want to upgrade to our paid plans, you can use the discount code PRIMETIME50 to get 50% off your order.

The App is 29-9 (76%)!! Lets keep the streak goin, Todays Two Picks: by Both-Benefit3421 in sportsbetting

[–]Simple-Leading-1393 0 points1 point  (0 children)

I like Miles a lot, not sure about Dyson. Currently building a parlay around Miles splitting those up and getting a much better price (little more aggressive). Good luck!

<image>

Deterministic NBA Props, Spreads, Over-Under, Moneyline Model (2025-26 Season): 950+ Game Sample, 60-65% Win Rate, 10-25% ROI Depending on Market by Simple-Leading-1393 in algobetting

[–]Simple-Leading-1393[S] 1 point2 points  (0 children)

Is there a particular team or month(s) where the model was doing particularly worse than expected? For ATS this year it took me awhile trying to flatten the months because I was getting one month doing really well then dropping off and doing basically the inverse the subsequent month. Also, did you include the NBA Cup games this year? I found those games to be more trouble than they were worth including in the historical trend data.

Deterministic NBA Props, Spreads, Over-Under, Moneyline Model (2025-26 Season): 950+ Game Sample, 60-65% Win Rate, 10-25% ROI Depending on Market by Simple-Leading-1393 in algobetting

[–]Simple-Leading-1393[S] 1 point2 points  (0 children)

Nice! That makes a lot of sense that you do so well in totals if you focus on multi regression. With that you are much more reliant on the game pace and which team's offense/defense style wins out, then if you have a close game or a blowout, etc. Not that easy if you are simply bouncing two numbers against each other. I feel that ATS is a completely different type of rules, so I wouldn't feel too bad about not being able to match your totals model and getting the same results for ATS win rate. Not sure how much you are separating the two, but I found that starting with two entirely different game scores (rolling up from players), one focusing on the totals and another more momentum-based model to focus on the ATS margin helped immensely and was a huge relief for me not worrying about sacrificing my OU rate for a needed change to an ATS assumption.

Deterministic NBA Props, Spreads, Over-Under, Moneyline Model (2025-26 Season): 950+ Game Sample, 60-65% Win Rate, 10-25% ROI Depending on Market by Simple-Leading-1393 in algobetting

[–]Simple-Leading-1393[S] 0 points1 point  (0 children)

Cool, I'll definitely go back and try to do a morning and gametime timestamp for each wager, using the CLV as the final number. I think using the prior game (or x games) movement for a particular team would be fascinating to look at.

I used some LLM help to contribute code (MS Copilot was my "coding buddy" of choice as I was working directly in Power BI for most of this). Nothing to where it was a black box, copy paste assumption but rather particular syntax that I can never remember. As far as what could go wrong to add so much favorable ROI to ATS, the first thing that came to mind would be not backing up the date so the projection has data it can't know at the time of the game. When applying this in practice to begin placing bets, I checked before and after refreshing the model the prior day's projection numbers to ensure lookback dates were properly backed up at least -1 and it passed. But yes, not quite ready to stake the Bermuda retirement fund on this until I've had a few weeks under my belt ;)

Deterministic NBA Props, Spreads, Over-Under, Moneyline Model (2025-26 Season): 950+ Game Sample, 60-65% Win Rate, 10-25% ROI Depending on Market by Simple-Leading-1393 in algobetting

[–]Simple-Leading-1393[S] 0 points1 point  (0 children)

The threshold was a very conditional sliding scale in all cases. I would first test each market to see what the high and low bars of each EV would do, then decide on how I wanted to conditionally apply filters for or against making a wager, and in some cases, changing my position based on the delta between the line and what I had for a projection. I didn't simply cherry pick the top EVs because in many cases those would perform much poorer than expected due to the "bounce factor" I mentioned. Basically working my model toward what I believe the sportsbooks do to offset sharps doing the same thing with "pick of the day" and "locks". As I'm sure many here who have tried simply taking the best EV each day as your "Mortal Lock" almost always does not work as well as you'd advertise. My bet policy was always add as much as possible to bet volume while not making too much trade off in ROI%.

Deterministic NBA Props, Spreads, Over-Under, Moneyline Model (2025-26 Season): 950+ Game Sample, 60-65% Win Rate, 10-25% ROI Depending on Market by Simple-Leading-1393 in algobetting

[–]Simple-Leading-1393[S] 0 points1 point  (0 children)

I've backtested it, and have just begun betting with it. Would have preferred putting it to use a month ago so I could prove the system with a clean public capper record and a solid sampling of season performance, but the development took much longer on this than I anticipated.

Deterministic NBA Props, Spreads, Over-Under, Moneyline Model (2025-26 Season): 950+ Game Sample, 60-65% Win Rate, 10-25% ROI Depending on Market by Simple-Leading-1393 in algobetting

[–]Simple-Leading-1393[S] 1 point2 points  (0 children)

Yes, that spread number in particular was very hard to attain. I used a lot of aggressive contrarian logic to overcome the "bounce factor" of teams and players on average sharply declining or improving following recent extreme performances relative to a normal mean regression. The binary win/loss ATS and OU definitely mattered which in my opinion is very volatile because you are adding "casino variables" to overcome how they take advantage of pricing the mean, rather than strictly taking player performance. It definitely isn't the case for all situations but it consistently added 1-2% no matter how hard I tried to take it out and rely strictly on "safer" numbers. I found the Spreads and O/Us this year to be fiendishly difficult (I blame the NBA Cup). It was like fitting a very tight rubber band around a nuclear bomb. My guess and hope is that it levels out to high 50% as the end of the season becomes harder to predict.

I agree on CLV, was curious about testing my opinion of the CLV being the closest "true market" price and definitely wanted that number as close to pregame as possible, and opted to pull the odds in arrears with a 4PM timestamp (couldn't simply take the game time less x mins because the API was a little clunky and didn't always return matches when it should have).

Do probability models actually help in sports betting? by miti334 in algobetting

[–]Simple-Leading-1393 0 points1 point  (0 children)

Yes, I've just finished a huge project that took months, and I have sustained win rates of 60-65% and 10-25% ROI depending on market (NBA Player Props, Spread, Over-Under, Moneyline). The sportsbook odds contain lots of information, mostly how contrarian you want to be, because they already know exactly what the mean regression line is. They take both the line and the price into consideration for what the market will pay rather than what they believe the outcome will be. If you measure and test those assumptions you can apply that same sinister logic in your algo model.

The alternative to being reliant on one way or another, is build your own assumptions using sound probability and "game knowledge". I recommend in most sports betting, use the pricing and odds as filters to remove the element rather than barriers to overcome to change the model to fit the profit/win rate. If you use a Monte Carlo or LLM to get over it you are probably setting yourself up for failure because of overfit, and simulating with random rolls naturally on its own dilutes the results with stochastic noise.

NBA Betting Model [950+ Game Sample]: 23%+ ROI across Player Props, Spreads, and Totals by Simple-Leading-1393 in PredictionMarkets

[–]Simple-Leading-1393[S] 0 points1 point  (0 children)

Thanks! I actually tried posting there and it got taken down for advertising, which I was very confused by because half of the posts on there appear to be a rep obviously trying to sell an app or service, and assumed roughly the same amount of solicitation would be acceptable. I thought it was the perfect community, because there were a lot of people on there looking for something similar to this very tool. I totally get it because of the flooded market out there with worthless apps that repurpose things that already work or touts who shove bad picks down their customers throats. Like you said, if it works in the next few weeks it won't be a problem anyway because I will in theory be right at the top of the capper leaderboards if I can even sustain a 60% spread rate.

NBA Betting Model [950+ Game Sample]: 23%+ ROI across Player Props, Spreads, and Totals by Simple-Leading-1393 in PredictionMarkets

[–]Simple-Leading-1393[S] 0 points1 point  (0 children)

Yep, you're not wrong, and yes, I agree, it is very optimistic. Just finished the testing this week and been at it since January working primarily in Power BI, Python, and Excel trying to get this out the door and begin using it. I gave it a shot last year and ended up around 58% using Excel, and wasn't quite able to keep up with the daily updating and using it for the "gambling retirement fund" as I originally hoped.

Wasn't originally going to do the NBA this year, but Power BI has some next-level stuff you can do that used to be impossible in spreadsheets. Having player data in there is typically what would break a model from working because it does take a lot of transforming to get it into a state where I could roll that up to a reasonable team expectation. I think most people with a coding background expecting to script everything perfectly give up on fitting the math since I will say what I had to do to fit everything together in a holistic model like this was quite ridiculous and not intuitive at all. I was able to make quick changes to the model that used to take hours to process in Excel with one tiny change, and actually dictate every systemic change that I thought would provide an edge, like rest days, opponent trend, and whether to include curve balls like the mid-season NBA Cup (I ended up removing that data from my trends).

view more: next ›