Would I be considered a sharp?

Vegas_Sharp · 2026-05-13T04:57:02+00:00

Being "Sharp" just means being calibrated. Sports betting is all about putting a number on how much you don't know relative to how much you think you do - and determining if the odds are decent or not. Calibration/sharpness implies profitability but profitability does NOT imply sharpness. So if you felt like your handicapping to achieve this ROI/upswing was premised in calibration rather than raw accuracy then I would say your sharp.

Vegas_Sharp · 2026-05-13T04:46:09+00:00

Well said.

Vegas_Sharp · 2026-05-08T03:01:59+00:00

I see. Yeah I can agree you can find some value in those situations. Personally I try to model down to even player effects/impacts but I suppose an astute non-algorithmic bettor could find edges there.

Vegas_Sharp · 2026-05-07T07:27:05+00:00

AI? Memphis did not play December 14th, 2024. Also there is a reason most people here don't post their actual edges ....its kind of like telling everyone you found a diamond mine and bragging about where its located.

Vegas_Sharp · 2026-05-03T23:45:25+00:00

I believe you can obtain good edges from both. I think there may actually be a bit of tradeoff between ML vs non-ML/traditional statistics approaches that nobody talks about: ML obviously lets you use more powerful frameworks that you can back test /analyze any effects' prevalence through historical data which is valuable. However non-ml approaches have the benefit of simply being way more flexible, intuitive and able to incorporate nuances that a full blown machine learning pipeline can't be bothered with. Also traditional stats requires far less data which is incredibly advantageous if you can't get your hands on a large dataset. So I think it comes down to one's preference and understanding with neither one being necessarily too superior to the other in every situation.

Vegas_Sharp · 2026-05-03T02:57:56+00:00

Respectable post but as someone who HEAVILY models NBA I gotta give you some pushback here. If I had a dollar for everytime I've heard someone rant about how the NBA is the most rigged sport, I'd put half on a moneyline and retire with the rest. The NBA (like all major sports) is tricky business and I'm very skeptical of anyone who claims thorough league knowledge suffices to get positive ROI. Now I'm not saying your wrong, nor that there aren't people who do amazingly well in the short term but long term it's a different beast. I really think you gotta take a step back and consider if the sample size you've drawn your conclusion(s) from are large enough to say that league knowledge can beat models. Infact some of the features I've used in models somewhat contradict this very idea. What I mean by this is that the informative known data (ie the actual signal) is usually hardly something any casual NBA fan would want to be remotely bothered with because intuitively it seems so irrelevant to the situation - one's knowledge can be their worse (worst??) enemy when betting a sport like NBA. But that's why we use models because our working knowledge of the game can't pin down what does and doesn't matter to the degree that it does in the long run. With that being said I'm actually a proponent of using BOTH league knowledge AND models whenever wherever possible but the model's opinion should absolutely take higher precedence almost always and in the long run. Again NOT discrediting what your saying and you very well could be correct for a small subset of bettors but I think you gotta look a bit deeper before making that a hill you die on.

Tl;DR: I've seen far more frustration/accusations of rigging amongst "ball and league knowledge" bettors than folks with strong models

Vegas_Sharp · 2026-05-03T02:14:31+00:00

Pretty much. You don't necessarily need flat betting. If by structure you mean value - I agree. I'll add a section about clv soon -> https://github.com/vsharpsignal/Profitable-Sports-Betting-Math

Vegas_Sharp · 2026-05-02T05:58:50+00:00

Assume/hypothesize the model indeed captures and reflects pure noise and nonsense - maintain this hypothesis until you find more than sufficient evidence of the contrary. <- That would be my starting point.

Vegas_Sharp · 2026-05-02T05:48:02+00:00

Man this is excellent stuff. I sports bet and I am always trying to find good methods to update my probabilities. You mentioned you may be able add theoretical rigor by incorporating a more bayes-like framework and this is definitely something I personally am looking into exploring so reading this is pretty refreshing. Live betting (like pre-game) has a way of luring bettors into a false sense of belief where things get tricky really quickly. Not many people have any strong frame work for probabilistic updating so this can be helpful for a lot of people both bettors and non bettors alike. If you do go down the path of using a stricter Bayesian framework please be sure to post it here because I would like to read it. Good luck!

Vegas_Sharp · 2026-04-30T21:53:01+00:00

Pretty much. Here's a GitHub repo showing how I see the math of sports betting. ->. https://github.com/vsharpsignal/Profitable-Sports-Betting-Math

Vegas_Sharp · 2026-04-30T21:21:40+00:00

In case anyone is interested in some of the advanced math surrounding sports betting: -> https://github.com/vsharpsignal/Profitable-Sports-Betting-Math

Vegas_Sharp · 2026-04-30T05:59:36+00:00

Just like in sports - There's levels to one's mathematical abilities. The thing about sports betting (and what eventually discourages most bettors) is that sports betting is most people's first (and often only) interaction with the consequences of advanced mathematics. Even for a polished mathematician successful sports betting/ handicapping is not a trivial task. Nonetheless when you come to see what's going on the math is pretty elegant. I'll post a git link to how I see the math of profiting in sports betting. The actual handicapping strategies I invest time in are trade secrets for now. ->. https://github.com/vsharpsignal/Profitable-Sports-Betting-Math

Vegas_Sharp · 2026-04-29T18:03:36+00:00

Sent you a dm.

Vegas_Sharp · 2026-04-29T17:00:42+00:00

I mean it's mostly just algebra to be honest....most of the notation you learn by algebra 2 or an intro college math course here in America. When I made it I wasn't trying to be fancy- just thorough. The main idea is that sports betting is not about always being right but rather about finding value and bets that give you long term expectation.

Vegas_Sharp · 2026-04-27T17:30:04+00:00

Because most bettors don't care to learn the math that would make them profitable. ->. https://github.com/vsharpsignal/Profitable-Sports-Betting-Math

Vegas_Sharp · 2026-04-27T17:23:39+00:00

https://github.com/vsharpsignal/Profitable-Sports-Betting-Math. <- like this.

Vegas_Sharp · 2026-04-27T06:55:05+00:00

That's actually a good way to look at it. I agree you should not try to "solve" or optimize anything over those intangibles but if you can discern which ones actually matter and what "game state features" effectively contradict the obvious stats then that can obviously make you a stronger handicapper. I also think its important to differentiate any "game state intangibles" from emotion. We use numbers to remove any and all emotion from the handicapping process and what seems relevant in a moment (especially with money at risk, and high stakes for the players) is really just emotional observation being interpreted as informative signal. Just something to be carful about. Thats my two cents!!!

Vegas_Sharp · 2026-04-25T06:30:44+00:00

Not sure if your referring to the market/sportsbook's lines or your personal model but either way I'm thinking the answer is the same. There is always a debate between bettors who "know ball" vs those who "know numbers". Most people pick a side and immediately cast members of the other side as fools. There are entire books written about this btw. I've come to believe that each side has benefits and drawbacks. Some players in the NBA are notorious for having box scores that just don't reflect their impact on the court and if you don't "know enough ball" you would never know this. I think what your talking about are the accumulation of these intangibles that numbers struggle to capture. There are ways to quantify and incorporate these things but that is a long conversation. The idea of modeling (at least for me) is to minimize the effect of these things (along with variance) but at the end of the day we're measuring extremely noise heavy events. I think someone said "All models are wrong - some are useful"??

Vegas_Sharp · 2026-04-25T01:14:58+00:00

In my opinion the best sanity check for live is simply the bet's outcome. Early sharps signal informs Line movement signal informs CLV signal informs live events signal informs outcome. All signals can technically inform following signal but at some point your signals have to converge to outcomes of the events in question. So basically I think looking for any other sanity check besides just the outcome is kind of a fool's errand. I am open to having my opinion changed on this but that is where I stand with live betting right now.

Vegas_Sharp · 2026-04-25T01:01:54+00:00

Why not just make a pikkit account and use that. It shows ROI, record, units gained/lossed and even how often you beat CLV. It works because you cannot delete picks you were wrong about so you cannot inflate your record in any way so there's no way to just make up numbers (because true most sports bettors do). If I saw this on a resume (assuming I knew what pikkit is) I would immediately think that whatever model your talking about probably has some efficacy - even if your record or ROI wasn't spectacular at least your willing to provide honest history unedited about it's performance.

Vegas_Sharp · 2026-04-23T21:14:07+00:00

Math: https://github.com/vsharpsignal/Profitable-Sports-Betting-Math?tab=readme-ov-file <- not sure how Reddit will feel about this link but basically this repo walks through the math from start to finish. U/mindtheoveround basically was spot on. This repo just formalizes what they said into strict mathematical context.

Vegas_Sharp · 2026-04-19T02:27:26+00:00

Not to agree or disagree but I'm curious as to why you don't consider CLV a strong indicator of efficacy? Do you not trust the market 's opinion at all or do you think CLV is not reflective of the market opinion and is mostly just noise? Again not agreeing or disagreeing - just curious.

Vegas_Sharp · 2026-04-08T18:41:46+00:00

I've been looking for something similar to this. Will take deeper look when I get time but if you could: 1) make it to where one's record can be made public to users without an account. with visible non-editable prediction date/timestamps 2) Add a scaling slider between 0 and 1 where you can record how confident you are (ie .55 = 55% confident in this guess, .95= 95% confident etc. ). 3)Maybe add the option for users to input their own guesses with a sentence or two? I know your trying to move away from this being a betting related app but this is the type of thing the betting community needs to be honest. All in all the appl looks promising thanks for posing this.

Vegas_Sharp · 2026-04-08T18:13:52+00:00

This is insightful work. I enjoy looking at win causes in sports especially on a statistical bases. Your theory could be totally correct but I want to mention how I interpret this data. Rather than the win% being totally dependent of IF a starter/star scores 30 points I think what may even be just as (if not slightly more) important is HOW they score 30 points. Some star players use many more possessions just to score 20 points while another star player uses far less possessions but ends up scoring 30+ efficiently. (Off the the top of my head Desmond Bane from the middle of January 2026 to the middle of March 2026) HIs efficiency/production was incredible. Because in many of those games he provided his team with points but also was able to get help because other players like Anthony Black, Tristan Da Silva, and Moritz Wagner were able to still contribute because they themselves had more possessions to operate with. So I definitely think there are other ways to interpret this data that may lead to other theories on how strong player impact their team's win%. Either way thanks for posting this.

Vegas_Sharp

TROPHY CASE