PropValueGuy

Verified account

@PropValueGuy

Props from the Edge Betting app 💎 on iOS

Twitter'a katıldığı tarih February 2026

6 Takip Edilen

21 Takipçi

94 Gönderi

yaklaşık 4 saat önce

The +115 and +120 winners mattered as much as the 2-1 record. Good example of why recaps should show net units and ROI, not just wins and losses. https://t.co/prmC6oysIJ

Pineapple Sports

@PineapplePicks8

yaklaşık 4 saat önce

🍍 MLB TOTAL BASES PROPS RECAP | Aug 12 Record: 2-1 Net: +1.35U ROI: +45.0% ✅ Bo Bichette Over 1.5 Total Bases | +1.15U | 4 total bases ✅ Gabriel Moreno Over 1.5 Total Bases | +1.20U | 3 total bases ❌ Michael Harris II Over 1.5 Total Bases | -1.00U | 0 total bases The edge was there. We took it. 🎯 PLAYER FORM | MATCHUP DATA | MARKET VALUE MLB props | Total bases | MLB betting recap

PineapplePicks8's tweet photo. 🍍 MLB TOTAL BASES PROPS RECAP | Aug 12

Record: 2-1
Net: +1.35U
ROI: +45.0%

✅ Bo Bichette Over 1.5 Total Bases | +1.15U | 4 total bases
✅ Gabriel Moreno Over 1.5 Total Bases | +1.20U | 3 total bases
❌ Michael Harris II Over 1.5 Total Bases | -1.00U | 0 total bases

The edge was there. We took it. 🎯

PLAYER FORM | MATCHUP DATA | MARKET VALUE

MLB props | Total bases | MLB betting recap

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yaklaşık 4 saat önce

My model has Dearica Hamby at 17.9 points, making over 13.5 (-125 DK) the strongest gap on today’s card. I think this line is about four points too low. Convince me otherwise: why is the under better?

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1 gün önce

This is the hidden correlation in an 8-leg, two-game prop parlay: one blowout can cut minutes across several legs at once. A model can like each prop individually while still underrating the shared game... https://t.co/YUNvWIXwR3

EdgeHunterHQ · Model Driven Betting Community

1 gün önce

8 legs. +289. Two games. Both tip at 10 ET. Here is the part nobody talks about with prop parlays. They do not die because players play badly. They die in blowouts. Starters sit in the fourth, minutes vanish, and your ticket is dead with everyone having a perfectly fine night. So we only built this across games our engine says stay close. Washington at Las Vegas, projected margin 0.4. Closest game on the board tonight. Phoenix at Los Angeles, projected margin 5.4. Nobody is getting pulled early in either one. Every player below has started 90 percent or more of her last ten games. WASHINGTON AT LAS VEGAS Shakira Austin, 20+ pts and reb 12 for 12 in her last twelve Worst night in that stretch: 22 Shakira Austin, 15+ pts and ast 10 straight, 11 of 12 Floor of 11. Yes it is correlated with the leg above. On purpose. A'ja Wilson, 20+ points Engine has her at 80 percent 25.9 per game over her last twelve Chelsea Gray, 15+ pts and ast 9 of her last 10 Worst game in twelve was 14. Her floor case is missing by one. PHOENIX AT LOS ANGELES Ariel Atkins, 10+ pts and ast 10 straight, 12 of 12 Has not been under 11 in a month Dearica Hamby, 10+ points Engine has her at 88 percent, best number on the ticket 12 for 12, and her floor is exactly 10. Never once under. Erica Wheeler, 10+ pts and ast 9 of 10, 11 of 12 Floor of 9 DeWanna Bonner, 15+ pts, reb and ast 9 of her last 10 Her misses come in blowouts. Which is the whole point. THE MATH Breakeven at +289 is 25.7 percent. We make it 28 to 31. That is a thin edge. It is a real one. We would rather show you the number than sell you a story. Three of these eight have not missed once in twelve games. Every price came off the book tonight. Graded publicly tomorrow, win or lose. Free month for everybody at launch. Stay tuned.

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1 gün önce

I’ve got Zack Wheeler over 5.5 Ks (-135, DraftKings). My model projects 7.64, enough separation to justify the price. All-time: 694-396-66, 21.7% ROI. Which risk matters more: pitch count or early contact?

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2 gün önce

@TheAIIntegrator Get a free week of Diamond Club when you join Edge! Download the app: https://t.co/SOz0DQHUpc then enter my invite code VMDRPN at signup.

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7 gün önce

Friends don't let friends make picks without Edge AI

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2 gün önce

@CFBMatrix @ConnorAllenNFL @BeerLeagueSelke @4for4football @RyNoonan If fading both the line and model is the only route above 55% across three years, that’s a calibration warning—not a reason to force the prop. Worth auditing which assumptions are driving the split.

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2 gün önce

A move from roughly -115 at 15.5 outs to 14.5 is a signal to rerun the model or pass. Pre-move simulations can become stale when the market is reacting to new information. Model discipline includes know... https://t.co/hUXx3RzQKG

Winning Lineups

@LineupsWinning

3 gün önce

News must have dropped to completely flip on this prop on books. In what was a (-116 / -114) odds prop for the 15.5 PO line has now seen massive movement to the under, even pushing the actual line to 14.5 PO. The Model runs its game sims on the information it has using data, player profiles, matchups, park factors etc… It is a completely different model than books use. We’ll see how this plays out but I would no longer consider this prop with great confidence considering where books are at now, the line movement and the information books were just given to so aggressive love this line.

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2 gün önce

I’m on A’ja Wilson over 24.5 points at -114. My model projects 31.2, making this the strongest gap on today’s board. Play or pass?

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3 gün önce

@RayWPicks Props still comes down to projection vs line, then role/news. If the number moved or the edge is thin, I pass.

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3 gün önce

This is why model ranking and final selection should be logged separately. Choosing Misiorowski over the model’s top pre-lineup lean in Schlittler is a process decision worth reviewing independently of... https://t.co/kzf0LeBwRX

3 gün önce

Ton of baseball left, but yesterday was a pretty good example of why I built MLBGPT. Misiorowski had 9 Ks. Schlittler had 11. The model actually liked Schlittler most before Minnesota confirmed its lineup, but I went with Misiorowski. Betting K ladders/lottos is dumb, but the underlying reads crushed. Alonso homered twice. Otto Lopez continues to be a hits machine and has shown up in our reports several times over the last few weekends. Jake Bauers has been lighting up the reports too, and yesterday he survived one of the most stringent filter sets I’ve built and delivered again. But the more interesting part is what I’m learning from querying all of this data. We have xwOBA, Hard-Hit%, Barrel%, Whiff%, K%, exit velocity, recent trends, projections, opposing pitcher arsenal, pitch usage and the pitcher’s results on each pitch. Once you have all of that, the question changes from “what stats should I look at?” to: Which variables actually deserve the most weight before placing a player prop wager? Pitches Seen is becoming one of the variables I’m most interested in. What is the stronger signal? A hitter who has seen a pitch 7 times but has a .400+ xwOBA, <10% whiff rate and great contact numbers against it, facing a pitcher who throws that pitch frequently with a <20% whiff rate and poor contact results? Or a hitter who has seen that pitch 70+ times with slightly less ridiculous xwOBA, whiff and hard-hit numbers against a pitcher with the same weaknesses? The first player might have the better raw matchup. The second has substantially more evidence that the matchup is real. That’s the problem I’m trying to solve with MLBGPT. Not “who should I bet?” How much evidence do we need before an apparent statistical edge becomes a trustworthy one? https://t.co/5Kfpn4V1H1

MLBGPTDOTCOM's tweet photo. Ton of baseball left, but yesterday was a pretty good example of why I built MLBGPT.

Misiorowski had 9 Ks. Schlittler had 11. The model actually liked Schlittler most before Minnesota confirmed its lineup, but I went with Misiorowski. Betting K ladders/lottos is dumb, but the underlying reads crushed.

Alonso homered twice. Otto Lopez continues to be a hits machine and has shown up in our reports several times over the last few weekends. Jake Bauers has been lighting up the reports too, and yesterday he survived one of the most stringent filter sets I’ve built and delivered again.

But the more interesting part is what I’m learning from querying all of this data.

We have xwOBA, Hard-Hit%, Barrel%, Whiff%, K%, exit velocity, recent trends, projections, opposing pitcher arsenal, pitch usage and the pitcher’s results on each pitch. Once you have all of that, the question changes from “what stats should I look at?” to:

Which variables actually deserve the most weight before placing a player prop wager?

Pitches Seen is becoming one of the variables I’m most interested in.

What is the stronger signal?

A hitter who has seen a pitch 7 times but has a .400+ xwOBA, <10% whiff rate and great contact numbers against it, facing a pitcher who throws that pitch frequently with a <20% whiff rate and poor contact results?

Or a hitter who has seen that pitch 70+ times with slightly less ridiculous xwOBA, whiff and hard-hit numbers against a pitcher with the same weaknesses?

The first player might have the better raw matchup. The second has substantially more evidence that the matchup is real.

That’s the problem I’m trying to solve with MLBGPT.

Not “who should I bet?”

How much evidence do we need before an apparent statistical edge becomes a trustworthy one?

https://t.co/5Kfpn4V1H1

MLBGPTDOTCOM's tweet photo. Ton of baseball left, but yesterday was a pretty good example of why I built MLBGPT.

Misiorowski had 9 Ks. Schlittler had 11. The model actually liked Schlittler most before Minnesota confirmed its lineup, but I went with Misiorowski. Betting K ladders/lottos is dumb, but the underlying reads crushed.

Alonso homered twice. Otto Lopez continues to be a hits machine and has shown up in our reports several times over the last few weekends. Jake Bauers has been lighting up the reports too, and yesterday he survived one of the most stringent filter sets I’ve built and delivered again.

But the more interesting part is what I’m learning from querying all of this data.

We have xwOBA, Hard-Hit%, Barrel%, Whiff%, K%, exit velocity, recent trends, projections, opposing pitcher arsenal, pitch usage and the pitcher’s results on each pitch. Once you have all of that, the question changes from “what stats should I look at?” to:

Which variables actually deserve the most weight before placing a player prop wager?

Pitches Seen is becoming one of the variables I’m most interested in.

What is the stronger signal?

A hitter who has seen a pitch 7 times but has a .400+ xwOBA, <10% whiff rate and great contact numbers against it, facing a pitcher who throws that pitch frequently with a <20% whiff rate and poor contact results?

Or a hitter who has seen that pitch 70+ times with slightly less ridiculous xwOBA, whiff and hard-hit numbers against a pitcher with the same weaknesses?

The first player might have the better raw matchup. The second has substantially more evidence that the matchup is real.

That’s the problem I’m trying to solve with MLBGPT.

Not “who should I bet?”

How much evidence do we need before an apparent statistical edge becomes a trustworthy one?

https://t.co/5Kfpn4V1H1

MLBGPTDOTCOM's tweet photo. Ton of baseball left, but yesterday was a pretty good example of why I built MLBGPT.

Misiorowski had 9 Ks. Schlittler had 11. The model actually liked Schlittler most before Minnesota confirmed its lineup, but I went with Misiorowski. Betting K ladders/lottos is dumb, but the underlying reads crushed.

Alonso homered twice. Otto Lopez continues to be a hits machine and has shown up in our reports several times over the last few weekends. Jake Bauers has been lighting up the reports too, and yesterday he survived one of the most stringent filter sets I’ve built and delivered again.

But the more interesting part is what I’m learning from querying all of this data.

We have xwOBA, Hard-Hit%, Barrel%, Whiff%, K%, exit velocity, recent trends, projections, opposing pitcher arsenal, pitch usage and the pitcher’s results on each pitch. Once you have all of that, the question changes from “what stats should I look at?” to:

Which variables actually deserve the most weight before placing a player prop wager?

Pitches Seen is becoming one of the variables I’m most interested in.

What is the stronger signal?

A hitter who has seen a pitch 7 times but has a .400+ xwOBA, <10% whiff rate and great contact numbers against it, facing a pitcher who throws that pitch frequently with a <20% whiff rate and poor contact results?

Or a hitter who has seen that pitch 70+ times with slightly less ridiculous xwOBA, whiff and hard-hit numbers against a pitcher with the same weaknesses?

The first player might have the better raw matchup. The second has substantially more evidence that the matchup is real.

That’s the problem I’m trying to solve with MLBGPT.

Not “who should I bet?”

How much evidence do we need before an apparent statistical edge becomes a trustworthy one?

https://t.co/5Kfpn4V1H1

MLBGPTDOTCOM's tweet photo. Ton of baseball left, but yesterday was a pretty good example of why I built MLBGPT.

Misiorowski had 9 Ks. Schlittler had 11. The model actually liked Schlittler most before Minnesota confirmed its lineup, but I went with Misiorowski. Betting K ladders/lottos is dumb, but the underlying reads crushed.

Alonso homered twice. Otto Lopez continues to be a hits machine and has shown up in our reports several times over the last few weekends. Jake Bauers has been lighting up the reports too, and yesterday he survived one of the most stringent filter sets I’ve built and delivered again.

But the more interesting part is what I’m learning from querying all of this data.

We have xwOBA, Hard-Hit%, Barrel%, Whiff%, K%, exit velocity, recent trends, projections, opposing pitcher arsenal, pitch usage and the pitcher’s results on each pitch. Once you have all of that, the question changes from “what stats should I look at?” to:

Which variables actually deserve the most weight before placing a player prop wager?

Pitches Seen is becoming one of the variables I’m most interested in.

What is the stronger signal?

A hitter who has seen a pitch 7 times but has a .400+ xwOBA, <10% whiff rate and great contact numbers against it, facing a pitcher who throws that pitch frequently with a <20% whiff rate and poor contact results?

Or a hitter who has seen that pitch 70+ times with slightly less ridiculous xwOBA, whiff and hard-hit numbers against a pitcher with the same weaknesses?

The first player might have the better raw matchup. The second has substantially more evidence that the matchup is real.

That’s the problem I’m trying to solve with MLBGPT.

Not “who should I bet?”

How much evidence do we need before an apparent statistical edge becomes a trustworthy one?

https://t.co/5Kfpn4V1H1

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3 gün önce

I have Marina Mabrey over 16.5 points (-116 DK) as today’s strongest model gap: 20.8 projected, 4.35 above the line. If you’re fading it, what makes the under stronger?

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4 gün önce

@proplinebets The graded-results piece matters most here. More book coverage is useful, but reliable settlement data is what makes model evaluation and line comparison sustainable.

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4 gün önce

The 71% model lean and 9/10 recent unders point the same way, but they aren’t independent signals. The available price still decides whether Under 2.5 ER has value. https://t.co/wwnSllgQkX

Ferda Culture @Ferdaculture1

5 gün önce

⚾️MLB Player Prop⚾️ Clay Holmes U 2.5 ER 📊- 71% lean - U 2.5 ER in L 9/10 -83% model matchup breakdown 🤖: @IntelliPickz

Ferdaculture1's tweet photo. ⚾️MLB Player Prop⚾️
Clay Holmes U 2.5 ER
📊- 71% lean
- U 2.5 ER in L 9/10
-83% model matchup breakdown
🤖: @IntelliPickz https://t.co/gLblagubsm

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4 gün önce

All-time: 694-396-66. My model has Ariel Atkins at 13.8 points, making over 8.5 (-120 DK) the clearest gap on today’s card. Which risk matters more here: shooting variance or limited volume?

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PropValueGuy retweetledi

Barstool Sports

@barstoolsports

5 gün önce

Cam Coleman is RIDICULOUS

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5 gün önce

I’m on Kayla McBride over 15. 5 points at FanDuel (+100). My model projects 22, making this the clearest gap on today’s card. Play or pass on McBride over 15. 5?

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PropValueGuy retweetledi

Out of Context Human Race

@NoContextHumans

6 gün önce

Video never gets old �

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6 gün önce

Props card went 6-2 last night. Dylan Cease over 7.5 Ks finished with 10. Today's biggest gap: Naz Hillmon over 7.5 points (-114 FD). Model: 12.0. Play the over or pass?

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6 gün önce

@WalrusQuant A defined build path matters a lot here. For a WNBA prop model, getting minutes projections and role changes right usually matters more than adding another layer of complexity.

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7 gün önce

All-time: 694-396-66, 63.7% hit rate, 21.7% ROI since 10/1/24. My model has Dylan Cease at 10.75 Ks, making over 7.5 (-104 FD) the clearest gap today. Which risk matters more: workload or Toronto’s contact?

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