Which starting pitchers will finish inside the top ten this season? Who are the top ten pitchers in fantasy baseball right now?

**Los Angeles Dodgers** ML (-250) vs Colorado Rockies
Confidence: 85% (Grade: A)

**Atlanta Braves** ML (-122) vs Minnesota Twins
Confidence: 65.7% (Grade: B)

**Miami Marlins** ML (+224) vs Philadelphia Phillies
Confidence: 62.8% (Grade: B)

**Kansas City Royals** ML (-178) vs Athletics
Confidence: 61.1% (Grade: B)

**St. Louis Cardinals** ML (-116) vs Cincinnati Reds
Confidence: 60.1% (Grade: B)

**Chicago White Sox** ML (+144) vs Chicago Cubs
Confidence: 59.2% (Grade: B)

[ Removed by Reddit on account of violating the content policy. ]

46-41 on legs. +8.93u through nine days.

Today's slate: 6 legs, 2 tickets, 3 game totals.

Fifteen-game slate.

Date	Legs	Net
8/7	7-8	+6.18u
8/8	5-5	-7.10u
8/9	6-2	+8.58u
8/10	6-6	+2.72u
8/11	3-6	-1.83u
8/12	5-5	-4.33u
8/13	3-3	+0.88u
8/14	6-4	+3.85u
8/15	5-2	-0.02u
Total	46-41	+8.93u

One rule changed after last night

I went 5-2 on individual legs — my best record of the run — and still finished essentially flat because both tickets missed by one leg.

Four of my seven legs were earned run unders, and I put three of them together on one ticket.

Two nights earlier I had already written that earned runs were producing some of the widest projection errors.

Last night showed it inside one slate:

Ryan Gusto
Projected: 1.88
Actual: 2

Eduardo Rodriguez
Projected: 2.35
Actual: 2

Troy Melton
Projected: 0.78
Actual: 3

Matthew Boyd
Projected: 1.88
Actual: 7

Two were nearly exact.

Two missed by more than seven runs combined.

So I'm changing the structure:

No ticket will contain three legs from the same prop type.

Tonight, each ticket contains:

• One earned run leg
• One walks leg
• One strikeout leg

Three different statistical categories with different ways to fail.

One ugly inning shouldn't be able to wipe out an entire ticket built from the same stat.

The board

Pitcher	Pick	Odds	Proj	Prob	L5	L10	FPS	Behind	PPO	Game
Blade Tidwell	ER U2.5	-160	0.86	97%	5/5	10/10	69%	27%	5.91	COL at SF
Lake Bachar	BB U1.5	-260	0.46	96%	5/5	10/10	65%	40%	4.88	BOS at PIT
Drew Anderson	K U4.5	-135	3.37	86%	5/5	10/10	61%	34%	5.17	CWS at DET
Hunter Dobbins	K O3.5	-120	4.78	86%	5/5	9/9	58%	37%	5.45	STL at CHC
Michael Soroka	ER U2.5	-110	1.45	86%	4/5	9/10	65%	29%	4.99	ARI at ATL
Logan Henderson	BB U1.5	-140	0.89	79%	4/5	8/10	67%	25%	5.00	MIL at LAD

FPS = first-pitch strike rate over the last five. League average is about 61%.

Behind = share of pitches ending with the pitcher behind in the count over the last five. League average is about 34%.

PPO = pitches per out over the last five.

Six legs.

Six different games.

Three prop types.

Two of each.

Ticket A — One of each, +292

Model estimate: 80.1%
Implied estimate: 25.5%

• Blade Tidwell U2.5 ER (-160)
• Lake Bachar U1.5 walks (-260)
• Drew Anderson U4.5 K (-135)

Ticket B — One of each, +500

Model estimate: 58.4%
Implied estimate: 16.7%

• Michael Soroka U2.5 ER (-110)
• Logan Henderson U1.5 walks (-140)
• Hunter Dobbins O3.5 K (-120)

About those probability estimates

An 80% estimate on a three-leg ticket is not something I'm comfortable treating literally.

It comes from individual estimates of:

97%

96%

86%

Numbers that extreme can sometimes be driven by an expectation of limited workload rather than a dominant pitching performance.

So I'm treating these percentages the same way I've been treating the other very high estimates:

The direction is more useful than the exact percentage.

The magnitude is probably optimistic.

Blade Tidwell U2.5 ER

Highest probability on the board:

97%

Projection:

0.86 ER

Line:

U2.5

Recent results below the number:

5 of 5

10 of 10

Recent command:

69% first-pitch strikes

27% behind in the count

Both are better than league average.

But there's an important distinction in the projection.

A 0.86 earned run projection does not necessarily mean the model thinks Tidwell will dominate.

His recent efficiency is:

5.91 pitches per out

That's elevated.

So part of the under case is simply that he may not work deep enough into the game to allow three earned runs.

Same result.

Different mechanism.

Lake Bachar U1.5 walks

Projection:

0.46 walks

Recent results below 1.5:

5 of 5

10 of 10

The difficult part is the number attached to it:

-260

That's easily the shortest odds of the six legs.

He would need to hit at roughly 72% just to justify that number by itself.

That's why I'm using him inside a ticket rather than by himself.

His command numbers are also mixed.

First-pitch strikes:

65%

That's above league average.

But pitches ending behind in the count:

40%

That's considerably worse than league average.

So he often wins pitch one and still works himself into bad counts later.

For a walks under, that's a real concern.

Despite the 96% model estimate, this isn't the leg I feel strongest about.

Drew Anderson U4.5 K

Projection:

3.37 K

Line:

U4.5

Recent results below the number:

5 of 5

10 of 10

Previous results against Detroit work out to roughly:

1.33 strikeouts per game

His command numbers are almost exactly league average:

61% first-pitch strikes

34% behind in the count

Nothing extreme here.

The recent strikeout results are the main argument.

Hunter Dobbins O3.5 K

Projection:

4.78 K

Recent results above 3.5:

5 of 5

9 of 9

Every start in the current sample.

Previous meeting with the Cubs:

4 K

His command numbers are actually below league average:

58% first-pitch strikes

37% behind in the count

Normally that can shorten an outing.

But for this specific prop it can also mean more deep counts.

Deep counts create more strikeout opportunities.

So the same command issue that would worry me on an outs over isn't automatically a negative on a low strikeout over.

Michael Soroka U2.5 ER

Projection:

1.45 ER

Recent results below 2.5:

4 of 5

9 of 10

Previous meeting with Atlanta:

1 earned run

Recent command:

65% first-pitch strikes

29% behind in the count

He's also the most efficient starter among these six at:

4.99 pitches per out

The recent earned run results and command numbers are both pointing in the same direction.

Logan Henderson U1.5 walks

Projection:

0.89 walks

Recent results below the number:

4 of 5

8 of 10

Best recent command numbers of the six:

67% first-pitch strikes

25% behind in the count

League averages are roughly 61% and 34%.

There is one clear concern.

His previous meeting with the Dodgers:

3 walks

That's the only previous matchup result here, and it went directly against tonight's under.

I'm still using the leg because the 25% behind-in-count rate gives me much more information than one game.

But the prior meeting absolutely belongs in the discussion.

What I left out

Sean Burke U2.5 ER — 92%

Strong number, but Burke is the opposing starter to Drew Anderson.

I don't want two separate legs tied to the same game.

Edward Cabrera O1.5 ER — 87%

Same issue.

Cabrera is the opposing starter to Hunter Dobbins.

One position per game.

Blade Tidwell U1.5 walks — 93%

Second prop on a pitcher already being used.

One position per starter.

Cody Bradford U5.5 hits — 81%

There are only two prior starts in the data.

Both finished below this number, but two starts are not enough for me to put much weight on the trend.

I left this one out tonight because the sample is too small.

Game totals

Three games qualify tonight after two straight nights with none.

Matchup	Line	Pick	Odds	Derived	Difference
Rangers at Athletics	10.5	UNDER	-108	9.02	-1.48
Orioles at Rays	7.5	OVER	-110	8.33	+0.83
Royals at Angels	9.5	UNDER	-113	8.72	-0.78

I use a game total when my derived number differs from the listed line by at least:

0.75 runs

Rangers at Athletics U10.5

Derived:

9.02

Line:

10.5

Difference:

1.48 runs

That's one of the largest gaps I've seen since I started keeping these results.

The previous high was 1.68 runs on 8/13, and that game finished on the expected side.

Across fourteen games with usable numbers tonight:

Average residual: -0.207

Standard deviation: 0.519

Current game-total record:

6-4

Model improvements I'm working on

I'm also working on the next version of the hits, strikeout and outs projections.

The current models already use a wide mix of inputs.

That includes workload, command, recent performance, opponent tendencies, matchup history, pitch-level information and Statcast data — including expected slugging.

So this isn't about adding xSLG for the first time.

It is already part of the models.

What I'm trying to improve is how contact quality is estimated for the specific pitcher and lineup combination on that day.

I'm developing a separate modeled expected-slugging measure that can be used alongside the existing Statcast xSLG inputs.

The idea is to take information already being used — hitter quality, pitcher quality, handedness, pitch mix, pitch usage, contact tendencies and other matchup inputs — and generate a more matchup-specific expectation for the quality of contact that lineup should produce.

Then I can test whether that gives the models useful information beyond the existing Statcast expected-slugging data and the other inputs already included.

For hits allowed, the goal is a better estimate of whether expected contact should turn into baserunners rather than leaning too heavily on recent hit totals.

For strikeouts, it could help identify lineups that may handle a pitcher's specific arsenal better or worse than their overall strikeout numbers suggest.

For outs, it provides another way to estimate how much traffic and damage a starter may have to work through before pitch count becomes the limiting factor.

The important part is that this is an improvement to an already multi-feature model, not a replacement for what is there now.

Statcast expected slugging is already included.

I'm trying to determine whether a separately generated, matchup-specific expected-slugging signal improves the hits, strikeout and outs projections beyond what the current models already know.

I'm testing it first. If the numbers don't improve, it doesn't get added.

Notes

Six legs across a fifteen-game slate.

Each leg comes from a different game.

The two tickets each contain three different prop types. That's the main structural change from last night.

Nine days is still far too small a sample to draw firm conclusions.

Several other numbers looked interesting at first.

I narrowed it to six after reviewing the supporting data and the arguments against each one.

Six legs tonight.

Overall results

Date	Legs	Net
8/7	7-8	+6.18u
8/8	5-5	-7.10u
8/9	6-2	+8.58u
8/10	6-6	+2.72u
8/11	3-6	-1.83u
8/12	5-5	-4.33u
8/13	3-3	+0.88u
8/14	6-4	+3.85u
8/15	5-2	-0.02u
Total	46-41	+8.93u

46-41 on individual legs.

+8.93u overall.

Game totals: 6-4.

Nine days is still a very small sample.

Baseball has enormous night-to-night variance. The useful part is laying out the workload, recent results, command numbers and opponent history behind each leg, then coming back afterward and seeing what actually mattered.

Results tomorrow — wins and losses both.

Marlins ML

Rays ML

My model really likes the Padres ML and I can see why, but the Padres seem to inconsistent and not sure Mize is super reliable.

I also like the NYM/WSN Over 8 a good bit and the ARI/ARL O9.

I do like the SFG ML and the BAL/TBR o, but not as much.

I feel like the MIL/LAD U7.5 is sitting right tehre but those offenses are good and not sure I trust the pens. and then the TEX/ATH O10 is intriguing but those offenses...sheeeeesh

41-39 on legs. +8.95u through eight days.

Today's card: 7 legs, 2 tickets, 0 game totals.

Fifteen-game slate.

Date	Legs	Net
8/7	7-8	+6.18u
8/8	5-5	-7.10u
8/9	6-2	+8.58u
8/10	6-6	+2.72u
8/11	3-6	-1.83u
8/12	5-5	-4.33u
8/13	3-3	+0.88u
8/14	6-4	+3.85u
Total	41-39	+8.95u

What last night changed

Three of my four losses yesterday had a legitimate warning attached beforehand, and I kept the leg anyway.

Kirby — Houston was the most patient lineup on the slate. Patient lineups create deeper counts and more opportunities for walks.

He walked 3.

Cole — the Yankees' relief staff was short, which gave him more room to work deeper into the game. More innings means more opportunities for a second walk.

He went six innings and walked 2.

Holmes — the matchup history didn't support the strikeout over.

He finished with 3 K.

So today I'm handling those situations differently.

If the opposing case is strong enough to write about, it's strong enough to leave the leg off.

Four others looked good at first, but I left them out after a second review.

The board

Pitcher	Pick	Number	Proj	Prob	L5	L10	Strike%	FPS	Behind	Previous
Brad Lord	Outs U11.5	+106	8.69	91%	5/5	10/10	64.7%	75%	24%	8 outs
Troy Melton	ER U1.5	+125	0.78	81%	4/5	8/10	66.2%	68%	28%	1 ER
Eduardo Rodriguez	ER U3.5	-160	2.35	80%	5/5	8/10	65.8%	66%	31%	0 ER
Jacob Misiorowski	BB U1.5	-111	0.94	78%	3/5	8/10	68.1%	75%	20%	—
Ryan Gusto	ER U2.5	-130	1.88	76%	4/5	8/10	62.4%	59%	32%	0 ER
Sean Manaea	Hits O4.5	-160	5.55	72%	4/5	8/10	65.9%	66%	32%	7 hits
Matthew Boyd	ER U2.5	-160	1.88	70%	3/5	7/10	67.4%	72%	28%	0 ER

Proj = my projection.

FPS = first-pitch strike rate over the last five. League average is about 61%.

Behind = share of pitches ending with the pitcher behind in the count over the last five. League average is about 34%.

Previous = result from the previous meeting with tonight's opponent.

Ticket A — Earned run unders, +547

Model estimate: 49.3%
Implied estimate: 15.5%

• Eduardo Rodriguez U3.5 ER (-160)
• Troy Melton U1.5 ER (+125)
• Ryan Gusto U2.5 ER (-130)

Three different games.

All three have stayed below their number in 8 of their last 10.

All three also held tonight's opponent to one earned run or fewer in the previous meeting.

Ticket B — Workload and control, +536

Model estimate: 49.7%
Implied estimate: 15.7%

• Brad Lord U11.5 outs (+106)
• Jacob Misiorowski U1.5 walks (-111)
• Matthew Boyd U2.5 ER (-160)

Three different games.

About those model estimates

A roughly 3x difference between my estimate and the implied number on both tickets is larger than I'm comfortable treating literally.

My individual-leg probabilities have been running high.

Last night's Michael King O3.5 K was assigned 80.1% and did finish above the line — but only barely, with exactly 4 K.

So for now:

I trust the direction more than I trust the size of the percentage.

The probability numbers should be treated as relative strength, not as proven calibration.

Brad Lord U11.5 outs

This is the strongest number on today's board at 91%, and the underlying numbers are unusual.

Over his recent starts:

First-pitch strikes: 75%

Behind in the count: 24%

Both are much better than league average.

And yet he still needs:

7.43 pitches per out

That's the highest rate on today's board by a wide margin.

So he's getting ahead of hitters but still struggling to finish plate appearances efficiently.

That's exactly the type of combination that can create short outings.

Twelve outs at 7.43 pitches per out would require roughly:

89 pitches

My projection:

8.69 outs

Recent results below 11.5:

5 of 5

10 of 10

Previous meeting with the Mets:

8 outs

Troy Melton U1.5 ER

Number:

+125

Projection:

0.78 ER

Recent command:

68% first-pitch strikes

28% behind in the count

He's stayed below 1.5 earned runs in:

8 of his last 10

Previous meeting with Detroit:

1 earned run

Jacob Misiorowski U1.5 walks

Best control numbers on the board.

Over his recent starts:

75% first-pitch strikes

20% of pitches ending behind in the count

For comparison, league averages are roughly:

61% first-pitch strikes

34% behind

Projection:

0.94 walks

Line:

1.5

He's stayed below the number in:

8 of his last 10

Sean Manaea O4.5 hits — single

Projection:

5.55 hits

Previous meeting with Washington:

7 hits allowed

Recent results above 4.5:

4 of 5

8 of 10

I'm keeping Manaea separate from both tickets because he's the opposing starter to Brad Lord.

I don't want multiple parts of the night depending on the same game developing in the same way.

What I left off

Logan Webb U20.5 outs (+155)

Calculated probability:

72%

But the recent results don't support that number.

He's stayed below 20.5 in only:

1 of his last 5

and

5 of his last 10

That's four straight starts above the line.

When a 72% estimate is sitting next to a 1-of-5 recent result, I want more evidence before using it.

Left off.

Hayden Wesneski

Three different numbers initially qualified:

• K U4.5 — 80%
• BB O1.5 — 80%
• Outs O15.5 — 73%

The problem is that he has only three appearances in the relevant history.

Three-for-three isn't enough for me to call something a trend.

The walks number also doesn't fit cleanly with his control numbers.

He's around:

65% first-pitch strikes

Yet the projection is calling for roughly two walks.

That disagreement is enough for me to stay away.

Ian Seymour U17.5 outs

Calculated probability:

71%

Recent results below the line:

3 of 5

5 of 10

That's mediocre support for a number that high.

Left off.

Randy Dobnak U2.5 ER

Calculated probability:

87%

Second-highest number on today's board.

But there's almost no useful opponent history behind it.

Only two career plate appearances against this lineup and a thin overall sample.

I'm not giving an 87% estimate much weight when the evidence underneath it is that limited.

Game totals

None tonight.

Second straight night with no totals.

I only take a game total when my derived number differs from the listed line by at least:

0.75 runs

Largest gap tonight:

+0.65

Rockies at Giants O7.5.

Across the twelve games with usable numbers:

Average residual: -0.179

Standard deviation: 0.372

My totals and the listed numbers are simply too close tonight.

Matchup	Derived	Line	Difference
Rockies at Giants	8.15	7.5	+0.65
Marlins at Reds	8.91	9.5	-0.59
Nationals at Mets	7.92	8.5	-0.58
Royals at Angels	7.97	8.5	-0.53
Brewers at Dodgers	7.04	7.5	-0.46
Rangers at Athletics	9.06	9.5	-0.44
Cardinals at Cubs	8.70	9.0	-0.30

Game totals are currently:

6-4

Part of keeping that segment selective means doing nothing on nights when the numbers don't separate enough.

Tonight is one of those nights.

Notes

Seven legs across a fifteen-game slate.

I kept the list shorter on purpose.

Eight days is still a very small sample, so there is not enough history to draw firm conclusions.

Several other numbers looked interesting, but I left them out after reviewing the full data.

Seven legs tonight.

The shorter list is intentional.

Overall results

Date	Legs	Net
8/7	7-8	+6.18u
8/8	5-5	-7.10u
8/9	6-2	+8.58u
8/10	6-6	+2.72u
8/11	3-6	-1.83u
8/12	5-5	-4.33u
8/13	3-3	+0.88u
8/14	6-4	+3.85u
Total	41-39	+8.95u

41-39 on individual legs.

+8.95u overall.

Game totals: 6-4.

Eight days is still a very small sample.

Baseball has enormous night-to-night variance. The goal isn't to pretend every outcome can be predicted.

The useful part is laying out the workload, recent results, command numbers and opponent history behind each leg, then coming back afterward and seeing what actually mattered.

Results tomorrow — wins and losses both.

**New York Yankees** ML (-140) vs Toronto Blue Jays
Confidence: 80.7% (Grade: A)

**Los Angeles Angels** ML (+100) vs Kansas City Royals
Confidence: 67.3% (Grade: B)

**Tampa Bay Rays** ML (-154) vs Baltimore Orioles
Confidence: 66.5% (Grade: B)

**Atlanta Braves** ML (-184) vs Arizona Diamondbacks
Confidence: 63.4% (Grade: B)

**Cleveland Guardians** ML (-126) vs San Diego Padres
Confidence: 59.6% (Grade: B)

**Washington Nationals** ML (+106) vs New York Mets
Confidence: 58.9% (Grade: B)

**Miami Marlins** ML (+102) vs Cincinnati Reds
Confidence: 58.8% (Grade: B)

**Texas Rangers** ML (-106) vs Athletics
Confidence: 58.3% (Grade: B)

**San Francisco Giants** ML (-126) vs Colorado Rockies
Confidence: 58.3% (Grade: B)

**Detroit Tigers** ML (-126) vs Chicago White Sox
Confidence: 58.0% (Grade: B)

32-32 on legs. +4.22u through six days.

Today's card: 6 legs, 2 tickets, 3 game totals.

Nine-game slate. Deliberately smaller than yesterday.

Date	Legs	Net
8/7	7-8	+6.18u
8/8	5-5	-7.10u
8/9	6-2	+8.58u
8/10	6-6	+2.72u
8/11	3-6	-1.83u
8/12	5-5	-4.33u
Total	32-32	+4.22u

Six days is not a meaningful sample yet. Nearly all of the gain still traces back to two tickets on two nights.

Why the card shrank

I've gone 1-5 on tickets over the last two nights while going 3-6 and 5-5 on the individual legs.

Tuesday also put 18 units at risk across legs, tickets and totals — the most I've had on one card.

Baseball has a huge amount of randomness.

A pitcher can execute the plan you projected and give up four singles. Another can miss his spots all night and still strike out ten.

My job here isn't to pretend I can predict every bounce. It's to put the actual evidence for each pick in front of you:

• Workload arithmetic
• Matchup history
• Recent results
• Hits allowed
• Pitch efficiency
• Bullpen situation

That way you can judge the number on the evidence instead of taking it on faith.

Stacking three legs into one ticket multiplies the variance.

That's the argument for a shorter card rather than a longer one.

Three-leg tickets need something around a 55% individual-leg hit rate to justify the payout structure I'm using.

Six days is nowhere near enough to know whether I'm there.

So today:

2 tickets. 6 legs.

I'm leaving good-looking options off rather than reaching to fill a third ticket.

One rule changed again

I also want to be clear about the BvP adjustment I made Monday.

I gave batter-vs-pitcher history more weight because it correctly flagged two losses from the previous card.

Then Tuesday I built my core ticket entirely from legs where BvP and previous matchup results both agreed.

It went:

0-for-3.

Castillo was the loudest example.

He had a 16.1% matchup strikeout rate across 56 plate appearances supporting his U5.5 K.

He finished with:

10 K.

So after one night each way, I'm not treating BvP as a hard veto anymore.

It's weighted context, not an automatic decision.

The board

Pitcher	Pick	Price	Proj	Book	L5	L10	Time
Keider Montero	Outs U16.5	+107	15.35	16.82	2/5	3/10	1:10
Davis Martin	K U4.5	+106	4.75	4.74	2/5	2/10	2:10
Max Scherzer	K U4.5	-145	3.94	4.18	1/5	1/9	3:07
Braxton Ashcraft	Outs U17.5	+115	16.05	18.09	1/5	4/10	1:10
Aaron Nola	Hits O5.5	+105	5.86	5.39	4/5	7/10	7:30
Jacob deGrom	Hits O4.5	+105	4.72	4.45	4/5	6/10	10:07

Proj = my projection.
Book = the sportsbook's implied number after removing vig.

Keider Montero U16.5 outs

Cleanest number on the board for me.

Montero needs 17 outs to beat the under.

At his recent 4.96 pitches per out, 17 outs would require roughly:

84 pitches

His average over the last five:

74 pitches

That's a meaningful workload jump just to reach the other side of the number.

Detroit's bullpen is rested, so an earlier hook is available if he starts laboring.

His one previous meeting with Cleveland ended at:

15 outs.

Max Scherzer U4.5 K

He's cleared this number in only:

1 of his last 9 starts

The bigger thing for me is pitch efficiency.

Over his last five, Scherzer is averaging roughly:

6.9 pitches per out

That's easily the highest number on this board.

If you're burning that many pitches to record outs, you're simply getting fewer batters and fewer opportunities to accumulate strikeouts.

The ugly part is the price:

-145

Davis Martin U4.5 K

This is basically the plus-money version of the Scherzer idea.

Martin has stayed below the number in:

3 of his last 5

and

8 of his last 10

Cincinnati has also struck out at only:

10% across 20 plate appearances

against him.

That's a small matchup sample, so I'm not treating it as decisive.

But it does point in the same direction as the recent results.

Tickets

Two tickets.

Six pitchers.

No leg repeated.

A. Unders — +621

Model estimate: 21.4%
Implied estimate: 14.0%

• Keider Montero U16.5 outs (+107) — 1:10 ET
• Davis Martin U4.5 K (+106) — 2:10 ET
• Max Scherzer U4.5 K (-145) — 3:07 ET

All three are early games.

So this ticket will be decided before dinner.

B. Hits and workload — +804

Model estimate: 15.5%
Implied estimate: 9.5%

• Braxton Ashcraft U17.5 outs (+115) — 1:10 ET
• Aaron Nola O5.5 hits (+105) — 7:30 ET
• Jacob deGrom O4.5 hits (+105) — 10:07 ET

The two hits legs are based on hits allowed, not workload.

That's the evidence I want for this type of line.

Aaron Nola O5.5 hits

Hits allowed in his last ten:

7, 3, 8, 7, 3, 6, 7, 5, 6, 6

He's cleared 5.5 in:

7 of 10

and

4 of 5

Season average:

5.75 hits allowed

That's already above the line.

He's also allowing a hit to roughly:

24.7% of the batters he faces

Jacob deGrom O4.5 hits

He's cleared this number in:

4 of his last 5

and

6 of his last 10

The trend has also moved upward:

Season: 4.73

Last 10: 5.2

Last 5: 5.8

His two previous meetings with the Angels resulted in:

6 hits and 5 hits.

Braxton Ashcraft U17.5 outs

This one is a workload argument.

Ashcraft needs 18 outs to beat the under.

At his recent 5.78 pitches per out, that would require about:

104 pitches

His average over the last five is:

83 pitches

That's roughly a 25% workload increase just to reach 18 outs — the biggest workload gap on my board.

He's reached 18 outs only once in his last five.

His previous meeting with Miami ended at:

15 outs.

There is one important counterpoint:

Pittsburgh's bullpen is short on available arms, which can buy a starter a longer leash.

I'm still keeping the under because 104 pitches is a difficult workload requirement regardless of who is available behind him.

What I left off

Max Fried U17.5 outs (+155)

This had the highest calculated edge on my board:

+15.4%

But there are two strong reasons against it.

His previous meeting with Seattle went:

21 outs

And the Yankees' bullpen is taxed with two arms unavailable.

That creates exactly the longer leash I'd rather not see when taking an outs under.

Highest calculated edge on the page.

Not on the card.

Logan Gilbert O17.5 outs

His previous meeting went:

16 outs

The Yankees have also hit:

.277 across 44 plate appearances

against him.

Left it alone.

Payton Tolle O5.5 K

Recent form is strong:

5 of 5

8 of 10

But Toronto has a 17% whiff rate across 29 plate appearances against him.

His two previous meetings produced:

4 K and 6 K

Not enough agreement for me to include it.

Cade Cavalli and Andrew Abbott

Both came back calibrator-saturated.

In other words, the calculated probability is sitting on a known model ceiling rather than giving me a useful read of the matchup.

Neither made the card.

Weather

Nothing decisive today.

Miami

Retractable roof.

Expected to be closed.

Toronto

Retractable roof.

Expected to be closed.

Seattle

The only notable wind reading is around:

12.5 mph gusts

That's where Fried and Gilbert are pitching.

I left both of those legs off anyway.

Game totals

Matchup	Line	Pick	Price	Time
Pirates at Marlins	8.0	OVER	-104	1:10
Reds at White Sox	8.5	OVER	+105	2:10
Brewers at Dodgers	8.5	OVER	+107	10:10

Three of the nine games cleared my threshold.

Strongest total: Pirates at Marlins O8.0

Model edge:

+1.68 runs

That's the largest totals edge the model has produced since I started tracking these.

Reds at White Sox is here for the third straight day

And I've been wrong on it twice.

Monday:

U7.5

Final:

9

Tuesday:

O9.0

Final:

5

Today:

O8.5

I'm including it because that's what the current inputs produce.

I'm not going to manually override the output because the previous two games went against me.

But the history deserves to be shown:

0-2 on this matchup this week.

Lifetime game-total record:

3-4

Caveats

• Six legs and two tickets on a nine-game slate is the smallest card I've posted. That's intentional.
• Every leg is from a different game.
• Prices are cross-book aggregates and may be shorter at one individual sportsbook.
• Six days is nowhere near enough data to establish a meaningful track record.

One leg that got left off because of the no-overlap structure was Parker Messick O5.5 K.

He cleared my edge filter at +7.0%, has reached the number in 4 of 5 and 6 of 10, and recorded 6 K in the previous meeting.

But he's the opposing starter to Montero.

I don't want two separate tickets depending on the same game developing into a low-scoring pitchers' duel.

So Messick stayed off.

Overall results

Date	Legs	Net
8/7	7-8	+6.18u
8/8	5-5	-7.10u
8/9	6-2	+8.58u
8/10	6-6	+2.72u
8/11	3-6	-1.83u
8/12	5-5	-4.33u
Total	32-32	+4.22u

32-32 on individual legs. +4.22u overall.

Nearly all of that gain traces back to two tickets on two nights.

Six days isn't a track record in a sport this noisy.

That's why I publish the reasoning behind every pick — so each one can be judged on its evidence rather than on the running total.

Results tomorrow — wins and losses both.

https://preview.redd.it/f41uq2kig6jh1.png?width=494&format=png&auto=webp&s=d0b6f8b150a41f601e8409a17e374b0c9ab0a1f7

**Texas Rangers** ML (-128) vs Los Angeles Angels
Confidence: 82.1% (Grade: A)

**Atlanta Braves** ML (-174) vs New York Mets
Confidence: 75.8% (Grade: A)

**Chicago White Sox** ML (-141) vs Cincinnati Reds
Confidence: 72.9% (Grade: A)

**Los Angeles Dodgers** ML (-207) vs Kansas City Royals
Confidence: 69.6% (Grade: A)

**New York Yankees** ML (-124) vs Seattle Mariners
Confidence: 68.8% (Grade: A)

**Chicago Cubs** ML (-158) vs Washington Nationals
Confidence: 67.8% (Grade: B)

**Milwaukee Brewers** ML (-120) vs San Diego Padres
Confidence: 65.2% (Grade: B)

**Detroit Tigers** ML (-123) vs Cleveland Guardians
Confidence: 61.0% (Grade: B)

**Miami Marlins** ML (-106) vs Pittsburgh Pirates
Confidence: 59.8% (Grade: B)