Settled model recap · 2026-09-25

MLB Pitcher Prop Picks Recap for September 25, 2026: 3-4, -0.93 units

Review 7 pitcher-prop Picks from September 25, 2026 with the exact saved sportsbook prices, model probabilities, lineup or player values, final results, and plain-English postgame review.

Published after the saved Picks settled. The original prices and inputs stay attached.

Record3-4-0

Net units-0.93u

Game Picks0

Player Props7

One-minute recap

The card missed: 7 pitcher-prop Picks finished 3-4 for -0.93 units. The useful question is which assumptions failed, not how to explain the result after the fact.

Decision coverage note

Decision coverage was incomplete in the pitcher strikeout lane (8 opportunities expired despite a prior decision opportunity) and the additional pitcher props lane (48 opportunities expired without a decision). This recap is complete for the 7 settled Picks that were actually recorded, but the slate does not meet full-lifecycle acceptance. No missing decision was reconstructed, and these incidents do not count as Picks or change the record above.

Saved Picks and reviews

Open any Pick for the why

MLB Player Props

7 pitcher-prop Picks, covering strikeouts and reviewed from the saved workload, opponent, and price snapshots.

Pitcher prop

Clay Holmes Over 3.5 strikeouts

CHC at BOS · 3 strikeouts · over 3.5

Betonlineag -149lossSee the review ↓

Postgame review

Lost: Clay Holmes Over 3.5 Ks

Clay Holmes finished with 3 strikeouts, leaving the saved over 3.5 on the wrong side of the line for a 1.00-unit loss.

At Betonlineag -149, the model gave the over 60.7% against a 59.8% break-even mark, a +0.9-point gap. The saved projection was 4.7 strikeouts across 23.4 batters faced. The pitcher graded 51.3/100 and the opponent strikeout matchup graded 39.0; the confirmed lineup carried a 20.8% strikeout rate.

The actual result finished 1.7 strikeouts below the point projection. The projection supported the ticket, but the actual result crossed to the other side of the sportsbook line. We will compare the same workload, opponent, line, and price profile before changing production weights.

Saved pregame inputs

Model probability60.7%

Price break-even59.8%

Calculated difference+0.9 pts

Frozen pitcher-prop inputs

Clay Holmes vs. BOS

Projected Ks: 4.7
Actual Ks: 3

Pitcher value51.3

Opponent K matchup39.0

Lineup K rate20.8%

Lineup coverage100%

Projected batters faced23.4

Evidence quality88%

Pitcher and opponent values use the frozen 0–100 rolling scale. Projection standard deviation: 2.33 Ks. Robustness: 75%.

Pitcher prop

Cristopher Sanchez Under 5.5 strikeouts

TB at PHI · 10 strikeouts · under 5.5

DraftKings +122lossSee the review ↓

Postgame review

Lost: Cristopher Sanchez Under 5.5 Ks

Cristopher Sanchez finished with 10 strikeouts, leaving the saved under 5.5 on the wrong side of the line for a 1.00-unit loss.

At DraftKings +122, the model gave the under 47.2% against a 45.0% break-even mark, a +2.2-point gap. The saved projection was 5.7 strikeouts across 25.8 batters faced. The pitcher graded 57.7/100 and the opponent strikeout matchup graded 47.6; the confirmed lineup carried a 18.8% strikeout rate.

The actual result finished 4.3 strikeouts above the point projection. The point projection sat on the other side of the line, and the ticket lost. We will compare the same workload, opponent, line, and price profile before changing production weights.

Saved pregame inputs

Model probability47.2%

Price break-even45.0%

Calculated difference+2.2 pts

Frozen pitcher-prop inputs

Cristopher Sanchez vs. TB

Projected Ks: 5.7
Actual Ks: 10

Pitcher value57.7

Opponent K matchup47.6

Lineup K rate18.8%

Lineup coverage100%

Projected batters faced25.8

Evidence quality93%

Pitcher and opponent values use the frozen 0–100 rolling scale. Projection standard deviation: 2.58 Ks. Robustness: 81%.

Pitcher prop

Michael McGreevy Over 3.5 strikeouts

STL at MIL · 2 strikeouts · over 3.5

DraftKings -115lossSee the review ↓

Postgame review

Lost: Michael McGreevy Over 3.5 Ks

Michael McGreevy finished with 2 strikeouts, leaving the saved over 3.5 on the wrong side of the line for a 1.00-unit loss.

At DraftKings -115, the model gave the over 56.1% against a 53.5% break-even mark, a +2.7-point gap. The saved projection was 4.6 strikeouts across 23.1 batters faced. The pitcher graded 49.5/100 and the opponent strikeout matchup graded 38.0; the confirmed lineup carried a 23.0% strikeout rate.

The actual result finished 2.6 strikeouts below the point projection. The projection supported the ticket, but the actual result crossed to the other side of the sportsbook line. We will compare the same workload, opponent, line, and price profile before changing production weights.

Saved pregame inputs

Model probability56.1%

Price break-even53.5%

Calculated difference+2.7 pts

Frozen pitcher-prop inputs

Michael McGreevy vs. MIL

Projected Ks: 4.6
Actual Ks: 2

Pitcher value49.5

Opponent K matchup38.0

Lineup K rate23.0%

Lineup coverage100%

Projected batters faced23.1

Evidence quality92%

Pitcher and opponent values use the frozen 0–100 rolling scale. Projection standard deviation: 2.30 Ks. Robustness: 80%.

Pitcher prop

Tomoyuki Sugano Over 3.5 strikeouts

COL at CWS · 5 strikeouts · over 3.5

Betonlineag -114winSee the review ↓

Postgame review

Won: Tomoyuki Sugano Over 3.5 Ks

Tomoyuki Sugano finished with 5 strikeouts, cashing the saved over 3.5 and adding +0.88 units to the prop record.

At Betonlineag -114, the model gave the over 55.5% against a 53.3% break-even mark, a +2.3-point gap. The saved projection was 4.5 strikeouts across 22.7 batters faced. The pitcher graded 41.6/100 and the opponent strikeout matchup graded 37.4; the confirmed lineup carried a 24.6% strikeout rate.

The actual result finished 0.5 strikeouts above the point projection. The ticket and the point projection told the same story. We will compare the same workload, opponent, line, and price profile before changing production weights.

Saved pregame inputs

Model probability55.5%

Price break-even53.3%

Calculated difference+2.3 pts

Frozen pitcher-prop inputs

Tomoyuki Sugano vs. CWS

Projected Ks: 4.5
Actual Ks: 5

Pitcher value41.6

Opponent K matchup37.4

Lineup K rate24.6%

Lineup coverage100%

Projected batters faced22.7

Evidence quality92%

Pitcher and opponent values use the frozen 0–100 rolling scale. Projection standard deviation: 2.27 Ks. Robustness: 79%.

Pitcher prop

Gavin Williams Under 6.5 strikeouts

CLE at KC · 4 strikeouts · under 6.5

DraftKings +111winSee the review ↓

Postgame review

Won: Gavin Williams Under 6.5 Ks

Gavin Williams finished with 4 strikeouts, cashing the saved under 6.5 and adding +1.11 units to the prop record.

At DraftKings +111, the model gave the under 51.4% against a 47.4% break-even mark, a +4.0-point gap. The saved projection was 6.1 strikeouts across 23.6 batters faced. The pitcher graded 58.5/100 and the opponent strikeout matchup graded 66.4; the confirmed lineup carried a 21.2% strikeout rate.

The actual result finished 2.1 strikeouts below the point projection. The ticket and the point projection told the same story. We will compare the same workload, opponent, line, and price profile before changing production weights.

Saved pregame inputs

Model probability51.4%

Price break-even47.4%

Calculated difference+4.0 pts

Frozen pitcher-prop inputs

Gavin Williams vs. KC

Projected Ks: 6.1
Actual Ks: 4

Pitcher value58.5

Opponent K matchup66.4

Lineup K rate21.2%

Lineup coverage100%

Projected batters faced23.6

Evidence quality92%

Pitcher and opponent values use the frozen 0–100 rolling scale. Projection standard deviation: 2.68 Ks. Robustness: 80%.

Pitcher prop

Hunter Brown Under 5.5 strikeouts

HOU at OAK · 4 strikeouts · under 5.5

DraftKings +108winSee the review ↓

Postgame review

Won: Hunter Brown Under 5.5 Ks

Hunter Brown finished with 4 strikeouts, cashing the saved under 5.5 and adding +1.08 units to the prop record.

At DraftKings +108, the model gave the under 50.2% against a 48.1% break-even mark, a +2.1-point gap. The saved projection was 5.4 strikeouts across 22.8 batters faced. The pitcher graded 55.6/100 and the opponent strikeout matchup graded 55.5; the confirmed lineup carried a 21.3% strikeout rate.

The actual result finished 1.4 strikeouts below the point projection. The ticket and the point projection told the same story. We will compare the same workload, opponent, line, and price profile before changing production weights.

Saved pregame inputs

Model probability50.2%

Price break-even48.1%

Calculated difference+2.1 pts

Frozen pitcher-prop inputs

Hunter Brown vs. OAK

Projected Ks: 5.4
Actual Ks: 4

Pitcher value55.6

Opponent K matchup55.5

Lineup K rate21.3%

Lineup coverage100%

Projected batters faced22.8

Evidence quality90%

Pitcher and opponent values use the frozen 0–100 rolling scale. Projection standard deviation: 2.50 Ks. Robustness: 77%.

Pitcher prop

Reid Detmers Under 6.5 strikeouts

LAA at SEA · 7 strikeouts · under 6.5

BetMGM -110lossSee the review ↓

Postgame review

Lost: Reid Detmers Under 6.5 Ks

Reid Detmers finished with 7 strikeouts, leaving the saved under 6.5 on the wrong side of the line for a 1.00-unit loss.

At BetMGM -110, the model gave the under 56.7% against a 52.4% break-even mark, a +4.3-point gap. The saved projection was 5.5 strikeouts across 23.2 batters faced. The pitcher graded 59.6/100 and the opponent strikeout matchup graded 55.5; the confirmed lineup carried a 20.5% strikeout rate.

The actual result finished 1.5 strikeouts above the point projection. The projection supported the ticket, but the actual result crossed to the other side of the sportsbook line. We will compare the same workload, opponent, line, and price profile before changing production weights.

Saved pregame inputs

Model probability56.7%

Price break-even52.4%

Calculated difference+4.3 pts

Frozen pitcher-prop inputs

Reid Detmers vs. SEA

Projected Ks: 5.5
Actual Ks: 7

Pitcher value59.6

Opponent K matchup55.5

Lineup K rate20.5%

Lineup coverage100%

Projected batters faced23.2

Evidence quality92%

Pitcher and opponent values use the frozen 0–100 rolling scale. Projection standard deviation: 2.53 Ks. Robustness: 80%.

Clay Holmes: What had to hold

Strikeout count: the saved expectation was 2.40813 to 7.0738 strikeouts. The pregame model centered on 4.7 strikeouts, with uncertainty around that estimate. The case becomes weaker if: the actual count falls outside the saved descriptive range; this alone does not diagnose the cause.

This range was frozen before the game as a descriptive check, not a calibrated confidence interval.

Clay Holmes: What actually happened

The saved decision settled as a loss, with -1.00 units on 1 units of reference risk.

Strikeout count finished at 3 strikeouts, inside the saved 2.40813 to 7.0738 range.

Clay Holmes: What enters model review

These are checks of the original assumptions, not proof of what caused the result. A win does not validate every assumption, and a loss does not invalidate every forecast.

The measured observations can enter a source-verified review dataset. This recap changes no model weights; any proposed change requires separate chronological and prospective evaluation.

Cristopher Sanchez: What had to hold

Strikeout count: the saved expectation was 3.08779 to 8.25655 strikeouts. The pregame model centered on 5.7 strikeouts, with uncertainty around that estimate. The case becomes weaker if: the actual count falls outside the saved descriptive range; this alone does not diagnose the cause.

This range was frozen before the game as a descriptive check, not a calibrated confidence interval.

Cristopher Sanchez: What actually happened

The saved decision settled as a loss, with -1.00 units on 1 units of reference risk.

Strikeout count finished at 10 strikeouts, outside the saved 3.08779 to 8.25655 range.

Cristopher Sanchez: What enters model review

These are checks of the original assumptions, not proof of what caused the result. A win does not validate every assumption, and a loss does not invalidate every forecast.

The measured observations can enter a source-verified review dataset. This recap changes no model weights; any proposed change requires separate chronological and prospective evaluation.

Michael McGreevy: What had to hold

Strikeout count: the saved expectation was 2.3344 to 6.93488 strikeouts. The pregame model centered on 4.6 strikeouts, with uncertainty around that estimate. The case becomes weaker if: the actual count falls outside the saved descriptive range; this alone does not diagnose the cause.

This range was frozen before the game as a descriptive check, not a calibrated confidence interval.

Michael McGreevy: What actually happened

The saved decision settled as a loss, with -1.00 units on 1 units of reference risk.

Strikeout count finished at 2 strikeouts, outside the saved 2.3344 to 6.93488 range.

Michael McGreevy: What enters model review

These are checks of the original assumptions, not proof of what caused the result. A win does not validate every assumption, and a loss does not invalidate every forecast.

The measured observations can enter a source-verified review dataset. This recap changes no model weights; any proposed change requires separate chronological and prospective evaluation.

Tomoyuki Sugano: What had to hold

Strikeout count: the saved expectation was 2.26745 to 6.81655 strikeouts. The pregame model centered on 4.5 strikeouts, with uncertainty around that estimate. The case becomes weaker if: the actual count falls outside the saved descriptive range; this alone does not diagnose the cause.

This range was frozen before the game as a descriptive check, not a calibrated confidence interval.

Tomoyuki Sugano: What actually happened

The saved decision settled as a win, with +0.88 units on 1 units of reference risk.

Strikeout count finished at 5 strikeouts, inside the saved 2.26745 to 6.81655 range.

Tomoyuki Sugano: What enters model review

These are checks of the original assumptions, not proof of what caused the result. A win does not validate every assumption, and a loss does not invalidate every forecast.

The measured observations can enter a source-verified review dataset. This recap changes no model weights; any proposed change requires separate chronological and prospective evaluation.

Gavin Williams: What had to hold

Strikeout count: the saved expectation was 3.39598 to 8.7605 strikeouts. The pregame model centered on 6.1 strikeouts, with uncertainty around that estimate. The case becomes weaker if: the actual count falls outside the saved descriptive range; this alone does not diagnose the cause.

This range was frozen before the game as a descriptive check, not a calibrated confidence interval.

Gavin Williams: What actually happened

The saved decision settled as a win, with +1.11 units on 1 units of reference risk.

Strikeout count finished at 4 strikeouts, inside the saved 3.39598 to 8.7605 range.

Gavin Williams: What enters model review

These are checks of the original assumptions, not proof of what caused the result. A win does not validate every assumption, and a loss does not invalidate every forecast.

The measured observations can enter a source-verified review dataset. This recap changes no model weights; any proposed change requires separate chronological and prospective evaluation.

Hunter Brown: What had to hold

Strikeout count: the saved expectation was 2.86999 to 7.8686 strikeouts. The pregame model centered on 5.4 strikeouts, with uncertainty around that estimate. The case becomes weaker if: the actual count falls outside the saved descriptive range; this alone does not diagnose the cause.

This range was frozen before the game as a descriptive check, not a calibrated confidence interval.

Hunter Brown: What actually happened

The saved decision settled as a win, with +1.08 units on 1 units of reference risk.

Strikeout count finished at 4 strikeouts, inside the saved 2.86999 to 7.8686 range.

Hunter Brown: What enters model review

These are checks of the original assumptions, not proof of what caused the result. A win does not validate every assumption, and a loss does not invalidate every forecast.

The measured observations can enter a source-verified review dataset. This recap changes no model weights; any proposed change requires separate chronological and prospective evaluation.

Reid Detmers: What had to hold

Strikeout count: the saved expectation was 2.94739 to 7.99817 strikeouts. The pregame model centered on 5.5 strikeouts, with uncertainty around that estimate. The case becomes weaker if: the actual count falls outside the saved descriptive range; this alone does not diagnose the cause.

This range was frozen before the game as a descriptive check, not a calibrated confidence interval.

Reid Detmers: What actually happened

The saved decision settled as a loss, with -1.00 units on 1 units of reference risk.

Strikeout count finished at 7 strikeouts, inside the saved 2.94739 to 7.99817 range.

Reid Detmers: What enters model review

These are checks of the original assumptions, not proof of what caused the result. A win does not validate every assumption, and a loss does not invalidate every forecast.

The measured observations can enter a source-verified review dataset. This recap changes no model weights; any proposed change requires separate chronological and prospective evaluation.

What comes next

What we take forward

Each result can raise a useful question, but no single game rewrites the model. Any adjustment has to repeat across comparable frozen reads and survive a prospective test first.

Technical receipt

Fact pack: 54eb126e8a01926c34af84513310b66b4dbc54f5c80f2f84bcc6fc8f6dfb22a2

Decision receipts: mlb-k-prop-c7c401c913a6ea16cc6314df, mlb-k-prop-fc56ff79698f4ad609e1df40, mlb-k-prop-3cc22a4b9a6bab9db080081e, mlb-k-prop-91fb570eb1a7587277971383, mlb-k-prop-949e202f6a7082e43eda4178, mlb-k-prop-c4cc1d11780a6f2afe9fab21, mlb-k-prop-f60070ac719890391a87f707

Settlement receipts: 7173ed2c85a0eaf22d4fd21694b522d9cb050cbcf56e8935c75135ed28447a50, 11776e69933b72a587edc743a2c1555e88688f5b8fa2f75cd4193d8c45db6c9b, 62b12b9c0fe0bf503d4d4f6ba4639d5f388fffb9719f295669d857a59e775d46, efd03bd20b2a53d9dc731ca7a2cfc506310adaf608dc0f5132b5afbff88e04f8, 5d47a4eaa718212f56f18d1809439924ed512eefdf95fe776824cf8e78c1c91d, ba8d608da5147e9f74bc23d13f1e7b12ae21895ed33f48fb3e734ab9e90523c1, eb46ad26af1ca108e4ee62bddf39335bea91f4ba2c59506e813191cf45ff8806