MLB Manager Trends

MLB Manager Trend #001

The New York Mets are 74-41-7 (+1.81 rpg, 64.3%) OVER the total for +28.65 units and +21.3% roi as +100 to +150 road underdogs under Manager Terry Collins. Today the Mets square off against the Chicago Cubs in the Windy City starting Dillon Gee over Travis Wood for +143 on the Money Line and 8.5 as the lined total.

MLB Manager Trend #002

The Oakland Athletics are 28-4 (87.5%, +22.41 units, +49.1% roi) SU and +58% roi RL as -130 to -175 home favorites facing plus.500 teams under Bob Melvin.

MLB Manager Trend #003

Under Terry Collins, the Mets are 42-29 (59.2%, +24.3 units, +32.6% roi) SU on the road off of a 1 or 2 run loss.

MLB Manager Trend #004

The New York Mets are 52-49 +14.35 units SU as 150 or less road underdogs under Manager Terry Collins; a so-so stat, yet a profit nonetheless.

MLB Manager Trend #005

Under Don Mattingly, the Dodgers are 40-17 70.2%, +19.92 units after losing to the current opponent as road favorites.

The latest MLB manager trends continue to show why coaching context should be treated as a supporting market filter rather than a standalone prediction tool. These angles are not saying a manager “causes” a result by himself. They are identifying repeatable team environments where managerial identity, series position, bullpen usage, starter context, rest, timing, and opponent profile have historically lined up with profitable betting results.

The current research board includes moneyline, run line, Over/Under, play-on, and play-against angles. Several of the strongest trends come from manager-specific totals patterns, especially around Craig Albernaz, Matt Quatraro, Stephen Vogt, David Bell, Dave Roberts, and Skip Schumaker. The uploaded trend sheet includes records, ROI, profit, P-values, descriptions, and SDQL for each angle.

MLB Manager Trends Summary Table

Manager / Angle	Market	Play	Record	ROI	Profit	P-Value
Pat Murphy road teams after 5+ starter strikeouts	ML	ON	39-14 / 73.6%	44.1%	+2751	0.000401
Dave Roberts teams after allowing 3+ runs	OU	OVER	173-112 / 60.7%	16.0%	+5234	0.000181
Craig Albernaz teams after allowing 3+ runs	OU	OVER	22-4 / 84.6%	61.8%	+1770	0.000267
Stephen Vogt early-season teams	ML	ON	27-7 / 79.4%	57.5%	+2256	0.000410
Craig Albernaz vs teams winning ≤56.25%	OU	OVER	15-2 / 88.2%	67.7%	+1275	0.001175
Craig Albernaz teams with rest and starter context	OU	OVER	16-3 / 84.2%	60.1%	+1260	0.002213
Fade Craig Albernaz opponent setup	OU	OVER	17-3 / 85.0%	61.9%	+1360	0.001288
Craig Albernaz early-series bullpen setup	OU	OVER	17-3 / 85.0%	60.9%	+1355	0.001288
Fade Torey Lovullo opponent setup	ML	AGST	25-9 / 73.5%	36.5%	+1689	0.004510
Stephen Vogt road teams vs opponent starter wins	OU	UNDER	91-55 / 62.3%	18.3%	+3126	0.001816
Fade Craig Albernaz late-series setup	OU	OVER	10-1 / 90.9%	75.9%	+900	0.005859
Fade Aaron Boone in May starter-context setup	RL	AGST	40-21 / 65.6%	32.5%	+2279	0.010214
Carlos Mendoza teams with rest	OU	OVER	31-13 / 70.5%	32.1%	+1682	0.004784
Skip Schumaker start-time bullpen setup	OU	OVER	131-83 / 61.2%	16.2%	+3926	0.000631
Stephen Vogt late-series starter-strike setup	OU	UNDER	62-32 / 66.0%	24.1%	+2654	0.001297
Skip Schumaker home favorite setup	OU	OVER	132-89 / 59.7%	13.5%	+3445	0.002310
Fade Matt Quatraro late-series Central-time setup	OU	UNDER	105-67 / 61.0%	16.2%	+3146	0.002325
Matt Quatraro early-season teams	OU	UNDER	62-35 / 63.9%	21.6%	+2375	0.003991
Stephen Vogt regular-season run-line-margin setup	OU	UNDER	105-70 / 60.0%	14.3%	+2853	0.004989
Craig Counsell May win setup	RL	AGST	31-12 / 72.1%	23.1%	+1345	0.002703
David Bell early start-time setup	OU	UNDER	77-49 / 61.1%	16.0%	+2298	0.007926
Matt Quatraro since 2024 series setup	OU	UNDER	185-121 / 60.5%	14.9%	+5148	0.000151
David Bell since 2024 starter-batters-faced setup	OU	UNDER	79-42 / 65.3%	24.0%	+3308	0.000493
Matt Quatraro since 2024 off a loss	OU	UNDER	81-45 / 64.3%	22.3%	+3143	0.000857
John Schneider since 2024 offensive-context setup	OU	OVER	118-70 / 62.8%	19.6%	+4199	0.000286
Matt Quatraro since 2024 high-total setup	ML	ON	37-14 / 72.5%	36.2%	+2300	0.000885
Matt Quatraro pre-All-Star setup	OU	UNDER	159-113 / 58.5%	11.2%	+3413	0.003132
David Bell regular-season times-tied setup	OU	UNDER	202-156 / 56.4%	7.7%	+3149	0.008641
Matt Quatraro since 2024 AL opponent setup	OU	UNDER	127-80 / 61.4%	16.9%	+3956	0.000666
Matt Quatraro since 2024 series-game setup	OU	UNDER	144-96 / 60.0%	14.0%	+3773	0.001174
David Bell since 2024 starter-strikes setup	OU	UNDER	112-77 / 59.3%	12.9%	+2802	0.006601

What These MLB Manager Trends Actually Show

The most important takeaway is not that one manager is automatically profitable to bet on or against. The value comes from the combination of managerial environment and game-state filters.

For example:

• Several Craig Albernaz angles lean strongly toward the Over, especially when paired with opponent, series, and starter-context filters.
• Several Matt Quatraro angles lean toward the Under, particularly since 2024 and in series-based conditions.
• Stephen Vogt shows both moneyline and Under signals depending on the market and setup.
• David Bell appears repeatedly in Under systems tied to start time, series structure, and starter workload.
• Skip Schumaker appears in multiple Over systems tied to timing, home/road context, and prior line conditions.

This is why MLB manager trends should be evaluated as part of a larger research process. A manager label alone is too broad. The edge usually appears when the manager variable is combined with starter workload, bullpen usage, series game number, rest, opponent strength, and market pricing.

MLB Manager Trends and Statistical Significance

The research board also includes P-values, which help separate random-looking hot streaks from stronger historical patterns. A lower P-value does not guarantee future profit, but it does indicate that the historical result is less likely to be explained by chance alone.

A simple way to think about this:$$\text{Trend Strength} = \text{Record} + \text{ROI} + \text{Sample Size} + \text{P-Value Context}$$Trend Strength=Record+ROI+Sample Size+P-Value Context

A 10-1 trend with a massive ROI may be interesting, but it still carries small-sample risk. A 185-121 trend with a lower ROI may be more stable because the sample size is larger. The strongest research candidates are usually the ones that balance profitability, sample size, logical explanation, and statistical support.

How to Use These MLB Manager Trends

These trends are best used as research indicators, not blind betting commands.

A disciplined process would look like this:

1. Identify the active manager trend.
2. Check the SDQL logic behind the trend.
3. Confirm the sample size, ROI, profit, and P-value.
4. Compare the trend to the current market price.
5. Check whether the line has already moved.
6. Use the trend as one input inside a broader betting model.

That final point matters. MLB manager trends can reveal hidden team tendencies, but the market still determines whether value exists. A profitable historical system can become unplayable if the current number is already gone.

How This Fits Into the Market

MLB manager trends are one part of a broader market-based betting process. To understand how these angles fit into a larger framework, see our guide to sports betting market mechanics, our breakdown of public bias and market distortion, and our explanation of what sports betting systems actually measure.

Process & Proof

For long-term context, these systems should be evaluated alongside documented betting results and the daily Raw Numbers process.