We Fact Checked the NY Post’s MLB Betting Systems

I found this gem while scouring the interwebs for useful sports betting info. It’s NY Post MLB betting advice from July, 2021. Let’s see if it is useful as we enter August of 2022.

I’m not sure what to expect. The NY Post is hardly a sports betting advice paper so the fact I found 12 “MLB betting systems” is pretty awesome. They are very surface level to be sure, but remember – more complicated doesn’t always mean better.

Let’s put these systems through the ringer and see 1) if they ever were profitable and 2) if they remain profitable today.

The author is very up front that these systems were red hot when he wrote this article – which was in July of 2021. We need to look at these through our expert lenses – keeping 2 main criteria in mind: The system needs to churn consistent profits AND the system needs to have a logical explanation.

For example, fade East Coast road underdogs traveling out West in Game 1 of the series following a loss in which the bullpen tossed at least 5 innings.

Now I just made this up, but assume we had the data for this “system” and it was consistently in the black. Can we develop a logical explanation for why this may be? Of course we can! This makes perfect sense.

After getting beaten around and depleting their pen, a weary team must now hop on a plane and endure jet lag while going to battle against a superior opponent. I can get behind that.

Beware of systems with no logic behind them like always betting home teams on Wednesday day games following a Tuesday game in which 5 home runs were hit. I mean, sure, maybe there is some consistency over the past couple of years, but how can you justify that this is a concrete trend rather than a fluke? You can’t!

With this in mind, let’s analyze the first 9 of these 12 MLB betting systems from the New York Post and see if we can unearth something profitable. Why only the first 9? 9 is a baseball number … but I also have a real reason.

Research Tools for Finding MLB Betting Systems and Data

For this review I’ll be using Killersports and nothing but Killersports. Their SDQL feature for MLB is one of the best sports betting research tools I’ve ever used – even rivaling the paid sites (yes, Killersports is free).

One area in which Killersports does not deliver is bullpen usage data. The final 3 systems laid out by the NY Post reference bullpen data. Since we can’t get this, I’ve made the executive decision to analyze only the first 9 systems.

MLB Home Teams Betting Systems

Can we justify why this system works? I think so. The author from the Post puts it well. He states, “Hitting … [is] contagious. Factor in the absence of motivation that would come with facing a division opponent and it seems these offensive droughts can carry over from game to game, even for a team playing at home” (Steve Mackinen, 2021).

 

Let’s take a peek at how this system has performed over the past 10 seasons – excluding 2020 because that year sucked. Keep in mind the original author was only looking at trends present in the first half of 2021, but we shouldn’t be taking stock in a trend that has only existed for a few months. We need a few season’s worth of data (at least) before we can be comfortable this isn’t a product of random variance.

Season	SU Record (%)	Avg Line	$ Profit
2022*	33-29 (53.2%)	-124	-$161
2021	33-38 (46.5%)	-125	-$917
2020	–	–	–
2019	37-29 (56.1%)	-121	$784
2018	46-48 (48.9%)	-114	-$549
2017	35-38 (47.9%)	-113	-$940
2016	41-28 (59.4%)	-127	$949
2015	43-39 (52.4%)	-125	-$289
2014	57-48 (54.3%)	-128	-$381
2013	44-35 (55.7%)	-121	$386
2012	38-33 (53.5%)	-127	-$253
2011	41-41 (50.0%)	-123	-$724
Total	448-406 (52.5%)	-123	-$2,095 (-1.9% ROI)

* as of end of action on 7/29/22

This one looks legit, folks. Such home teams have had a negative ROI for their bettors in 4 of the past 5 seasons and have been down consistently over the past 10.

Teams in this spot are not so terrible that we can profit by betting against them, but we should definitely be keeping them off our card.

These low-scoring home teams were exceptionally awful in 2021 (when the article was written) which is why this system earned the pole position.

The author considers a “big offensive game” to be double-digit runs, citing that this system, “is literally the opposite of System No. 1 as hot-hitting home teams in divisional games were a great bet over the final month of the first half [of 2021]”.

I was extremely interested to look at the data for this one because it directly opposes the what-to-do-following-a-10-run-game system I’ve been using for a few years now.

Unlike in my system, the NY Post makes no distinction between favorite and underdog – although the author does distinguish between divisional opponent and non. Perhaps this makes a huge difference. Let’s see.

Season	SU Record (%)	Avg Line	$ Profit
2022*	26-18 (59.1%)	-158	-$52
2021	50-34 (59.5%)	-139	$798
2020	–	–	–
2019	66-49 (57.4%)	-151	-$96
2018	51-39 (56.7%)	-155	-$230
2017	50-42 (54.3%)	-141	-$842
2016	42-43 (49.4%)	-131	-$1,446
2015	37-37 (50.0%)	-125	-$669
2014	31-18 (63.3%)	-135	$975
2013	33-29 (53.2%)	-156	-$1,204
2012	39-20 (66.1%)	-145	$1,355
2011	29-29 (50.0%)	-135	-$821
Total	454-358 (55.9%)	-143	-$2,232 (-1.8% ROI)

* as of end of action on 7/29/22

Hmm, it appears the author happened to write his article during an unusual year (2021) in which these high-scoring home teams were excelling. Normally, they are negative ROI picks.

Although it’s a bummer we didn’t find anything useful here, it is comforting to know our original post-10-run system still holds true.

Gotta call the Post out on this one …

Testing a Road MLB System Plus Another for Home Teams

This system seems simple enough. I’m on board. The momentum from a nice team victory should offset the minor disadvantage of playing on the road.

Let’s see if this trend is a reliable moneymaker or if it was simply popping for a few months in 2021 when this article happened to be written.

Season	SU Record (%)	Avg Line	$ Profit
2022*	42-49 (46.2%)	-105	-$1,110
2021	72-67 (51.8%)	+103	$636
2020	–	–	–
2019	60-65 (48.0%)	-110	-$1,388
2018	62-98 (38.8%)	+102	-$4,462
2017	61-75 (44.9%)	+103	-$1,097
2016	58-69 (45.7%)	+118	-$143
2015	76-72 (51.4%)	+111	$1,118
2014	83-108 (43.5%)	+115	-$1,539
2013	74-79 (48.4%)	+112	$212
2012	70-77 (47.6%)	+113	$154
2011	74-83 (47.1%)	+108	-$393
Total	732-842 (46.5%)	+107	-$8,012 (-4.4% ROI)

* as of end of action on 7/29/22

Nope, this one is a “simply popper”. 2021 was the only season in which these teams won consistently since 2015. The road teams have only put together a few winning seasons in the past 10 years and hold a terrible -4.4% ROI.

This is nearly enough to justify betting against this trend, but not quite. Opposing this trend (which means betting on the home team in this situation) has been a break-even proposition over the past 10 seasons.

There is definitely no stock in siding with road teams off a shutout win. We should avoid them.

“Embarrassing pitching performances” are defined as games in which a team allows 12 or more runs. It is important to note this system is based on overall team pitching and not just the starting pitcher’s previous time out – as many systems are.

The author notes that, “Bettors aren’t exactly lining up to bet teams with roughed-up pitching staffs, but it seems that home teams that got hit hard in the prior game have been motivated by it and have responded well”.

He hits the nail right on the head when he says that no one is rushing to bet on teams whose pitchers just got rocked. This is where betting on pro sports mimics the stock market.

You must learn to buy low and sell high. Bad games happen but no one is better at putting that performance behind them and bouncing back than a pro athlete.

This is why – just like buying low in the stock market – having the guts to put units down on struggling teams is a proven positive-ROI strategy.

Season	SU Record (%)	Avg Line	$ Profit
2022*	23-22 (51.1%)	-109	-$124
2021	48-29 (62.3%)	-103	$1,928
2020	–	–	–
2019	53-50 (51.5%)	+107	$670
2018	41-32 (56.2%)	-107	$1,100
2017	42-47 (47.2%)	-113	-$1,145
2016	34-42 (44.7%)	-123	-$1,267
2015	32-42 (43.2%)	-123	-$1,715
2014	28-21 (57.1%)	-125	$191
2013	31-32 (49.2%)	-105	-$430
2012	29-34 (46.0%)	-122	-$1,311
2011	28-36 (43.8%)	-126	-$1,345
Total	389-387 (50.1%)	-111	-$3,448 (-3.5% ROI)

* as of end of action on 7/29/22

Mixed results here. This system needs more refining in order to find a profitable angle. We are on an upswing, however. This system was terrible from 2011-2017 but has profited a cool $3,574 since the 2018 season (excluding 2020 results).

Because of the inconsistent nature of the data, I’m going to have to downvote this one. I’ll definitely keep an eye on this trend, though.

What to Do in Case of Big Offensive Numbers

In the author’s words, “Oddsmakers tend to shade the numbers toward teams that had big hitting outings the prior day, and for the most part, these explosions tend to be flukes”.

This meshes well with our reasoning for the previous system as we should avoid paying top dollar to bet teams at their peak because this is the definition of buying high (which you want to avoid).

Season	SU Record (%)	Avg Line	$ Profit
2022*	52-59 (46.8%)	-125	-$1,894
2021	104-112 (48.1%)	-118	-$2,107
2020	–	–	–
2019	133-157 (45.9%)	-116	-$4,425
2018	117-141 (45.3%)	-115	-$4,907
2017	147-137 (51.8%)	-121	-$855
2016	156-138 (53.1%)	-112	$607
2015	137-147 (48.2%)	-107	-$2,193
2014	143-112 (56.1%)	-107	$3,164
2013	151-144 (51.2%)	-116	-$738
2012	146-117 (55.5%)	-114	$1,960
2011	127-134 (48.7%)	-111	-$2,458
Total	1413-1398 (50.3%)	-114	-$13,846 (-3.8% ROI)

* as of end of action on 7/29/22

The data confirms it. Overpaying for teams at their peak (buying high) will cost you about 3.8% ROI in the long run. Nice catch here by the Post.

 

A “power surge” here is defined by a game in which a team smacks at least 5 homers. Citing that, “Teams ha[d] really ridden the momentum of a big power outing,” in 2021, let’s take a look and see if this trend holds over multiple seasons.

Season	SU Record (%)	Avg Line	$ Profit
2022*	19-19 (50.0%)	-136	-$391
2021	31-26 (54.4%)	-138	-$286
2020	–	–	–
2019	28-28 (50.0%)	-151	-$594
2018	10-16 (38.5%)	-134	-$1,195
2017	19-12 (61.3%)	-106	$753
2016	12-16 (42.9%)	-134	-$988
2015	11-6 (64.7%)	-117	$508
2014	8-4 (66.7%)	+103	$492
2013	10-8 (55.6%)	-126	-$55
2012	14-7 (66.7%)	-128	$645
2011	8-8 (50.0%)	-130	-$70
Total	170-150 (53.1%)	-132	-$1,181 (-2.6% ROI)

* as of end of action on 7/29/22

This home run-chasing trend has been absolute garbage lately – losing $2,466 since 2018 (not even including 2020).

Siding with the power-surging teams worked like magic in the early 2010s – winning $1,590 between 2012 and 2015.

This quick-lived trend is now dead as the tides have changed. This is why it is so important to do your due diligence and keep up with the changing landscape of sports betting.

Sorry New York Post, but this one ain’t reliable no more.

A Few Miscellaneous MLB Betting Systems

The author at the Post is big on this one due to the large sample size. He says, “If you‘re looking for a system that comes up often AND produces big results, [you should] look no further”.

So what’s the logic behind this one? Road teams are already slightly more fatigued than the host team. Hitting 0 home runs the previous game means they couldn’t score in chunks. They had to grind all day.

Stringing together small hits and mounting a substantial rally is one of the most difficult things to do in all of pro sports. So many high-scoring offenses rely on the 3-run homer to get them half of their runs in one swing.

Perhaps teams who have grinded all day (possibly with very little to show for it) are not in the mood to do it again the next day – especially on the road.

Season	SU Record (%)	Avg Line	$ Profit
2022*	111-120 (48.1%)	+117	$496
2021	125-189 (39.8%)	+121	-$5,447
2020	–	–	–
2019	134-159 (45.7%)	+122	-$148
2018	142-171 (45.4%)	+121	-$532
2017	127-174 (42.2%)	+118	-$3,715
2016	141-178 (44.2%)	+116	-$2,349
2015	173-204 (45.9%)	+118	-$1,082
2014	218-251 (46.5%)	+113	-$1,295
2013	180-242 (42.7%)	+118	-$4,085
2012	186-235 (44.2%)	+118	-$2,260
2011	225-240 (48.4%)	+118	$2,119
Total	1762-2163 (44.9%)	+118	-$18,298 (-4.2% ROI)

* as of end of action on 7/29/22

Wow, I love seeing this. Once again, the -4.2% ROI is not low enough to warrant betting against this trend, but we should definitely be fading teams who meet this criteria.

Because this system is so broad and simple, it seems like a perfect candidate for some further filtering. I bet we could squeeze out even more profits from this one by playing around with it in the lab.

As it stands, though, it’s a very solid fading system.

I like the simplicity and it makes logical sense. “Teams that strike out 15 times or more in a game are obviously not seeing the ball well. This is a telltale sign of a team that is a great fade opportunity”.

I agree with his assessment. We’ve already touched on how hitting (or lack thereof) can be contagious. It’s worth taking a deep look and seeing whether a system this simple can profitably exploit that fact.

Season	SU Record (%)	Avg Line	$ Profit
2022*	38-44 (46.3%)	+106	-$395
2021	65-83 (43.9%)	+103	-$2,488
2020	–	–	–
2019	101-102 (49.8%)	+116	$658
2018	67-74 (47.5%)	+118	$62
2017	61-61 (50.0%)	+104	$214
2016	59-54 (52.2%)	+100	$829
2015	40-65 (38.1%)	+105	-$2,378
2014	43-46 (48.3%)	+105	-$143
2013	39-54 (41.9%)	+102	-$1,736
2012	33-38 (46.5%)	+100	-$247
2011	27-28 (49.1%)	+117	$343
Total	573-649 (46.9%)	+108	-$5,281 (-3.6% ROI)

* as of end of action on 7/29/22

Boom! This isn’t just a 2021 thing, it’s been true for a number of years.

What scares me is the stretch from 2016-2019 in which these high-strikeout teams actually performed very well. If that anti-trend continued any longer I might’ve had to label this system unreliable on the grounds of inconsistency.

I love the -3.6% ROI over the last 10 years on such a large sample size, though. Once again, this is a good system already that can be made better with some refining in the lab.

In order to be classified a “quality home team” in this case, you must simply be a home favorite. The author reasons that, “These teams did something right in getting players on base the prior game, and that [the good hitting] tends to carry over”.

I can get behind that line of reasoning. A large portion of bettors don’t bother with factoring in things like number of hits or runners left on base in the previous game – they just check final scores.

Stranding many runners without scoring is the baseball equivalent of stalling out in the red zone each possession. It means the offense is doing 90% of the right things but failing to cap it off.

Take advantage of these statistics which the public overlooks. Sure, this particular offense might have only scored 1 run yesterday but they had runners on the bases all night.

Don’t totally discount a team just because they couldn’t get the one key knock they needed to break the game open.

Season	SU Record (%)	Avg Line	$ Profit
2022*	24-11 (68.6%)	-158	$629
2021	43-23 (65.2%)	-180	$557
2020	–	–	–
2019	34-23 (59.6%)	-162	$157
2018	32-30 (51.6%)	-170	-$1,337
2017	36-23 (61.0%)	-163	$230
2016	46-36 (56.1%)	-164	-$846
2015	34-26 (56.7%)	-154	-$550
2014	46-34 (57.5%)	-146	-$315
2013	46-31 (59.7%)	-154	-$149
2012	48-25 (65.8%)	-151	$1,430
2011	65-37 (63.7%)	-150	$1,039
Total	454-299 (60.3%)	-159	$845 (0.7% ROI)

* as of end of action on 7/29/22

As logical as this system sounded, it is not reliable in its current form. Sure, it has won a small amount of money over the past 10 years but look at the recent stats.

Since 2013, this system has lost $1,624 and that scares me.

With additional filters, this system has the potential to be profitable. As it stands, however, this one is a big NO.