# Sports Betting Strategies

MLB – The Blowout Effect

«When a team blows out an opponent, how do these two teams react in their following game?»

The results presented in this article come from a dataset containing information about all Major League Baseball (MLB) regular season games from the seven (7) seasons covering the 2010 to 2016 period. In total, we have data on over 17,000 games.

## 1. Framework

Suppose Team A blows out Team B, which means they win by “x” runs. We consider the following three scenarios:

SCENARIO #1:
These two teams meet again the next day. Should we bet on Team A or Team B?

SCENARIO #2:
Team A’s next game is against a different opponent, let’s call it Team C. Should we bet on Team A or Team C?

SCENARIO #3:
Team B’s next game is against a different opponent, let’s call it Team D. Should we bet on Team B or Team D?

The main objective is to analyze the blowout effect.

On one hand, does the team that won by a large margin follow up with a nice performance because of momentum, or does it tend to be sloppier because of overconfidence?

How about the team that lost by a wide margin? Is the blowout a wakeup call for them and leads to a razor-sharp performance in their next game, or does it crush their self-confidence?

## Team A blows out Team B, and they meet once again the next day

### 2.1 Basic Exploration Under Scenario #1

Here are the results from placing \$1 bets on Team A and Team B the day after the former beats the latter by exactly “x” runs (based on the data from the 2010 – 2016 seasons):

This very trivial betting strategy yields very interesting and surprising findings.

First of all, betting Team A (i.e. the team that just blew out its opponent) leads to an overall \$20.26 profit when the margin of victory was five runs or more. The Return On Investment (ROI) is equal to 0.7% (\$20.26 / 3102 games). However, we note that the results are more impressive when considering games following a blow out by 8+ runs. Under such circumstances, we achieved a \$49.17 profit over a total of 930 games which corresponds to a non-negligible ROI of 5.3%. That’s a very promising MLB betting strategy that we will scrutinize further.

Secondly, betting Team B turns out to be a disaster. This strategy incurs a loss no matter the margin of victory, except if x = 10 (which is probably due to randomness). There is no doubt that we should absolutely avoid backing a team that just got beaten up badly when facing the same rival the next day.

### 2.2 The Road/Home Split Under Scenario #1

Let’s first break down the results from the table above into two separate cases, depending on whether Team A (the team that won its previous game by a large margin against Team B) plays on the road or at home when facing the same opponent once more.

Let’s kick off by highlighting the fact that all of the profit on Team A arises when they are on the road (+\$21.60 on the road versus -\$1.33 at home). However, a deeper look reveals a compelling finding: the gains are approximately the same on both locations if we focus on the strategy we raised earlier, which consisted of placing a bet only if the margin of victory was eight runs or more (+\$28.19 on the road versus +\$20.99 at home).

As for Team B, backing them leads to a \$49.34 loss on the road compared to a \$100.12 loss at home. Even focusing on cases where the margin of victory was large, nothing good comes out of it.

### 2.3 The Odds Split Under Scenario #1

Are there any signs that certain sets of odds provide profitable situations? Let’s assess the role of money lines under the current setting. In order to do so, I have separated the possible money lines into 11 categories. We then look at the profit made within each such category.

Based on the table above, it seems like placing wagers on Team A is preferable whenever the money line on Team B is +125 (i.e. 2.25 in decimal format) or less. In terms of the money line on Team A, we are talking about -140 (i.e. 1.714) or more. In plain English, Team A is a better bet if it is either an underdog or a small favorite. Under those conditions, the profit was +\$51.85 over 2148 games (ROI = 2.4%).

We do not perceive any exciting finding with respect to Team B, except maybe if their money line is above +200 (i.e. 3.00 in decimal format). This limitation provides a +\$14.92 gain over 105 games (ROI = 14.2%).

The preceding sections advocated betting on Team A whenever the margin of victory was 8 or more. What does the profit look like in this context as a function of the odds?

As you can see above, the strategy is profitable across all odds. There does not appear to be any “odds” effect whatsoever.

### 2.4 The Season Split Under Scenario #1

A good way to gauge a system’s reliability is to check its performance across years. We do not feel good about a particular system if almost all of the gains were made in 1-2 specific year(s). We prefer discovering consistent winnings. Let’s see how it plays out for the strategies discussed earlier.

All indications point towards betting Team A if the margin of victory was 8 runs or more. The road/home split and the odds split both showed that this strategy seemed to work under any circumstances. Let’s see the season-by-season performance:

The fact that a good chunk of the gains were made in 2012 is not ideal, but we still notice six winning seasons versus only one losing one. Those are pretty consistent numbers.

During the odds split, we raised the possibility of wanting to bet Team B when being big underdogs (i.e. money line above 3.00 in decimal format). Let’s see how the \$14.92 gains were distributed across the seven seasons considered in this study:

We definitely do not like what we are seeing here: three winning seasons versus four losing ones. I recommend abandoning this idea.

## Team A blows out Team B, and Team A's next game is against a different opponent called Team C

### 3.1 Basic Exploration Under Scenario #2

Here are the results from placing \$1 bets on Team A and Team C when Team A’s most recent game was a win by exactly “x” runs against a different team (still based on the data from the 2010 – 2016 seasons):

The first observation is that both propositions (betting Team A and betting Team C) are equally bad from a gambling perspective. As a matter of fact, they both induce a \$17-\$18 loss.

The table above nonetheless suggests a potential winning strategy. More specifically, betting Team C following a 7+-point blowout win by Team A over a different team yields a \$20.39 profit over 590 games (ROI = 3.5%). We shall keep this tactic in mind as we refine our analysis in the following sections.

### 3.2 The Road/Home Split Under Scenario #2

We now break down the results above contingent on the location of the game:

All of the losses on Team A occurred on the road. When they were playing at home, the majority of the profits were obtained when the margin of victory was 5 or 6, so I do not see anything interesting here (it would be illogical to bet Team A following a 5 or 6-point victory, but staying away after a win by 7+ runs!).

I brought your attention earlier to the following potential betting strategy: betting Team C when Team A won its previous matchup (against a different team) by 7 runs or more. The road/home split yields a +\$21.07 profit on the road (over 275 games, ROI = 7.7%) versus a -\$0.68 profit at home. Therefore, it seems like Team C takes advantage of Team A’s overconfidence, especially when Team A plays in front of its home crowd.

### 3.3 The Odds Split Under Scenario #2

This scenario has shown thus far that betting Team A was a bad idea. When looking at the previous table, there might be some light at the end of the tunnel: betting this team if their money line was above +150 (i.e. 2.50 in decimal format) led to a +\$25.24 profit over 121 games. The ROI therefore stands at 20.9%.

Backing Team C has proven profitable so far when the margin of victory was 7+. Omitting temporarily the margin of victory, the table above shows that betting Team C is beneficial when the money line on their opponent is -150 (i.e. 1.667 in decimal format) at most, but even more so if it is -200 (i.e. 1.50 in decimal format) at most. What if we focus on the cases where the margin of victory was 7+? What if we incorporate the fact that Team C must also be on the road?

We see pretty clearly how the strategy should be employed particularly when the odds on their opponent was between 0 and 1.50 in decimal format (+\$34.58 over 46 games if margin 7+, or +\$32.90 over 42 games if margin 7+ and Team C on the road). Let’s be careful about the small sample size though. In terms of the money line on Team C, we therefore require odds +180 (2.80 in decimal format) or higher. More precisely, we generate more gains when Team C is a big underdog.

### 3.4 The Season Split Under Scenario #2

We brought up four potential strategies under the setting where a team wins easily (Team A) before facing a different team in their next game (Team C). Let’s review the season-by-season profit for each of them.

Betting Team C if Team A’s margin of victory in their previous game was 7+:

Only 2015 incurred a loss, but it was a fairly big one…

Betting Team C if they are on the road and if Team A’s margin of victory in their previous game was 7+:

Once again we get decent results: five winning seasons versus two losing ones.

Betting Team C if they are on the road, if their money line was 2.80 or higher, and if Team A’s margin of victory in their previous game was 7+:

Those are near-perfect results, as all seven seasons led to a positive profit!

Betting Team A if their money line was greater than 2.50:

The results are mitigated here. We do have five winning seasons versus two losing ones, but two of the profitable years were by a slight margin. As a matter of fact, the 2010-2013 seasons combined for a \$4.18 loss, while the 2014-2016 led to a \$29.42 gain. That’s not necessarily the type of consistency (or lack thereof) we are looking for. I believe it’s best to omit this strategy.

## Team A blows out Team B, and Team B's next game is against a different opponent called Team D

### 4.1 Basic Exploration Under Scenario #3

Here are the results from placing \$1 bets on Team B and Team D when Team B’s most recent game was a loss by exactly “x” runs against a different team (still based on the data from the 2010 – 2016 seasons):

We observe a similar pattern to the previous strategy, where both options seem to be equally poor (a \$41.32 loss from backing Team B compared to a \$30.27 loss from wagering on Team D).

We may still want to examine further a prospective strategy: betting Team B after they were defeated by 11 runs or more against a different opponent. The gain was \$16.35 over 78 games; the ROI then equates to 21.0%. We’ll dig deeper, but we are wary of the small sample size here.

### 4.2 The Road/Home Split Under Scenario #3

Let’s see how the location of the game may or may not affect the gambling outcome:

Recall how we suggested earlier that it might be a good idea to bet Team B whenever their previous game was a loss by 11 runs or more (when now facing a different team). Despite losing much more when backing Team B on the road, it is good to see how restricting ourselves to the case where the margin of victory was 11+ yields a positive gain, no matter the location (+\$8.15 on the road, +\$8.20 at home).

Meanwhile, all of Team D’s losses occur on the road, but the results at home do not indicate a viable betting strategy since increasing the margin of victory does not lead to better results.

### 4.3 The Odds Split Under Scenario #3

We do not spot any interesting patterns from the previous table, either from betting on Team B or Team D. Let’s remember, though, that we intended to pay attention to the case where we bet Team B if the margin of victory was 11 or more. What is the performance of this system across all 11 odds categories? The answer can be found below:

The phenomenon that stands out is the following: the odds do not matter here.

### 4.4 The Season Split Under Scenario #3

A single strategy was reported in the case where a team gets blown out (Team B) before facing a new opponent (Team D): betting Team B if they were beaten by 11 runs or more in their previous game. How was the \$16.35 profit distributed across the years?

The results are not too bad: five out of the seven seasons provided some winnings. Add in the fact that the sample size was fairly small (78 games) and it prompts me to recommend this strategy with caution.

## 5. Conclusion

Let’s summarize the findings from this study by describing clearly the betting strategies that seem to offer a promising outlook.

STRATEGY #1: Suppose Team A blows out Team B by 8+ runs. If they meet again the next day, bet Team A.

• +\$49.17 over 930 games (ROI = 5.3%)
• Expected profit per season = 7.02 units (\$49.17 / 7 seasons)

STRATEGY #2: Suppose Team A blows out Team B by 7+ runs. If Team A’s next game is against Team C, bet Team C (especially if they are on the road and/or their money line is +180 [i.e. 2.80] or higher).

• +\$20.39 over 590 games (ROI = 3.5%)
• Expected profit per season = 2.91 units (\$20.39 / 7 seasons)

STRATEGY #3: Suppose Team A blows out Team B by 11+ runs. If Team B’s next game is against Team D, bet Team B. Proceed with caution since the results are less convincing.

• +\$16.35 over 78 games (ROI = 21.0%)
• Expected profit per season = 2.34 units (\$16.35 / 7 seasons)

Those three strategies combined are expected to generate a profit of 7.02 + 2.91 + 2.34 = 12.27 units per full MLB season. Accordingly, if your average bet is \$100 you should expect to make 12.27 * 100 = \$1,227 per year.

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