MLB Betting Strategy - The Comeback

Professor MJ’s

Sports Betting Strategies

 

MLB – The Comeback

 

Many curious sports bettors have asked themselves the following question:

When a team pulls off a comeback win in Major League Baseball, how do the two teams involved in that matchup react in their next game?

We answer this question through a statistical investigation, which is based on a dataset of more than 17,000 games (covering the 2010 to 2016 regular seasons). The findings derived from this study will be very reliable considering the huge sample size!

1. Framework

Suppose Team A overcomes a deficit to beat Team B, which means Team A won the meeting despite trailing by a certain number of runs at some point in the game. We consider the following three scenarios:

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

The main objective is to analyze the “comeback” effect.

On one hand, does the team that stunned its opponent follow up with a solid performance because of increased confidence, or do they tend to rest on their laurels and play sloppier?

What about the team that collapsed? Do they tend to roll up their sleeves and work harder in their next game, or does it shake their confidence?

2. Scenario #1: Team A pulls off a comeback versus Team B, and they meet once again the next day

 

2.1 Basic Exploration Under Scenario #1

Let’s see what happens if we place imaginary $1 bets, during the 2010-2016 period, every time two teams meet again after one of the two teams involved in the matchup made a nice comeback to beat the other.

The results in the table below show the outcome of placing such wagers, as a function of how “big” the comeback was. This is measured in terms of the largest run deficit that was overcome by the winning team the previous day (called “Largest Deficit” throughout this article).

Obviously, we should stay away from Team B (i.e. the team that was the victim of the comeback in the previous contest). As you can see in the last column of the table above, we suffered losses no matter the deficit.

Conversely, we already have a promising system on our hands when glancing through Team A’s profit column. We won money under all cases, except when the deficit was 3. Therefore, we are going to dig deeper with respect to the following betting system:

  • Betting Team A after it overcame a 4+-point deficit against the same opponent the previous day. Profit = +$33.72 over 384 games. Return On Investment (ROI) = 33.72 / 384 = +8.8%.

2.2 The Road/Home Split Under Scenario #1

Does it make a difference whether Team A was the road or the home team? We shall answer this question via the following couple of tables:

Wagering on Team B remains a poor choice: we lost money across all situations. There is absolutely no doubt that we shouldn’t put a single penny on them.

Recall how we decided earlier to focus on betting Team A if it overcame a deficit of four runs or more. Under such circumstances, remember that we won $33.72. These winnings were obtained as follows: $19.98 on the road versus $13.74 at home. To me, that’s not a significant difference.

As a result, I’m going to contend that we should adopt the system described earlier no matter the location of the game.

2.3 The Odds Split Under Scenario #1

We are off to a good start! Let’s now verify whether the retained betting strategy performed better on favorites or underdogs.

To do so, I have broken down Team A’s money line into 11 ranges:

What is your conclusion?

Here is mine: we might want to avoid cases where we are betting big favorites. More specifically, we lost money in the 0 – 1.50 and 1.50 – 1.5714 odds ranges, so why not leave them out?

In light of the above, the revised betting system becomes:

  • Betting Team A after it overcame a 4+-point deficit against the same opponent the previous day. Bet only if Team A’s money line is greater than 1.5714 (-175 in American format). Profit = +$38.13 over 344 games. ROI = +11.1%.

2.4 The Season Split Under Scenario #1

I am willing to put money on a system that has done well in the past, only if it has shown good consistency from year to year. Let’s find out whether our prospective betting strategy fits that profile:

Great stuff! We could not have asked for better results!

Indeed, we detect six winnings seasons versus a single losing one, in which we lost a negligible amount of money (-$1.58 during the 2014 MLB regular season).

I am very hopeful that this baseball betting system is going to replicate its results in the future.

3. Scenario #2: Team A pulls off a comeback versus Team B, and Team A’s next game is against a different opponent called Team C

 

3.1 Basic Exploration Under Scenario #2

Let’s take a look at the results from placing $1 bets on Teams A and C (still based on the data from the 2010 – 2016 seasons):

Betting Team A produced gains when the largest deficit was exactly three, but we lost money whenever it reached four runs or more.

Who would trust a system that claims you should bet a team if it overcame, in its last game, a deficit of exactly three runs, but that you should stay away if the deficit was four or more? I wouldn’t. It just doesn’t make sense from a logical standpoint. Also, earning less than 7 units over a 7-year period isn’t particularly impressive.

The alternative option of putting cash on Team C is intriguing: we lost a notable amount of money when the deficit was three, but they turned out to be moneymakers as soon as the deficit reached at least four runs. That’s a more coherent system.

Therefore, I have decided to scrutinize further the following strategy:

  • Betting Team C if their opponent overcame a deficit of 4+ runs in their previous game against a different opponent. Profit = +$13.50 over 177 games. ROI = +7.6%.

Let’s keep in mind, though, that we should remain cautious since the losses were huge when the deficit hit exactly three runs. It makes me wary and I’m going to require more impressive numbers before adopting this system.

3.2 The Road/Home Split Under Scenario #2

Hopefully, we can increase our winnings by splitting the results based on the location of the game. Let’s see if that’s the case or not:

All of a sudden, betting Team A becomes a winning proposition when they were the visiting team. Still, we are going to exclude this option since the gains were too small (only $4.50).

Team C also performed better when playing on the road. That is especially true of the system we retained which suggested to bet them if the deficit was 4+: the profit equals $17.09 on the road compared to -$3.58 at home.

In view of those findings, we will now pursue the following revised betting strategy:

  • Betting Team C on the road if their opponent overcame a deficit of 4+ runs in their previous game against a different opponent. Profit = +$17.00 over 97 games. ROI = +17.6%.

Notice how the ROI grew from 7.6% to 17.6%. That’s great, but seeing the sample size drop below 100 is cause for concern. How trustworthy are the results now?

3.3 The Odds Split Under Scenario #2

Much like we did earlier with Scenario #1, we create 11 odds categories and explore the performance of the retained system as a function of Team A’s money line:

One might argue that we should reject cases where Team A’s odds exceed 2.05 since we did not win cash in any of the last five ranges.

However, I see three arguments against doing so: 1) there were just 12 games matching this description, so the results aren’t very reliable; 2) the losses, -$1.38, were small; 3) the very next range, 1.952 – 2.05 yielded great gains (+$7.09).

Taking these factors into account, my final judgment here is that this betting approach did well across all odds.

3.4 The Season Split Under Scenario #2

Did the system under study generate gains on a consistent basis?

We observe six seasons where we finished the season above $0, compared to just one where we ended up in the red.

You have certainly spotted how low the sample sizes are; the number of bets during the whole 2014 season was just five! That’s less than one pick per month!

4. Scenario #3: Team A pulls off a comeback versus Team B, and Team B’s next game is against a different opponent called Team D

 

4.1 Basic Exploration Under Scenario #3

What would have happened by risking $1 on Teams B and D under the third scenario?

You don’t need to be a genius to determine that betting Team B is not a sound idea. The overall losses of -$34.50 are enormous!

Meanwhile, betting Team D led to $12.37 gains, but not only are those winnings not particularly big, but we lost cash when the deficit reached four runs or more. That’s not reassuring at all.

4.2 The Road/Home Split Under Scenario #3

Hopefully, splitting the results as a function of the location of the game will unveil a promising system:

We have renewed hope when detecting gains across all deficits in the case of backing Team D on the road. Under this setting, we are going to examine the following potential betting system:

  • Betting Team D on the road if their opponent was the victim of a 3+-run comeback in their previous game against a different opponent. Profit = +$11.71 over 236 games. ROI = +5.0%.

Let’s be honest: a 5% ROI is not earth-shattering with a sample size close to 200. Let’s see if we can improve the results in the subsequent section.

4.3 The Odds Split Under Scenario #3

How did the above system do, from 2010 to 2016, depending on Team D’s odds?

The only modification I could suggest is to omit cases where Team D was a huge underdog with odds higher than 3.00 (or +200 in American format).

Doing so yields the following revised betting technique:

  • Betting Team D on the road if their opponent was the victim of a 3+-run comeback in their previous game against a different opponent. Bet only if Team D’s money line is 3.00 or less. Profit = +$16.27 over 228 games. ROI = +7.1%.

4.4 The Season Split Under Scenario #3

How did the above system perform from 2010 to 2016?

I’m not sure those results convince me, especially considering I wasn’t super excited by the numbers obtained thus far. There are five winning seasons versus only two losing ones, but the losses in 2013 are relatively important.

I’ll tell you my final recommendation regarding this betting strategy in the Conclusion section later on.

5. The Late Comeback

I felt like looking at one more specific situation that might occur during a game.

Suppose a team pulls off a dramatic comeback in the latest stages of a game, and the two teams involved in the meeting face each other once again the next day. Who is most likely to win the rematch?

5.1 The Late Road Comeback

I have defined a late comeback by the road team as a win obtained after trailing through eight full innings.

Whenever this situation materialized during the 2010-2016 period, I have placed fictitious $1 bets when these two teams met once again the next day. Here are the results:

Clearly, if you want to put some money at risk, it should be done on the road team!

Does it matter if they were favorites or underdogs? Let’s find out!

I am torn between not caring about the odds, or focusing only on cases where the road team’s odds were greater than 2.25. Sure, the latter criterion produced good results but at the cost of vastly reduced sample size, which makes the results less reliable.

More precisely, imposing the restriction with respect to the money line produced 33 bets won, 32 bets lost and a $22.46 profit, which equates to a 34.6% ROI.

Let’s have a look at the yearly performance of the system with and without the odds criterion:

Both systems had a 5-2 record in terms of whether we attained profitability or not.

However, notice the huge losses in 2015 when the money line was not taken into account. That makes me believe it is preferable to include this criterion when taking into consideration this betting strategy.

Can we really put much faith into a system based on a sample size of just 65 games, though?

5.2 The Late Home Comeback

What is the definition of a late comeback by the team playing in front of its home crowd? I have decided to incorporate two specific cases:

  • The home team wins the game despite trailing through 8 ½ innings;
  • The home team wins the game despite trailing at some point in extra innings.

Whenever this situation arised in the 2010 to 2016 MLB regular seasons, I placed $1 bets on each team if they faced each other once again the next day:

Neither choice turned out to be a winner. Backing the road team seems like the best option, if we really had to bet.

Let’s see if we can find a system with a promising outlook when breaking down the road team’s results by odds:

Nope. Nothing good comes out of this search.

Sure, we made huge gains when the odds lied between 2.75 and 3.00, but all ranges next to it generated ugly results.

Also, the most optimistic people might claim betting favorites was profitable: we won $12.71 when the money line was 1.952 or less. However, we did suffer losses when the odds were 1.667 or less, and gaining $12.71 over seven seasons it not sufficiently large to warrant taking a risk on this betting strategy.

 

6. Conclusion

Overall, we came up with one definitively great betting system and three that were borderline acceptable.

Consequently, I strongly advise implementing the following betting strategy as part of your sports investing arsenal to beat your bookies:

  • Suppose Team A comes back from a deficit of 4+ runs against Team B. If they meet again the next day, bet Team A if its money line is greater than 1.5714.
    • +$38.13 over 344 games (ROI = 11.1%)
    • Expected profit per season = 5.45 units ($38.13 / 7 seasons)

Using this betting method, you can expect to earn an average of $5,450 if your average bet is $1,000.

If your risk tolerance level is higher, you could also include the following baseball systems:

  • Suppose Team A comes back from a deficit of 4+ runs against Team B. If Team A’s next game is against Team C, bet Team C if they are on the road.
    • +$17.09 over 97 games (ROI = 17.6%)
    • Expected profit per season = 2.44 units

 

  • Suppose Team A comes back from a deficit of 3+ runs against Team B. If Team B’s next game is against Team D, bet Team D if they are on the road and their money line is 3.00 or less.
    • +$16.27 over 228 games (ROI = 7.1%)
    • Expected profit per season = 2.32 units

 

  • Suppose a road team makes a late comeback, which means winning despite trailing through 8 innings. Bet that team if they face the same opponent the next day and their money line is greater than 2.25.
    • +$22.46 over 65 games (ROI = 34.6%)
    • Expected profit per season = 3.21 units

7. Who is the best online sportsbook?

Take a look at my list of top 20 safest online bookmakers: Professor MJ's sportsbook reviews

 

Thanks for reading!

Professor MJ (www.professormj.com)

Disclaimer: I am not telling anyone to go out and bet those angles blindly. There are no guarantees in the sports betting world. This article is presenting findings from past data and then trying to find what seem to be potential winning strategies. Bet at your own risk. I am not responsible for any losses incurred from such wagers.