MLB Betting Strategy - The Big Upset

Professor MJ’s

Sports Betting Strategies

 

MLB – The Big Upset

 

This statistical study answers the following question:

When a team pulls off a big upset in Major League Baseball, how do the two teams involved in that matchup react in their following game?

The results from this investigation are based on more than 17,000 games covering the 2010 to 2016 seasons. Such a large dataset provides some pretty reliable findings!

1. Framework

Suppose Team A upsets Team B, which means Team A won the game despite fairly high odds (i.e. a large money line) on them. 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 “upset” 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 was on the losing end of the upset? 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 upsets Team B, and they meet once again the next day

2.1 Basic Exploration Under Scenario #1

Let’s make as if we had placed $1 bets on Team A and Team B after the former pulled off a big upset over the latter, during the 2010 to 2016 MLB regular seasons. The results are shown as a function of how big the upset was, as determined by the odds on the underdog, Team A, for that game:

Betting and fading a MLB team that pulled off a big upset the previous day when facing the same team again in their next following match

It’s pretty interesting to see that betting Team A led to an overall profit (+$16.47)! However, the patterns within the table temper my enthusiasm quite a bit.

First, if you take a closer look, you’ll see that a lot of money was made when the upset was done with a money line between 2.50 and 2.60. However, we lost a good chunk of cash with odds between 2.60 and 2.80, and also between 2.30 and 2.50 (not shown in the table).

That’s not a good sign because I do not see any logical reason why we should bet Team A, but only if its money line when pulling off the upset the previous day lied between such specific values as 2.50 and 2.60. It just does not make any sense. To me, the gains made within this range were just due to randomness, and I would not trust the trend to repeat itself in the future.

Similarly, the profit was great in the 3.10 - 3.40 range, but the next range (3.40+) turned out to be a disaster so once again I do not see any potential winning system here.

As for betting Team B, we lost a good amount of money doing so through the 2010 to 2016 seasons (-$58.90). One might argue that this strategy did well after a huge upset (odds 3.40 or more), but the small sample size of 55 makes the results unreliable.

As of now, there isn’t a betting strategy worth of note. Let's keep exploring!

2.2 The Road/Home Split Under Scenario #1

Does it make a difference whether Team A was the road or the home team? Let’s take a look at the empirical results!

We saw earlier that we won approximately $16 from betting Team A. The couple of tables above show a $32 profit when Team A was the road team compared to a $16 loss when playing in front of its home crowd. In other words, betting a team to follow up a big upset with another victory is a more profitable option when playing on the road.

Unfortunately, the same weird patterns as those described in the previous section show up once again. I am still going to cautiously retain this strategy for further investigation since the gains are bigger despite a smaller sample size, which yields an interesting ROI (Return On Investment). More specifically, the ROI = $32.21 / (333 + 461 games) = +4.1%.

Strategy #1 = Betting a road team that pulled off an upset with odds higher than 2.50 when facing the same opponent as the previous day.

Betting Team A at home is bad across the board. Same for betting Team B, no matter if they were at home or on the road.

2.3 The Odds Split Under Scenario #1

All right, so Team A upsets Team B with odds 2.50+ and these two teams are meeting once again the next day (let’s call it “today”). Should we bet one of those two teams under certain sets of odds? Let’s break down the earlier results, but this time as a function of today’s odds.

There just isn’t any viable strategy regarding Team B; we should definitely stay away from them.

The table above shows a good performance for Team A, from a gambling perspective, when today’s odds are less than 2.25 or more than 3.25, in decimal format. I’m not sure I want to trust the latter since the sample size is fairly small.

However, I’m willing to give a shot to the system claiming that we should bet Team A when its money line is lower than 2.25 (after upsetting the same opponent the day before with odds greater than 2.50). This system won 37.79 units over 261 games (ROI = +14.5%).

Strategy #2 = Betting a team that pulled off an upset win with odds higher than 2.50 when facing the same opponent as yesterday, but only if today’s money line is 2.25 or less.

In plain English, we are backing a team that created a fairly huge upset yesterday, but only if they are now either favored to win today’s matchup or if they are slight underdogs.

The preceding section advocated betting Team A whenever they were the road team. What does the profit look like in this context as a function of today’s odds?

Do you see a discernible pattern? I don’t. Our bankroll got hurt in the 2.75 – 3.25 odds range, but we made money in almost every other category outside of that range. A system directing you to bet all odds, except if between 2.75 and 3.25 would be strange and nonsensical. Therefore, I would argue that the system is fine no matter what today’s odds are.

2.4 The Season Split Under Scenario #1

I put faith into a betting system only if it has shown good consistency from season to season. I don’t feel comfortable about a strategy that yielded big up and down spikes, in terms of yearly profit.

With this premise in mind, let’s take a look at Strategy #1’s performance from 2010 to 2016:

There are three seasons where the profit was close to zero (2010, 2012 and 2015). Out of the remaining four years, three of them were profitable while one put us in the red in a big way.

If you want to look at the glass as half full, you’ll argue that we suffered just one bad season out of seven. On the other hand, if you want to see the glass as half empty you may claim that losing close to 19 units over a single season might have wiped out your entire bankroll! We might want to pump the brakes with respect to this potential strategy.

During the odds split, we developed Strategy #2. Let’s see how the $37.79 gains were distributed across the seven seasons considered in this study:

Now, we’re talking! The worst season occurred in 2011 where we only lost 2.24 units, which is minimal. We do observe some great consistency, albeit not perfect since you could proclaim that almost 80% of the overall profit was made through just two seasons (2010 and 2016). Still, I like what I’m seeing here!

3. Scenario #2: Team A upsets 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 Teams A and C (still based on the data from the 2010 – 2016 seasons):

As was the case under the first scenario, the team that pulled off the big upset yesterday turns out to be a money maker in their following match, as shown by its $6.10 overall profit.

Unfortunately, the same pathological pattern can be noticed: we won money in the 2.50 – 2.60 odds range, but lost outside of this interval. That’s not a good sign.

There is not much to draw from the case of betting Team C: we suffered losses in almost all ranges of money lines.

At first sight, there isn’t any system to get us excited. But let’s keep digging further!

3.2 The Road/Home Split Under Scenario #2

Could we find a viable betting strategy by breaking down the earlier results based on the location of the game?

Strangely enough, this time our wallet benefits more from betting Team A when playing at home (+$17.96) as opposed to being on the road (-$11.86).

I remain wary of such system because we made a profit when the odds were between 2.50 and 2.70, while a bigger upset (money line 2.70 or more) or a smaller upset (money line 2.50 or less) wasn’t profitable. It makes me feel very suspicious about its future outlook, I wouldn’t trust it to work well in upcoming seasons.

Meanwhile, betting Team C does not suggest any promising strategy based on the two tables above.

3.3 The Odds Split Under Scenario #2 

No luck thus far trying to find lucrative angles under the second scenario. What if we now break down the results depending on today’s odds?

Backing Team A was successful in the 1.952 to 2.50 range, but that’s about it. I would have preferred to discern gains at either ends of the spectrum (lowest odds or highest odds).

Betting Team C is more intriguing. The overall losses are not good (-$17.70), but we did increase our bankroll by $4.57 + $4.52 = $9.09 over 131 games when today’s money line on Team A was less than 1.952. In other words, wagering on Team C emerged as a good option when Team A was established as the favorite (so we were betting the underdog).

I have to admit I’m not overly excited about this prospective betting strategy, though. Making a profit of just nine units over the course of seven full seasons is negligible. I don’t believe it’s worth the risk.

In conclusion, the second scenario has nothing to get our teeth into.

4. Scenario #3: Team A upsets 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?

Both teams produced losses overall. As a matter of fact, betting Team B could not have done any worse; there isn’t a single range of odds where a gain was obtained!

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:

The only glimmer of hope that I could spot from the couple of tables above is the following: betting Team D when the upset odds were above 2.90. In this case, we made a $7.72 profit over 35 games. The sample size is so small that we cannot reasonably come to the conclusion that those findings are trustworthy.

We are still empty-handed when it comes to finding a good betting strategy under the third scenario. We only have one last shot remaining to find one.

4.3 The Odds Split Under Scenario #3

Team B was the victim of a big upset yesterday (underdog’s money line was bigger than 2.50 in decimal format). They are now facing a different squad, called Team D.

What’s the gambling outcome from betting Team B and Team D, depending on today’s odds on Team D?

Just when we were about to wave the white flag, the figures above provide some hope!

I see two potentially winning systems:

Strategy #3 = Betting Team B when today’s money line on their opponent is higher than 2.25. Profit = +$8.59 over 162 games (ROI = +5.3%).

Strategy #4 = Betting Team D when today’s money line on them is 1.952 or less. Profit = +$20.90 over 133 games (ROI = +15.7%).

4.4 The Season Split Under Scenario #3

Let’s verify if one of the two betting strategies above did a good job of showing consistent gains over time.

First, how did Strategy #3 perform from 2010 to 2016?

I’m not sure those results convince me. Sure, there are five winning seasons versus only two losing ones. But the gains basically came from two years; 2013 and 2016. The rest was just a wash. I prefer to stay away from this system, especially considering its overall gains were far from earth-shattering.

Let’s turn out attention to Strategy #4’s performance across time:

Amazing stuff!!! We finished in the red from using this system only one season out of seven. Not only that, but the losses were very small that year (-$1.69). Based on the evidence, I endorse this betting strategy.

Let me explain quickly what this system is about. A team lost despite being big favorites. In their following match, they are facing a different team but this time as underdogs. In this specific situation, you want to bet the favorite (i.e. against the team that suffered the original upset).

5. Conclusion

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

  • STRATEGY A: Suppose Team A upsets Team B with odds higher than 2.50. If they meet again the next day, bet Team A if the money line is 2.25 or less.
    • +$37.79 over 261 games (ROI = 14.5%)
    • Expected profit per season = 5.40 units ($37.79 / 7 seasons)

 

  • STRATEGY B: Suppose Team A upsets Team B with odds higher than 2.50. If Team B’s next game is against Team D, bet Team D if they are established as the favorites (i.e. a money line of 1.952 or less).
    • +$20.90 over 133 games (ROI = 15.7%)
    • Expected profit per season = 2.99 units ($20.90 / 7 seasons)

What is the expected impact on your wallet from using these two systems over the course of a single season? You can anticipate earning 5.40 + 2.99 = 8.39 units per year, which equates to $839 if your average bet is $100.

6. 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.