Professor MJ’s

Sports Betting Strategies

 

MLB – The Hot Teams Matchup

 

In this article, we are going to investigate the following situation:

 

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

Note: Some people have emailed me asking why I left out the most recent seasons. The reason is simple: I don’t own the data for such years. If you do and are willing to share it with me, I will be glad to either: 1) include those seasons in my analysis; 2) use it to verify if the suggested betting strategies would have done well during those years.

1. Basic Exploration

Let’s start the analysis with a very simple investigation: we are betting $1 on each road team coming off at least one win playing against a home team coming off at least one win. Here are the results:

• Record = 595-738 (win percentage = 44.6%)
• Profit = -$68.74 (ROI = Return On Investment = -5.2%)

Let’s now turn our attention to the opposite strategy: betting $1 on each home team coming off at least one win playing against a road team coming off at least one win. The results are presented below:

• Record = 738-595 (win percentage = 56.0%)
• Profit = +$3.25 (ROI = Return On Investment = +0.2%)

Neither option is a viable betting strategy, which is not surprising. Let’s try to find some profitable angles by exploring other factors.

2. The Streak Split

Perhaps the length of the teams’ respective winning streaks happen to play a role in the outcome of the game. For instance, it is possible that the public tends to pour money on a team whose winning streak is a long one. In such instances, the money line on their opponent may provide a good opportunity to astute gamblers.

2.1 Betting the Road Team

Let’s make the following investigation: betting $1 on road teams coming off at least one win facing a home team who is also coming off at least one victory, but this time we break down the results by the length of each team’s winning streak.

The figures below indicate the profit obtained from placing such wagers on the road team; the numbers in brackets specify the number of bets.

Overall, the losses were fairly significant, but we do find an interesting prospect:

• Strategy #1: Betting a road team coming off at least one win playing a home team coming off at least four wins in a row. Profit = +$10.57 over 163 games (ROI = +6.5%).

The narrative fits the hypothesis described earlier where we want to fade (i.e. bet against) teams surfing a long winning streak. However, I can’t say I love Strategy #1 because of the -$7.03 losses incurred when the home team had a 6-game winning streak or more. Still, let’s keep this strategy in mind for further examination.

2.2 Betting the Home Team

Now, let me break down the case of betting a home team coming off at least one win playing against a road team also coming off at least one win by the length of each team’s winning streak. Once again, the figures below indicate the profit obtained from placing $1 bets, whereas the numbers in brackets specify the number of games.

Based on the figures above, let me point out three potential betting strategies:

• Strategy #2: Betting a home team coming off at most three straight wins playing a road team coming off at least one victory (first three rows of the table). Profit = +$19.77 over 1170 games (ROI = +1.7%).
• Strategy #3: Betting a home team coming off at least one win playing a road team coming off a maximum of two consecutive victories (first two columns of the table). Profit = +$15.87 over 980 games (ROI = +1.6%).
• Strategy #4: Betting a home team coming off at most two straight wins playing a road team coming off at least four consecutive victories. Profit = +$8.79 over 137 games (ROI = 6.4%).

The return on investment for Strategy #2 is not impressive, but the huge sample size makes it more appealing. Also, the rationale behind it supports our previous hypothesis of staying away from teams riding a long winning streak.

I’m not a big fan of the third strategy since it goes against our initial conjecture; I just don’t see the logic behind it.

The fourth strategy seems more promising: a good mix of a reasonable sample size, a good return on investment and its rationale makes perfect sense.

3. The Odds Split

Are each of the suggested strategies performing better when we are betting underdogs or favorites? More specifically, what profit are we making for different sets of odds? This section answers that question.

Let’s examine Strategy #1: Betting a road team coming off at least one win playing a home team coming off at least four wins in a row.

Do you seen any patterns in the table above? I don’t. The $10.57 profit was made over many different odds categories, including some as favorites and some as underdogs.

Let’s jump to the second strategy: Betting a home team coming off at most three straight wins playing a road team coming off at least one victory.

We are observing huge up-and-down swings from one category to the next. One might argue that betting when the money line is higher than 2.05 seems like the way to go. It yields a $17.85 profit over 264 games (ROI = 6.8%). Let’s call it Strategy #2B.

Let me now break down the results for the third strategy by odds:

Nothing worth of note here. Betting favorites worked out well in the 1.5714 – 1.80 range, but did badly for both big favorites (0 – 1.5714) and slight favorites (1.80 – 1.952). As for underdogs, we do not perceive anything spectacular nor convincing.

How about Strategy #4? What was the performance of the system as a function of the odds? The answer can be found below:

The first few categories are alternating between positive and negative profits, but the last four all yield a net gain. The profit from betting if the money line was above 2.25 is +$11.17 over 20 games (ROI = 55.9%). It looks great at first sight, but such a small sample size is pretty unreliable so let’s not get overly excited here.

4. The Season Split

In my humble opinion, the final step before adopting a particular betting strategy is to check if it would have done well in most seasons. A bad sign is when a good chunk of the gains were made in one or two specific season(s).

With this objective in mind, let’s find out the season-by-season performance of strategies 1, 2, 2B, 3 and 4 detailed earlier.

Strategy #1: Betting a road team coming off at least one win playing a home team coming off at least four wins in a row.

My assessment here is that the consistency is neither great, nor bad. We distinguish four good profitable seasons, two that were close to zero, and one horrible year in 2013.

Strategy #2: Betting a home team coming off at most three straight wins playing a road team coming off at least one victory.

We discover four winnings seasons versus three losing ones. That’s not reassuring. Considering the strategy’s low ROI, I would personally exclude this strategy.

Strategy #2B: Betting a home team coming off at most three straight wins playing a road team coming off at least one victory (if the money line is higher than 2.05).

Basically, we had three great and three bad years, whereas 2013 yielded close to $0. I really hate what I’m seeing here and strongly suggest you leave out this strategy.

Strategy #3: Betting a home team coming off at least one win playing a road team coming off a maximum of two consecutive victories.

My gosh, the results above look like a roller coaster ride with some very rewarding years, and one particularly painful 2014 season. In total, we detect four winning seasons compared to three negative ones.

Considering I was not a big proponent of this strategy in the first place because of its lack of logic, my advice is to drop this system.

Strategy #4: Betting a home team coming off at most two straight wins playing a road team coming off at least four consecutive victories.

We would have lost money in 2010 and 2011 before going on a five-year streak of positive net gains. A word of caution: the sample sizes are pretty small with roughly 20 games per season, on average. Therefore, the results are more or less reliable. Its rationale makes sense, so I’m still willing to give it a shot.

5. Conclusion

Based on this in-depth statistical study of past MLB data, I believe there are two strategies that offer a promising outlook and are worth betting in the future:

• Betting a road team coming off at least one win playing a home team coming off at least four wins in a row.
  • +$10.57 over 163 games (ROI = +6.5%)
  • Expected profit per season = 1.51 units ($10.57 / 7 seasons)
• Betting a home team coming off at most two straight wins playing a road team coming off at least four wins in a row.
  • +$8.79 over 137 games (ROI = 6.4%)
  • Expected profit per season = 1.26 units ($8.79 / 7 seasons)

Let’s face the reality: there isn’t any earth-shattering betting strategy when two Major League Baseball teams face each other with both being on a winning streak. The two systems we have retained have yet to convince me strongly about their future outlook.

My final tip would be to play these angles with caution. If you are risk averse, stay away from them. If you are willing to take more risk, please place wagers that are below your average bet amount. 

 

6. How to Thank Me

If there is one single way to thank me for the free articles I publish online, it is this one:

 

https://www.professormj.com/pages/sportsbook-reviews

It won’t cost you a penny, and the bookie will give me a commission if you make a deposit with them. A big thank you to those of you who have already done it!

 

Thanks for reading and don’t forget to join my mailing list if you wish to receive other sports betting strategies directly in your mailbox! You can do so from the bottom-right corner of the homepage: www.professormj.com

Professor MJ

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.