by James 

OOTP Predictions for March 29

22 Comments

Baseball Icon

Each day that I run simulations, I will post my analysis of the previous run's predictions. With that said here is yesterday's analysis:

https://datasciencereview.com/did-the-baseball-simulator-fail-for-opening-day-2019

While writing my analysis last night, Boston was still playing the Mariners and were losing to them. This is the biggest upset in the set of predictions. OOTP predicted that Boston would win by a wide margin. This doesn't mean that there is something wrong with the simulator. I covered why this is in last night's analysis. Suffice it to say, the Mariners ended up winning.

As you can see from the predictions, Boston is up against Seattle once more. The simulator is choosing Boston with a strong lead. It's likely to happen this time.

Baseball Icon

Remember, many distributions in statistics are about probabilities, not exact outcomes. Stock prediction software does not make claims to 100% accuracy. There is no reason to think that a baseball simulator can either.

Also, should Boston continue to lose to weaker teams than themselves, the simulator will reflect that it the future simulations.

Here are the predictions for today's games.

Detroit Tigers vs. Toronto Blue Jays (Home)

Predicted Winner: Toronto Blue Jays
Strength Indicator: 1.13

Colorado Rockies vs. Miami Marlins (Home)

Predicted Winner: Colorado Rockies
Strength Indicator: 1.36

Houston Astros vs. Tampa Bay Rays (Home)

Predicted Winner: Houston Astros
Strength Indicator: 1.15

St. Louis Cardinals vs. Milwaukee Brewers (Home)

Predicted Winner: St. Louis Cardinals
Strength Indicator: 1.06

Los Angeles Angels vs. Oakland Athletics (Home)

Predicted Winner: Los Angeles Angels
Strength Indicator: 1.06

Boston Red Sox vs. Seattle Mariners (Home)

Predicted Winner: Boston Red Sox
Strength Indicator: 1.46

San Francisco Giants vs. San Diego Padres (Home)

Predicted Winner: San Diego Padres
Strength Indicator: 1.06

Arizona Diamondbacks vs. Los Angeles Dodgers (Home)

Predicted Winner: Los Angeles Dodgers
Strength Indicator: 1.32

Results

Detroit Tigers vs. Toronto Blue Jays
Predicted Winner: Toronto Blue Jays
Actual Winner: Toronto Blue Jays-
Predicted Correctly

Colorado Rockies vs. Miami Marlins
Predicted Winner: Colorado Rockies
Actual Winner: Colorado Rockies-
Predicted Correctly

Houston Astros vs. Tampa Bay Rays
Predicted Winner: Houston Astros
Actual Winner: Tampa Bay Rays-
Predicted Incorrectly

St. Louis Cardinals vs. Milwaukee Brewers
Predicted Winner: St. Louis Cardinals
Actual Winner: St. Louis Cardinals-
Predicted Correctly

Los Angeles Angels vs. Oakland Athletics
Predicted Winner: Los Angeles Angels
Actual Winner: Los Angeles Angels-
Predicted Correctly

Boston Red Sox vs. Seattle Mariners
Predicted Winner: Boston Red Sox
Actual Winner: Boston Red Sox-
Predicted Correctly

San Francisco Giants vs. San Diego Padres
Predicted Winner: San Diego Padres
Actual Winner: San Diego Padres-
Predicted Correctly

Arizona Diamondbacks vs. Los Angeles Dodgers
Predicted Winner: Los Angeles Dodgers
Actual Winner: Arizona Diamondbacks-
Predicted Incorrectly

About the author 

James

James is a data science writer who has several years' experience in writing and technology. He helps others who are trying to break into the technology field like data science. If this is something you've been trying to do, you've come to the right place. You'll find resources to help you accomplish this.

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  1. Great info.

    Indeed, theyre still simulators, so they can predict something wrong. However, as I see, the majority of the times it predicts, it has it by the right end. And as I see it, this time will be right as well, no? How much should I put in bet, knowing you’re more into this knowledge than me. 

    Thanks a lot for sharing and waiting for your answer. 

    1. Hey Emmanuel, thank you for the comment. To be clear, I am not publishing this for the purposes of betting. I am using the simulator as a demonstration on how data and statistics can be used in an industry such as sports. Best Regards, Jim

  2. Prediction on sports are a pretty risky business. As a solo predictor myself , I agree with every predictions you have suggested in your article. The simulator is a written program with with many constraints put into consideration.

    This is a good post as it will assist sport staking decisions . Thanks for sharing the result of your simulation.

    1. Hey Olanike, thank you for your comment. I should note that I am providing these experiments to show how data and statistics are used in sports. Best Regards, Jim

  3. Data can go a long way in predicting the outcome of sporting events, however there are certain human factors that computerized data cannot take into consideration. Like, the absence of certain key players will always affect the outcome of sporting events. 

    In essence, it’s good to use data for forecasting, however human factors must be taken into consideration to ensure effective results

    1. Hi Louis, thanks for the comment. The human factor is something that needs to be considered, of course. My purpose of these experiments is not to predict the outcomes myself, but to test the viability of the simulator and to highlight just as you state. That no simulator can predict with 100% accuracy. Best Regards, Jim

  4. The first time i heard about data science was December last year. A friend told me about it as he was studying a course on it. He claimed if i know it also i would be a hot cake and my services would always be needed. Your ability to simulate and write programs is what i must commend you for it has not everyone could do it. Thanks so much for this educative post. I hope, to read more of your subsequent articles. 

    1. Thank you for your comment. Data science is about solving problems. People focus too much on the programming aspects. Programming is an important part of data science but it must be put into the perspective problem-solving. I have seen plenty of experienced programmers struggle with data science concept, simply because they have the wrong mindset. Best Regards, Jim

  5. Hi James,

    I’ve been following your website for a while now. I will carry out my own analysis after reading through your predictions to check for any discrepancies. For these ones posted above, I can vouch for it but with an accuracy of 78%. I know baseball simulation software can’t give 100% accuracy, in fact, no simulation software does that. 

  6. Hi there

    This is post is really insightful. This is actually the second time I am coming across the OOTP prediction. The first time was really cool. I the fixture between Detriot Tiger and Toronto Blue Jay will surely go in favour of the Toronto as predicted here because of the home ground advantage will really count in their favour 

  7. Hi James.

    I just added this website to my favorite bookmarks. Thanks for this amazing predictions. I had no idea that simulators could work. I’ll definitely try them today. But can you also make other sports predictions too, they will be very great and more visitors will visit your website. Keep up the good work. Thanks 

    1. Hey Alex, thanks for the comment and for bookmarking this site. The good news is the simulation company makes simulators for multiple sports. Enjoy! Jim

  8. Hi James

    Using your knowledge to predict the course of games in any sport is so difficult to do, as the human equation is often ignored.  You think you have taken all factors into consideration and still everything can go pear-shaped.  I wish it was possible but I believe it can never be achieved due to randomness being difficult to predict.

    How has your success been this season, compared to let’s say a bookmaker?  It would be interesting to find out.

    Thanks

    Antonio

    1. Hey Antonio, thanks for the comment and I agree with what you say. My purpose in providing this analysis is not to try to beat bookies. It’s purely for illustrative purposes on simulators and their use in data science settings. As someone else who commented stated, you need to consider the human factor.
      Best Regards, Jim

  9. Thanks for sharing your brief review about OOTP predictions for March 29. I think I will agree with you that many distributions in statistics are about probabilities, not exact outcomes. OOTP chooses Boston to win the last match which it lost but yet choosing it to win again could actually be true this time. All I will say is prepare your mind toward the list of games for March 29. It may and it may not, all is just predictions. But I hope the predictions come true.  

    1. Thanks for your comment. To be honest, I am less concerned about whether the predictions come true or not. I am conducting the experiment to help determine it. But, as for personal preference, I am just reporting the results. Of course, if the Mets go all the way to the World Series, for me that would be pretty cool!
      Best Regards,
      Jim

  10. Hi James! This is a topic that has always caught my attention: Data Science. And I’m glad I have discovered your site because you introduce us smoothly into the topic.

    I like the example of predicting MLB wining teams. I’m also a baseball fan and have found it amusing. I strongly agree the Boston Red Sox will win this time! I’ll be in expectation to contrast tonight results with the predictions of your software.

    1. Hi Henry, thank you for joining in on the discussion. It will be interesting to see the Red Sox/Mariners game in light of the strong win by the Mariners last night. The great thing about this version (20) of the simulator is that live updates are made to the stats. Therefore, if it’s shown that Boston is falling apart, the simulator will likely reflect that over time. It’s a lot fun just the same.
      Best Regards,
      Jim

  11. These simulators are a great tool to give you an edge in guessing who will win the game, it’s not cheating it’s statistics. I work in a field where I occasionally have to analyze data and I surely could use a better method of sifting through the lines of data than my current one. This sort of predictive analysis would be very suitable in an environment where be proactive is far better than being reactive and likely will save companies a lot of money by catching things well in advance.

    1. Hey Amber, thank you for your comment. Data science is a growing field and is coming into its own. The field has existed for a while, but is being formalized. It will be interesting to see future developments in this field. Best Regards, Jim

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