Welcome you back and thanks for reading, following along and interacting on Twitter @MLBMovingAvg. Let’s keep this monster season rolling and get this money.
A Quick Intro to MLB Moving Averages
MLBMA incorporates proven methods of technical analysis, indicators and trading techniques towards the current MLB betting space. I created some custom stats to encompass all the prerequisites, and most importantly save us all a ridiculous amount of time. Therefore, a team receiving high grades in Offensive Runs Created (ORC+) or Pitching Runs Allowed (PRA+) have already passed rigorous standards on combinations of weighted averages, trend & split tests.
To list all pertinent stats individually is a waste of precious time we just don’t have in a nonstop, 24/7 MLB market. Also if you’ve been following along this season, you have an idea of how I structure of my statistical arguments. Please feel free to refer to the earliest articles on my author page https://windailysports.com/author/mlbmovingavg/ to see some of these metrics walked out in more detail.
Ultimately my goal is to provide you instantly with the most comprehensive & unique betting snapshot of any given MLB slate. I want it to cater to those serious players without tons of time to do their own data mining, and be immediately effective across all formats. MLBMA is a 24/7 profit mindset. This type of system requires only the most disciplined, intelligent and thinking players; Those determined to provide sustainable income over the long haul.
MLBMA Implied Team Totals Betting Key
- Final – Each individual team’s score after the first five innings. Generally, a difference of one full run is the initial indicator for a high percentage ML play.
- F5 ITT – First Five Implied Team Totals. It is the sum of the two finals and can be applied to F5 Over/Under betting.
- F5 ML – First Five Inning Money Line Bet
- F5 RL – First Five Inning Run Line Bet. -0.5 for Favorites and +0.5 for Underdogs.
- FG ML – Full Game Money Line Bet
- FG RL – Full Game Run Line Bet. -1.5 for Favorites and +1.5 for Underdogs.
- Bets that satisfy the algorithm’s parameters are marked in green and labeled as the highest percentage plays. Not every flagged play ends up being bet.
Remember, these values are on a scale and not to be taken literally. This also makes them extremely versatile across betting formats. The same way we use a difference in implied totals to determine a winner, we can use totals to bet the Over/Under. Any Implied Team Total above 3.0 for a team is considered high, and below 2.6 is considered low. Therefore, a combined F5 total (F5ITT) over 6.0 would be a candidate for an Over bet and a combined F5 total under 5.2 would be a flag for the under.
Once an F5 play is identified, I run some custom MLBMA bullpen filters to determine if the bet is better suited as a nine inning play. The state of the average bullpen in the bigs is so revolting right now, that I instinctively lean towards the more predictable five inning play. However, some situations do call for a FG bet and I never like to leave any stones unturned; This is money we’re talking about.
MLBMA Algo Results w/Implied Team Totals, 8/12
*** I am not in love with this slate, but since I took the time to run the algo & all the filters I decided to share. If you notice all of the plays are nine inning plays, which means they failed somewhere along the F5 algorithm. I have been overly vocal in my distrust of bullpens this year. I pride myself on full transparency, and today I am not even risking 1%. Remember, that guideline is a maximum, not a minimum.
(That being said, $20 pays $475 if we sweep the board for all my Action Jacksons out there.)
I’ll wait for tomorrow’s slate to get back to serious action
NOTE: CONSIDER ALWAYS MOVING (-) RL PLAYS TO ML FOR PARLAYS
TODAY IS AT THE STANDARD 1% RISK AFTER LOSING NIGHT 8/11
SCROLL DOWN FOR STRATEGY
General Risk Strategy
I often get questions about betting strategy and it all starts with proactively determining how much I’m going to bet and then working backwards when applying that to my plays on a percent basis.
My daily allotted risk (R) shifts with performance. I have found that reducing bets during down times helps greatly in protecting capital. To be more specific, my standard is a risk of R=1% total stack. After a winning day it goes to 1.5%, and caps at a max 2% after two wins in a row. I do the opposite as well. I remain at 1% if there are two losing days, and reduce to a capped minimum of 0.5% for the third.
Daily Betting Strategies
I’d like to share a few of my fallback plans on playing strategy in case I don’t get to a specific plan on a given night. If a bet is at or close to even, bet it straight. We want to avoid pairing action as much as possible. Sometimes the odds make this unavoidable. Whenever I have two heavy favorite picks, I will pair them. If I have three favorites, I play a small ABC F5ML parlay, and then play all three F5RLs straight, but of course it always depends on the specific odds that day. I do not like to pay any juice beyond -200. I would never play those straight. We must pair, or fade. Any team can win on any day.
Whenever I have four picks, I’m usually going to play a Round Robin where 3 of 4 hits will guarantee a nice percentage profit, and all 4 is huge night on a relatively small risk. I also always take a small percentage of daily risk on a four game parlay.
If there are ever more than four plays, I try to compartmentalize the picks, and then follow one of the plans above. I either pair by length (F5/FG), or by time of day. It’s always a good idea to separate the later games on tickets to allow for chances to hedge and guarantee profit.
Tailor your game to your own account and expectations. I bet small relative to stack, and bet smart. If you can’t make money with $100, what makes you think you’ll make money with $10,000?
I never risk more than 1% on any outcome, and never bet more than 2% on any given night. I scale those numbers down into losing streaks, and increase them again as the wins roll in.
A very special thanks to https://fangraphs.com (where I’m good for at least twelve million clicks a season) for helping me scrape and mine this data to determine all of these formulas .
Let’s get it.
Baseball Twitter's Robin Hood. Lifetime baseball fan, avid capper and fantasy baseball player. 15+ years of stock trading experience, specializing in risk management and advanced game theories. Creator of the MLB Moving Averages algorithm in 2018