We have written an article to explain our unique approach to Major League Baseball. AccuScore MLB 101 was written to explain to you the basics of of MLB Betting...the AccuScore way. By the end of the 101 you will know how AccuScore MLB doubled, tripled, and even quadrupled our client's bankrolls since 2006 and understand how we have won over 13,000 units since 2008.
Table of Contents STEP 1: UNDERSTANDING THE MONEY LINE
STEP 2: TRANSLATE ODDS TO WINNING PERCENTAGES
STEP 3: SIDE VALUE
STEP 4: Side Value vs. Money Line Bets
STEP 5: MONEY MANAGEMENT AND RISK
STEP 6: HIGH CONFIDENT PREDICTIONS?
STEP 7: Building Your Own System
STEP 1: UNDERSTANDING THE MONEY LINE
First, understand what you are betting on. Most of you are familiar with betting on point spreads in football and basketball. While you can bet on Run Lines in MLB, which are comparable to point spread bets, most people wager on the Money Line in MLB. STEP 2: TRANSLATE ODDS TO WINNING PERCENTAGES
AccuScore simulates every baseball game one at bat at a time. It repeats the game simulation 1000s of times to generate precise winning percentages for each team. So in the end our Game Forecast will show you each Team’s winning percentage (those are those moving arrows you see in our nifty graphics). But how does a team having a 75% chance of winning compare to that team being a -220 Money Line Favorite? Here's how we translate the odds. If you need to risk 110 to win 100 on Boston we interpret this to mean that for ever 110 losses, Vegas expects Boston to win 100 times. So their winning percentage is 100 / 210 or 47.6%. If you have to bet 130 to win 100 on the Yankees we interpret this as for every 100 losses, NY will win 130 times. Their winning percentage is 130 / 230 = 56.5%
You will notice that 47.6% + 56.5% is greater than 100% (104.1%). Vegas will make money off commissions (“the Vig”) and your losses. They set the Money Line odds such that you need to go much better than 50/50 on your bets to make money. To account for the Vig in our translation we divide each team’s Vegas Odds Based Winning % by the total of the two teams (104.1%).
Boston's Vegas Odds Based Winning Percentage is now translated from -110 to 45.7%. New York’s Vegas Odds Based Winning Percentage is translated from -130 to 54.3%. STEP 3: SIDE VALUE
Now that we've translated Money Lines into Vegas Odds Based Winning Percentages we can make an apples to apples comparison with our Simulation based Winning Percentages. Our Vegas Win % had Boston winning 45.7%. Say Boston won 48.2% of simulations (SIM WIN % = 48.2%). We compare these numbers and see that the SIM WIN percentage for Boston is HIGHER than the Vegas Win %. This means Boston has a better chance of winning than the odds makers think they do. Because Boston is doing better in simulations than expected, we predict that Boston has a higher Side Value (the Boston Side of the bet has more value than the Yankee side of the bet, hence the term “Side Value”). So even though Boston is winning under 50% of simulations our Side Value prediction says to put money down on Boston. However, the Yankees are winning > 50% of simulations. If you want to make a bet using "traditional thinking" then you would bet on the Yankees because they are winning a higher percentage of simulations. Is Side Value better than traditional Money Line bets? STEP 4: Side Value vs. Money Line Bets
The Advisor reports how you would have done if you followed AccuScore’s predictions. For example, AccuScore's SV YTD is +146 for the Washington Nationals. This means that for Washington Nationals Road Games AccuScore's predictions have won +146. This DOES NOT mean that Washington was or was not the Side Value pick, it only means that whoever AccuScore picked in those games, the outcome was right enough to make a profit.
While this is only an example, you see that the Money Line YTD for Washington Road Games and Houston Home Games is +578 combined, while the combined SV YTD is -110. In the past 14 days (ML14, SV14) the same pattern holds. Advisor members can save the time and look at the overlaying trends and star rating to have the information presented in a more concise manner In this sample the SV is with WAS, but ML is with HOU. Advisor also shows that overall, AccuScore's SV Return when the Home Team is winning 50-59% of simulations as it is here is a whopping +7586 while it is just +2245 on Money Line predictions. Overall, Side Value ROI is substantially higher than ML. Ultimately, the final decision is YOURS AND YOURS ALONE. AccuScore gives Advisor the star rating factoring in all this research, but we suggest playing within the parameters of your system in making your decisions. We suggest you invest with consistency throughout the MLB season to limit variance and get the most consistent results from our forecasting data. STEP 5: MONEY MANAGEMENT AND RISK
More than any other major sport, MLB has more cases of teams ending in last place one year and coming in near the top of the division the next year. While NBA and NFL Favorites win well over 60% of games and College Football and Basketball Favorites win over 70% of the time, MLB Favorites only win 56% of the time. In 2006, we did an analysis that excluded even money games. If you had risked 100 units on every Favorite you would have won 56% of your bets, but because these favorites ROI are under 100 units per you would be down -9498 at seasons end. **In 2007, favorites won 57% of the time, but yielded a return of -2242. However, if you had bet on all the Underdogs you would have won just 43.8% of bets, but the greater payout per bet translated to a +4242 Net and a +6035 on Home Underdogs. While this might fluctuate year to year, you can look at the overlaying trends on Advisor to trim the fat off our selections and develop a system with a higher predisposition to winning. The good news from this analysis is that it does seem that betting on MLB Underdogs is as close to a “sure bet” as it gets in sports betting. HOWEVER, as great as the +4242 sounds, to make this much profit you really need to play a ton of games. It requires a commitment to daily betting and an acceptance that betting on MLB can be a "war of attrition". AccuScore's goal is to make this war of attrition even more profitable by pointing you towards the best 'Dogs and the good Favorites to bet on and making it easier to determine what bets to make through Advisor.For example, if you had used AccuScore's side values in 2007, you would have won 54% of the time (down 3% from how the favorites faired). Despite having a lower win percentage, side values returned 10,195. It is our opinion that no bet in MLB is substantially better than any other bet, so we advise risking the same amount on virtually all bets. You have to expect to lose more bets than you win when going with Underdogs (Side Value and Money Line Dogs) but based on past experience you should win enough to turn a profit – how many gamblers can do that consistently? STEP 6: HIGH CONFIDENT PREDICTIONS?
We promote a systematic investing approach with MLB. It is important to know that predictions with bigger differences between Simulation Percentages and Gambling Odds Percentages DID NOT perform better than predictions with smaller differences. In Football and Basketball many members make the mistake in relying just on the game forecast. This is a mistake. They have higher confidence in predictions where a team is covering the spread in 60% of simulations over teams that are covering 53% of simulations. In general, this higher % = higher confidence has worked. In baseball, this has not worked as well. Why? It has to do with the range of possible scores in MLB. The average MLB team may score 4.5 runs per game, but it is not shocking to see a team get shutout or score over 10 runs. When's the last time you saw an NBA Team score 190 pts in a game? How about an NBA Team getting shutout? The answer is never. The average margin of victory was over 3 runs or around 30% of the total runs scored each game. However, in simulations teams lose some and win some. A team's average margin of victory in simulations is typically under ½ a run. While the average margin of victory in simulations is around 3 (just like reality), the average margin of victory for an individual team is around 15% of reality. Unlike the NBA or NFL where our forecasted score is often close to the real game outcome, we know heading into any give MLB game that the forecasted score is likely going to be "way off". After all we might predict NYY 5.5, BOS 4.8 and the Yankees could end up winning 6-2. We were "way off" because we predicted a 0.7 MOV and they ended up winning by 4 (more than 500% more than predicted). We hope and think that most would agree that our prediction was not "way off" because we predicted the Yankees would win. But because MLB scoring dynamics differ significantly from other sports we cover, we strongly advise not reading too much into how much higher or how much lower our simulation winning percentages are from the Vegas Odds generated percentages. Get the overall Side Value or Money Line Prediction from AccuScore Advisor, but build your systems within three and four star forecasts and look for the trends that might show a game to have even higher expected ROI value. STEP 7: The Advisor & Building Your Own System
The Advisor is the best source for up to the minute odds, consolidating all the hundreds of trends and situations AccuScore covers. If you are a first time user of AccuScore, we encourage the systematic approach in baseball opposed to the hit it big on one game theory. Hopefully the preceding segments clarify some of this with proven results. We plan to continue to publish articles that detail hot trends and the value of some systems over others. - We want our clients to take the time to fully understand AccuScore’s proven profitable approach and “how it thinks”
- We want you to ask questions (no question is too dumb)
- We want our clients to chime in with their thoughts, experience, strategies, systems, and more.
MLB allows us to set up a standardized method for analyzing sports bets and converting it into a methodical investment opportunity based on accurate play-by-play simulation data. It’s up to you guys to make the real $$ by getting the most out of the data.