As one door closes, another one opens…
As the MLB regular season begins to wind down, a lot of our attention will shift focus over to the NFL. Until the MLB postseason is underway, the majority of America will be dialed in to the NFL after the annual long-awaited start that is Week 1 of the NFL season. To catch everyone up to speed, the San Diego Padres are vastly under performing based on preseason expectations whereas the San Francisco Giants are out doing themselves based on preseason oddsmakers. The preseason Giant’s win total was set at a whopping 75.5 games won with a 0.1% chance to win this year’s World Series. The Giants eclipsed this total on August 15th against the Rockies. They went OVER their win total with almost two months left in the season; let that one sink in. I digress… listed below are the current World Series winner odds for the 2021 MLB Postseason.
Currently, the Dodgers are set as the leading favorites with the addition of future Hall of Fame pitcher Max Scherzer. Odds aside, come postseason time it is in we as sports gamblers best interest to make our plays on teams that are so called HOT; or at the very least, not bet against them.
As we approach the postseason, it is important to pay attention to the forecasted weather in each participating city since it in fact will be October. Obviously the month of October is absolutely irrelevant to Tampa Bay’s dome stadium when it comes to weather having a potential affect on the game and its outcome. However, weather might be an important factor to consider when making plays on games in New York or Boston. Coming from personal experience, cold weather situations grant the pitcher with the competitive advantage over the hitter 100% of the time. Why? You might ask. When temperatures are considered “cold” the batter is at a disadvantage because of two reasons: first, hitting is a very technical and exact science that is very difficult to master. This already very difficult “art” becomes much more difficult when the batter is having a tough time feeling his bat, body movements, etc. Second, typically the pitcher has been going at it for a handful of innings by this point allowing him to be both in a rhythm and relatively warm. On the other hand, the batter only gets to step in the batters box every ninth hitter and they may not see the ball hit to them for an inning or so. These factors make it difficult for the batter to get their bodies primed to hit a 95 MPH fastball. Think about it – it would be like racing someone of equal speed as yourself but they get to warm up and you do not, you and I both know who is winning that race. The same goes for the pitcher and batter match-up’s. This was the long version of how cold weather gives the pitcher the advantage while the batter is at a disadvantage throughout cold weather MLB games. One last note on postseason baseball; the odds makers and public gamblers tend to overvalue the home team. During the dog-days of summer travel and jet-lag are genuine factors. However, come postseason baseball for these guys the travel factors are much more minute based on the importance of each game, allowing adrenaline to take care of this void.
Making my NCAAF plays the other day, a theory dawned on me that I would like to think could give me a competitive gambling edge that I am eager to share with all of you. We’ll call it the NCAAF Chain Reaction Theory. Note: This theory/edge only works for NCAAF, not NFL. I believe that NFL teams are much more consistent than NCAAF players/teams making the “regression” portion of the theory less significant. In short, the chain reaction theory strives to predict a NCAAF O/U outcome. My inspiration behind this proposition ties into the regression theory and/or linear regression (famously noted from the film Moneyball). The regression theory explains how a player and/or teams immaculate or faulty performance(s) typically come in the form of luck or chance.
Hence, previous outcomes will balance themselves out and be followed up by a performance or trend that differs from one(s) of the past. My theory is somewhat similar (but obviously different). The regression theory applies to all sports whereas mine (as previously stated) applies only to NCAAF. The easiest way to explain this assumption is by taking both teams’ past performances as opposed to a singular team/player’s past performance that the regression theory attempts to explain. Week in and week out there are countless of examples to back this theory up. If we take Team A and Team B Week 2 O/U totals and notice that both teams scores fell under the same OVER or UNDER category, then one would assume that the opposite would be much more likely to occur for Team A and Team B as they both faced off the very next week. Let me give you a real life example: Buffalo @ Nebraska (54.5 O/U) went UNDER. Both teams’ previous match-up’s ended with the total going OVER. I believe that this could be the theory of regression’s “big brother,” if you will. Take a look for these kind of “spots” in your Week 3 NCAAF plays.
My Week 3 NCAAF Picks:
VIRGINIA TECH (+3) @ WEST VIRGINIA – PICK = VIRGINIA TECH
NEVADA @ KANSAS ST. (+2) – PICK = KANSAS ST.
MISSISSIPPI ST. (-165) @ MEMPHIS – PICK = MISSISSIPPI ST. (ml)
SOUTH CAROLINA @ GEORGIA O/U 48.5 – PICK = UNDER
AUBURN @ PENN ST. (O/U 53) – PICK = UNDER