The purpose of Webbie’s Odds is to get a neutral, non-biased line that can be then compared to sportsbooks’ odds. The goal is to create value based on what a number should be based on “true” team value compared against a number that has expectations of the betting market involvement. Sportsbooks have risk they have to account for so their numbers are biased to protect against too high of a volume of risk on one side. Ultimately, there can be many various reasons odds get moved around such as money coming in on series prices, run lines, first five innings, and other reasons, but for our purposes, we’ll just use risk mitigation on the money line.
Webbie’s Odds are created using a base formula originally produced by Joe Peta, author of Trading Bases. If you have not ever read this book, I highly recommend it. Joe was a Wall Street trader who was injured in an accident. He was unable to continue his Wall Street job while rehabbing his injuries. He is a baseball fan. He applied Wall Street stock market formulas to MLB odds. He then used sabermetric data to ultimately create the most accurate line without bias available. Webbie’s Odds are these exact odds, with slight variances that I believe are significant to having the most accurate odds based on team and starting pitching strengths available. So, if we can agree on that, then we can dive into how to use Webbie’s Odds.
First, let’s look at 2022 Opening Day now that we have some odds from sportsbooks. I am using MGM’s line.
A couple of notes, BOS/NYY and SEA/MIN have been moved to Friday 04/08 as the weather conditions merited cancellations. You will the games listed by Away and Home teams then start times. I try to keep with the order of start times and then rotation numbers to make wagering simple. Next then is rotation numbers. Next is the away team listed pitcher, then the home team listed pitcher. Pretty standard to this point.
In the dark blue section, we see Webbie Line and Implied Probability. When I make the odds, the results are generated in a probability format. Ultimately, my odds tell me the probability one team will have to win this game. I then have to turn that into a money line. There are many calculators you can use for this but my program is written with the formulas so the transition to a money line is transparent. Let’s look at MIL @ CHI rotation #969 and #970.
Webbie’s Odds are listing MIL Burnes at -204 and MGM lists it at -160. The implied winning percentage for MIL is 67.07%. It means that MIL should win this game with Burnes on the mound against Hendricks 67.07% of the time. A simple way to gauge value is to subtract the higher odds line from the lower one to get the variance. In this case, MIL -204 less MIL -160 makes -44 variance. Determine which side is getting the benefit of the odds number. Here we see MIL should be -204 but we can bet them at -160 creating -44 points of value in the money line. MIL is the right side. For every 10 points of variance, 2.3% of the value is created. That calculation comes from knowing the implied percentage of -100 (50.00%) and -110 (52.38%). We take 4.4×2.3=10.1. It means that we are gaining 10.1% of value by wagering on MIL against the odds posted by MGM. Beautiful! We do this for every game and then make wagers accordingly. Now you can truly calculate EV+.
The above Webbie’s Odds shows the complete form. The royal blue section is Webbie’s Odds and the open line, AM line, and PM line moves. The navy blue section is the Totals section. Again, the same format where I have Webbie’s totals, the opening totals, then the AM number, and the PM number. I derive the totals from a concept of what it takes to score runs. On average, it takes 2.5 hits plus BBs to score one run in an MLB game. I have written a program that calculates predicted results based on many data points such as the past 30 days, pitcher versus an opponent, situational outcomes, pitcher versus the lineup, plus similar attributes for the bullpen.
I hope you like Webbie’s Odds. It did take much work to generate but I found them to be extremely valuable.