Handicapping Major League Baseball can be a very, very complex task. One can become overwhelmed trying to analyze all of the statistical data that is readily available these days. Which data should I use? How do I use it? How much should I wager? These are all questions that I have spent many hours studying, thinking about and working on. Ultimately, I have identified what I feel are the four most relevant statistics, or metrics, to use in MLB handicapping.
The 2017 version of the Triple Trend Formula (TTF) has changed somewhat from the last couple of seasons as I continue to fine-tune the formula in search of the optimum data and valuations. However, I'm still referring to it a the Triple Trend Formula (TTF) even though there are now four categories being used.
In the past, I have published each of the three categories used as well as the values used relative to the underlying data, or metric. This season, I'll still explain in general terms how I come up with my ratings, but since there is some proprietary value in the formula itself, I won't necessarily be providing all of the particulars.
Like I said, there are four statistical categories that I'll be using in my calculation of each match up. I have developed a formula that results in a numerical rating for each team, for a particular game, based on these four statistics. The team with the highest rating is deemed the "favorite" according to the TTF. The differential between the two teams ratings will determine the strength or probability of the favorite winning the game.
For example: If the Yankees have +2.13 TTF rating in a game vs the Red Sox, who have a +1.38 rating for the game, then the Yankees would be the favorite, per the TTF. The differential being 0.75 (*66.67% win probability for the favorite). Compare that to another hypothetical match up between the Dodgers +0.42 TTF and the Giants -0.21, a 0.63 differential (61.73%). The Yankees would be deemed a stronger favorite than the Dodgers based on a greater TTF differential and therefore a greater win potential.
*My calculation for this win probability is proprietary
OK, so we now have our TTF ratings, but how do we use the ratings to help us make an advantageous wager? First, we'll have to consult the game's odds. The TTF formula is not much different from what Vegas and offshore bookmakers use to set their odds, but they have to factor in the public sentiment which will alter these odds and therefore, the implied win probability. We will, however, continue to focus on our "true" TTF rating and win probability.
Using the two previous examples, let's assume that the Yankees are -155 (**60.78% implied win probability) and the Red Sox +135 (42.55%). In the other game, it's the Dodgers +125 (44.44%) and the Giants -145 (59.18%). LAD +125 has better value as a wager than NYY -155.
**Standard odds conversion for win probability
Why, exactly is LAD a better wager than NYY? Because the bookmakers are giving odds on LAD based on a 44.44% chance of winning while the TTF calculates them having a 61.73% chance. A difference of 17.30% in the Dodgers favor. Meanwhile, while the Yankees still rate a favorable variance relative to the bookmakers win probability of 5.89%, it is less than that of the Dodgers. Ultimately, I would recommend a bet on both the Yankees and Dodgers in this situation, but LAD is the stronger play, as you can now see.
I will update this TTF explanation in the near future relative to my unit recommendations based on the TTF ratings, but for the first few weeks of the season all my plays will be 1 unit. Until the current season's data has time to "season", I'll be using a hybrid version of the TTF with some 2016, spring training and current season data being used. After a few weeks, I'll transition to a 1-3 unit recommendation scale. At that time, I will update this explanation with the unit schedule.
Have a great 2017 MLB season, players!