As the weird Australian opening series has come and gone and the actual “Opening Day” is quickly approaching, I am left wondering what on earth I am going to do about this baseball fund idea. The concept makes a lot of sense in theory, but there is also quite a bit of bullshit, smoke, and mirrors in that I am basically making all of this up as I go along. For that reason, I am not sure that it is worth it to invest any more of my time (or yours) on the idea.

## The Shortcomings of My Model

In Trading Bases, Joe Peta points out three types of adjustments he made for every game. He took the detailed time to adjust each team’s expected wins based on how every lineup change would impact an entire season. He also adjusted each team’s expected wins based on the starting pitcher, and he made an adjustment for home field advantage too.

Peta suggests in the book that his model could probably still be successful without adjusting for daily lineup changes, and incorporating the home field advantage adjustment would be relatively simple. The issue for me is the starting pitcher adjustments. The way I adjusted the model in the previous baseball post was silly.

In his book, Peta takes the difference between the teams expected runs allowed and the runs allowed that could be expected if that day’s starting pitcher were to start every game. This is where the adjustment is going to get a bit more complicated. I can’t just add a pitcher’s WARP to the team’s bottom line. I need to account for the difference between the pitcher’s WARP and the cumulative WARP of the team’s starting rotation.

## Trying To Understand Starting Pitching Adjustments

Using this idea, rather than just adding Shelby Miller’s 0.4 WARP to the overall expected win total of the St. Louis Cardinals, I need to compare the combined WARP of the Cards’ five starters to what would happen if Miller started every game. The combines staff has a WARP of 8.7, which is mostly a product of Adam Wainwright being a badass. If Miller were to start every game, the staff would have a WARP of 0.4 x 5, which is 2. Therefore, games Miller starts represent 6.7 less wins than expected over the course of the season. That would adjust St. Louis to a 81.3 win team.

Applying the same logic to John Lackey and the Boston Red Sox, we find that Lackey represents an adjustment of -5.6 wins compared to the rest of the Red Sox staff. This makes Boston an 82.4 win team.

Converting those win totals to implied odds, we find that for this particular matchup, St. Louis is a .502 win team and Boston is a .509 win team.

Using the formulas that are provided in Trading Bases, we can adjust each of those expected winning percentages for the year to show us what we can expect from this particular game. To do that, we multiply each teams winning percentage for the season by (1-the other team’s winning percentage). Then, we divide each of the numbers we get by the total of the two numbers to adjust the probabilities to equal 100% because someone is going to win the game.

What we get is a .492 probability that the Cardinals win and a ..507 probability that the Red Sox win.

## Home Field Advantage

The next adjustment that we need to make is for home field advantage. In Trading Bases, Peta breaks down all of the reasons support 8% being the value of playing at home. In our example, I don’t remember which team was the home team, but based on the fact that Boston was used second, they were probably the home team, so we’ll use them. (Since this is a spring training game, home field is probably far less significant, but it’s just an example.)

We will add the 8% home field edge to Boston by simply multiplying their win total by 1.08. Since we started with two numbers that added up to 1 and adjusted one, we can simply subtract the new expectancy for Boston from 1 to get the new expectancy for St. Louis. This gives us a win expectancy of .453 for St. Louis and .547 for Boston. As you can see, adjusting for home field advantage turned a slight edge for St. Louis into a slightly bigger edge for Boston, but we still have basically a toss-up scenario.

## Comparing Our Newly Adjusted Numbers

Remember from the previous post that Vegas had placed odds of .523 for the Cards and .565 for the Sox compared to our calculation of .470 for the Cards and .530 for the Sox.

In order to find discrepancies between implied odds and money lines, we will just subtract the money line odds from the implied and adjusted odds we calculate. This will show that the Cardinals have a -.711 advantage and that the Red Sox have a -0.179 advantage. In this situation, it appears that neither team is undervalued, so we would pass on this game.

## The Time Factor

When I started writing this post, I believed that the process of writing it would convince me that I was way off base (pun intended) trying to put this thing together and that I would not be able to find a simple way to calculate the probabilities. True and False.

I believe that all of the adjustments we have made to this example were correct and make sense logically. However, it has taken me more than an hour to work through the calculations and write the post. I need to be able to do these calculations quickly for every game, every day. I can’t spend 15 hours every single day working on baseball betting.

The next step for me is going to be building an Excel spreadsheet that will allow me to simply punch in each team and their numbers every day and spit out results on the back end. If I can build something like that, using the math discussed above, I can probably get the process down to a reasonable amount of time required each day.