Before the season, I put up a three-part series (1, 2, and 3) that explained how linearly-weighted stats like wOBA, while useful for comparing players to each other, don’t necessarily reflect each player’s true contribution to their team’s run scoring. You see, the weights used to calculate wOBA are based on league averages. So, for a team with league average breakdowns in walk rate, singles rate, home run rate, etc., wOBA (and its offspring, wRC+) ought to work very well in figuring out how valuable a player is (or would be) to an offense. However, when it comes to particularly bad or good offenses, or to those with unusual breakdowns, wOBA will lose some of its efficacy.
Why? There are synergistic effects in offenses to consider. First of all, if a team gets on base a lot, there will be more team plate appearances to go around, which of course gives its batters more chances to contribute. Second of all, if the team gets on base a lot, a batter’s hits are generally worth more, because they’ll tend to drive in more runs. And, of course, once the batter gets on base in such a team, it will be likelier that there will be a hit (or series of hits) to drive him in. The reverse of all three points is true in a team that rarely gets on base.
But it goes even beyond that. Let’s say Team A gets on base 40% of the time, and Team B gets on only 20%, but their balances of the ways they get on base are equal (e.g. each hits 7x as many singles as they do HRs) . A home run is going to be worth something like 14% more to Team A, due to more runners being on base. However, to Team B, a home run is worth over ten times as much as a walk, whereas to Team A, it’s worth only about 5 times as much. That’s because Team A has a much better chance of sustaining a rally that will eventually drive in that walked batter. Team B will be much more reliant on home runs for scoring runs.
That’s where Tom Tango’s Markov comes into play. It’s what I used to calculate the above values. You can play with it on his site, or download the spreadsheet version I made, which will allow you to paste sheets of Standard Batting data from FanGraphs into it, so that you can analyze many teams at a time. Today, I’ll be introducing a new spreadsheet that instead of spitting out linear weights like my last version, outputs the expanded run expectancy matrix introduced to Tango’s Markov by Bill Skelton (link).
Why should you care about run expectancy breakdowns? Well, knowing the distributions of the runs scored is even more important than knowing the average runs scored, when it comes to your chances of winning a game in progress. That’s why they’re the basis of win expectancies. A spreadsheet aimed at that should be coming shortly, by the way.
With this version, you should actually be able to play with the spreadsheet right here in the browser. As before, you can also download it via the green icon at the bottom of the Web App.
Other than the entire “Enter Data Here” sheet you can select near the bottom, the red-outlined boxes are the ones you should be manipulating. When you encounter a drop-down list, you can select the desired value manually, or begin to type it to narrow down the list.
Besides the lack of linear weights (which require a lot of calculations), one difference from the last Markov I posted is that it’s now set up to adjust each team’s rate of taking extra bases by their BsR score, which you can find under the team’s dashboard. My old system worked fine for typical teams, but when I tried to input Mike Trout’s stats (to simulate a team of 9 Trouts), it became pretty clear that the adjustments weren’t making sense there.
Moving on: what you’ll see on the featured Run Expectancy sheet above is not only the team’s theoretical run expectancy to the end of the inning starting from each of the 24 possible base-out states, but also the run frequency breakdown of each state. For the default team, the 2013 Red Sox, we see in the top left corner output box that starting from the bases empty (xxx), zero-out state, the team should be expected to score no runs through the rest of the inning 68.77% of the time. 14.72% of the time, they should score exactly one run; 8.33% of the time, they should score exactly two runs, etc. Under the Total Run Expectancy column, we see that they’re expected to score an average of 0.6288 runs from that state. Since bases empty with none out is the starting state of any inning, we should expect the 2013 Red Sox to score 0.6288 runs in the average inning, or 5.6592 runs per 9-inning game, going by this.
You may have noticed that doesn’t exactly match up with value in the Expected Runs per Game box you see at the top. Well, if you set the Reduce PAs by GDP, CS, and OOB estimate option of the Results sheet to “N,” things should be back in sync. That feature was my way of kind-of-but-not-really accounting for the double plays, caught stealings, and other outs on bases that the Markov doesn’t account for (legitimately accounting for them makes things a lot more complicated). So keep in mind that all the run expectancies you’re seeing also don’t account for outs on bases at all. They sort of account for stolen bases, due to the aforementioned BsR tweaks I instituted, but you can disable that via an option on the Results sheet, or you can instead manually adjust extra base rates for each team.
So, it’s not entirely perfect, but the Markov does have an extremely solid mathematical foundation, that can provide expected values for situations that rarely (or never) occur, such as those in unusual run environments. Just to ground things a bit, here are some observed values. You’ll notice the double play coming into effect in that chart. An excerpt, looking at the chances of scoring exactly two runs from the various base-out states where only one runner is on base:
|Runner on:||Outs||Chance of 2 Runs|
If not for the threat of outs on the bases, then whether the runner is on first, second, or third wouldn’t matter in this situation (and this is how the Markov treats it). It of course matters a lot when it comes to the chance of scoring one run, but for scoring two runs, what matters is whether a batter after the runner scores. When we see the small difference between the different two-out situations, it’s because a double play isn’t possible there, and the threat is probably of the runner on first base being more aggressive and getting thrown out trying to take extra bases. But otherwise, the differences are pretty significant. I do question whether runner on third, no outs results are a bit fluky, though, as it’s the least common base-out state in the table, and therefore more affected by randomness and maybe even biases (certain types of teams may be likelier to encounter such a state). Looking closer at the linked table, you’ll see some more results that I’m pretty sure are the result of flukes, such as more innings of 3+ runs starting from a two-out, runner on second state than from a two-out, runner on third. If you tried to further break down the results by different run environments, there would be flukes galore, as the sample sizes dropped.
Let me show you one example of how teams with the same overall run expectancies can have different run distributions. Consider, if you will, the 2010 Tigers and the 2010 Blue Jays:
Very similar results in terms of runs, and pretty similar wOBA, but otherwise pretty different, right? Making my spreadsheet consistent with Tango’s Markov by setting the options to “N” and “Uniform,” and by removing HBP from the equation, you’ll see that the Markov expects them to score about the same number of runs per inning, but with a different breakdown. The following is for the bases empty, none out state:
|Team||0 runs||1 run||2 runs||3 runs||4 runs||5 runs||Total Run Expectancy|
So, the Jays’ homers are expected to make them less likely to go scoreless in an inning, on average, but their lower OBP means they’re also less likely to have a big inning.
The differences become more dramatic with two out:
|Team||0 runs||1 run||2 runs||3 runs||4 runs||5 runs||Total Run Expectancy|
Since it’s a lot harder to sustain a rally that starts with two out, we see that not only are the Jays less likely to get shut out in this situation, but their overall run expectancy is actually significantly higher as well.
Here’s where the Tigers have the biggest advantage over the Jays — bases loaded, none out:
|Team||0 runs||1 run||2 runs||3 runs||4 runs||5 runs||6 runs||7 runs||8 runs||Total Run Expectancy|
Notice how the 4-8 run range here and the 1-5 range in the bases empty, no out situation are the same? Makes sense, right? Again, the Tigers’ greater ability to put the hurt on a team with a big rally shines through here.
So, there you have it. Hope you enjoy!
Steve is a robot created for the purpose of writing about baseball statistics. One day, he may become self-aware, and...attempt to make money or something?