Analyzing Game-to-Game Offensive Output

Thanks to everyone that reads the site it means a lot to me that I can say the stuff I’m putting together is important to at least a few souls out there. With the site being active off and on and busy work schedules making it difficult to feed you the information you desire it’s been sort of an up and down road, but I’m happy to say that this month of April saw the most traffic since last May. Thanks for stopping by and I hope you continue to utilize this resource. Now on with the program.

Here’s the most recent update of our batter line totals for the year:

Not a whole lot has changed and as these samples get bigger you’ll see changes become even more minute, but the nice thing is that we can put even more credibility into this stuff as the season goes along. In my last post I looked at each player’s wOBA and xwOBA evolution over the course of the season. In this one I wanted to take a look at how the team, as a whole, has fared throughout this young season. I won’t bore you with the nitty gritty, but it’s now an easy process to calculate wOBA and xwOBA for any game or for all games. Here’s what that looks like:

The dots show the actual wOBA (awOBA) and expected wOBA (xwOBA) for each game. The lines take some of the volatility out by imposing a 5-game rolling average. This should give you a solid idea of whether we over or under performed expectations on a given day and over a longer time period. You’ll notice that the start of the year saw us beating expectations before going through a prolonged slump. We dug out of the slump by hitting very well, basically to date, before dipping just slightly back below mostly due to these past two games not approaching the pace.

I wanted to take this a step further. Since I’ve been comparing wRAA and xwRAA in the initial chart, I wanted to do the same on a daily basis. I also added in what I’m calling true runs to avoid confusion:

The confusion stems from my use of the word “actual” for the blue dots and lines. It’s a bit misleading because aRuns shows how many runs we SHOULD have scored based on our wRAA on a given day plus the 4.20 MLB-average runs per game. Meanwhile, xRuns shows what our xwRAA would look like added to the league average and Runs shows what we actually scored on that day.

This can be a good way to gauge some of the volatility in our offense. While xRuns shows what you would expect on a given day based strictly on the matchups, we all know that sometimes an offense exceeds expectations and vice-versa. Additionally, it’s not like 4.20 runs per game is some achievable figure in a given game.

So here we see that aRuns and xRuns pretty much follows the course of our wOBA figures with small differences due to plate appearance weighting each game. The best comparison to make here is to contrast aRuns with Runs to get an idea of where our offensive output is commensurate with what the team should have scored. The first 17 games of the year show true Runs being slightly below how many the team should have scored, across the board. There’s a short flip flop where the team actually exceeded what we should expect, before the last few games have dipped back below.

By aRuns the Rays should have scored 125.5 runs on the year which shows that our awOBA has been around 5.5 runs better than our xwOBA, but our true Runs scored is only 112. So while we’ve exceeded expectations from a wOBA standpoint, it hasn’t fully translated to true runs scored in games. This works out to basically half a run a game that we should be scoring based on awOBA that isn’t coming home. It’s tempting to chalk this completely up to ill luck, but with no precedence for this sort of research it’s a fool that seeks to take all the credit. Over the course of the season we will continue to learn how close this stuff is to predicting actual runs and any systemic flaws should flush themselves out. From this limited research, it looks like the Rays offense is performing quite well, if a bit better than should be expected, but it isn’t quite translating to production of true Runs, quite yet.

At the end of the day, all that matters is true Runs up on the scoreboard, but if the Rays can continue to follow their sound processes there will come a time when the offense can do even more to help carry this beleaguered starting staff.

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About Jason Hanselman

Rays fan.
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One Response to Analyzing Game-to-Game Offensive Output

  1. Pingback: Pitcher Expectations and Trends |

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