The Mets are off to a rocky start, and the fanbase is splitting. The recent sweep of the Braves have alleviated many fears, but there are still Mets fans that are hitting the panic button, while others are asking for patience. The remainder sits somewhere in the middle. Personally, I fall in the “patience” group but rather than write another article showing that “clutch” hitting is a small sample size issue that generally regresses to the mean or that the Mets do not in fact lack grit/leadership/heart, I decided to explain how I watch a Mets game.
I’ve heard from certain fans of football, basketball or hockey, that baseball is too slow for their liking. While the other sports involve—more or less—constant movement and action, baseball moves at a more methodical pace. The reason I don’t feel that baseball is a “slow” is because of the constant mental game being played on the field. Real baseball fans just don’t watch the game waiting for a run to score. Real baseball fans are realize, consciously or not, that every pitch is a result of previous data; does the batter hit sliders well? Does the batter like the ball low? Is the batter getting the timing down on a pitcher’s fastball? Where does the batter tend to hit the ball? The answer to these questions results in defensive positioning and pitch selection. When I watch a game, I’m constantly trying to guess where the pitcher is going to throw the ball and what pitch the pitcher is going to throw. Obviously, there is much more to the game but I think most readers of this site are aware of them. So, instead of continuing on about the intricacies of the game, I thought I would pull together some data from articles I’ve read that will help you to watch the game like a Geek.
The start of every play begins with the pitcher throwing the ball. However, before the pitch leaves the pitchers hands, you can have a hint of what the result will be. Recently retired Josh Kalk wrote a very interesting article last February with some in depth analysis of pitch selection. Using Pitch f/x data, Kalk calculated the results of seven different pitches when a specific pitch preceded it. For example, how well a changeup works after a fastball as opposed to a slider. I will only present that data for the fastball, but I strongly suggest you read the rest of the article; it’s awesome. Anyways, here are the results for the fastball being the preceding pitch:
From the data—negative is good—we can see that a changeup is actually the “worst” pitch for a pitcher to throw after a fastball, while the splitter—think J.J.—is the best. The reasoning could be that so many pitchers throw changeups after fastballs that hitters become used to seeing the combination, such that they are looking for it. The latter would explain why following a fastball with another fastball is actually a pretty good combo. Like I said, I’m not going to go through all the data presented by Kalk, but I will point out some of his more interesting findings.
- The fastball is the only pitch that makes every pitch after it more effective.
- Throwing a curveball followed by a slider or vica versa is generally a bad idea.
- Throwing any pitch after a changeup is less effective unless it’s another changeup, it’s not too often a pitcher double ups on the change.
Of course a pitcher has to pitch to his strength, but having an idea of how exactly certain pitches interact with each other makes watching the game more fun (to me at least.)
Another aspect of the game that I constantly consider is the count. Back in 2006, TangoTiger, currently a Seattle Mariners consultant, ran a quick study charting the wOBA of the average hitter after a certain count is reached. This is not to say the wOBA of a hitter that swings with the count 2-0, but, rather, the final result of a plate appearance that had a 2-0 count at one point. Here are TangoTiger’s results:
Through Count wOBA
Through 3-0 0.570
Through 3-1 0.490
Through 2-0 0.443
Through 3-2 0.403
Through 2-1 0.372
Through 1-0 0.371
Through 0-0 0.332
Through 1-1 0.314
Through 2-2 0.290
Through 0-1 0.283
Through 1-2 0.237
Through 0-2 0.212
Now, it’s pretty intuitive that less strikes and more balls generally leads to better results, but this chart shows exactly how much better it is to be in a 2-0 count over a 1-1 count. Also, the conventional wisdom has always been that first pitch strikes are the most important strikes, some have argued that the second strike is more important the first pitch strike. The reasoning being that the difference between the counts being 1-0 as opposed to 0-1 is not as big as the difference between the counts being 2-1 as opposed to 1-2. Thus, when watching the game, don’t just hope Perez throws a first-pitch strike, but hopes he gets to two strikes before two balls.
Lastly, after the pitch is selected and batters have had their counts, there is a game situation. Back in 2002, Tangotiger created a chart showing expected runs given certain situations which are presented below. (The data is a bit outdated but this chart really held a lot of weight when I first saw it.)
0 1 2
Empty 0.555 0.297 0.117
1st 0.953 0.573 0.251
2nd 1.189 0.725 0.344
3rd 1.482 0.983 0.387
1st_2nd 1.573 0.971 0.466
1st_3rd 1.904 1.243 0.538
2nd_3rd 2.052 1.467 0.634
Loaded 2.417 1.650 0.815
With this data in mind, it becomes easier to accept, though not less frustrating, when the Mets don’t score with bases loaded and two outs. On average, less runs score in the latter situation than when a guy is on first with none out. This data is also proof that giving up an out to move a guy over to second is not generally a wise idea (in certain situations it is, such as late in the game or the pitcher up.)
Armed with this information in one convenient article, you can now watch the next Mets game like a Geek with all this data on your mind. Strike one to Wright, no matter, it’s not the worst count he can be in. Johan threw a changeup, surprise the hitter, and throw another one. Frankie gave up a double with two outs, no worries; he’s still less likely to give up a run than when the inning began. Some might argue that constantly thinking about these numbers ruins the fun of being a fan, but to me, its part of the fun. Especially because baseball isn’t a math equation; while the numbers say otherwise, a two out rally can be started, Castillo could hit one over the wall or Delgado could drop a pop up that he catches 999 times out of 1,000, if anything I’m more excited/frustrated than the average fan because I realize how unlikely it is for these events to happen.