Chase Rate Over Expected

Introduction

Over the past few years, interest in evaluating individual pitches using Statcast data has increased quite a bit. These models, especially the ones created by Eno Sarris and Cameron Grove, have become very popular and are commonly quoted when debating how good a pitcher is relative to others. However, while grading individual pitches and averaging to give an overall score for a pitcher’s arsenal is common, there has been relatively little work done on using individual pitch grades to evaluate batter performance. In this article, I want to evaluate a hitter’s plate discipline by comparing their chase rate against what the expected chase rate is based on pitch quality derived by Statcast data, with the difference being called “Chase Rate Over Expected”.

Methodology

The dataset I used consisted of all pitches thrown in the “chase zone” from 2018 through 2022. The Statcast data provided by Baseball Savant does not directly say if a pitch was in the “chase zone,” so I manually calculated it based on the dimensions defined by Tom Tango (cited below).

To model the probability of a pitch in the chase zone getting swung at, I used a generalized additive model (GAM), with a mix of smooth and non-smooth terms. The smooth terms are an interaction term of vertical and horizontal location and an interaction term for vertical and horizontal movement of the pitch, and the non-smooth terms are speed, spin rate, extension, the count, and a binary term that is 1 if the batter and pitcher are the same handedness, and 0 if not.

Results

Who was in the top 5 in chase rate over expected (CROE) last year, minimum 100 pitches seen in the chase zone.

Top 5

Name	Chase	xChase	CROE
Austin Barnes	11.1%	28.1%	-17.0%
Cavan Biggio	10.9%	26.7%	-15.8%
Max Muncy	12.4%	27.8%	-15.4%
Sam Hilliard	14.6%	28.2%	-13.7%
Triston Casas	11.5%	25.2%	-13.7%

 

Bottom 5

Name	Chase	xChase	CROE
Francisco Mejia	54.9%	27.1%	27.8%
Javier Baez	51.3%	25.5%	25.8%
Oscar Gonzalez	49.1%	25.7%	23.3%
Hanser Alberto	48.1%	25.6%	22.5%
Harold Ramirez	48.5%	27.5%	21.0%

 

How stable is CROE year over year? Regressing CROE from the past year to the next year, we get an R^2 of 0.58, which is a decent amount.

How stable is chase rate year over year? Fairly stable, but regressing previous year chase rate against the next season’s chase rate has a lower R^2 than CROE year over year.

Finally, can previous CROE be used to forecast next season’s chase rate better than chase rate from last year? This regression has a slightly higher R^2 than the one that uses prior season chase rate, indicating that CROE does better in forecasting chase rate than previous season chase rate.

Is it fair to give all of the credit to the batter?

One of the implicit assumptions with this methodology is that I am giving all of the credit for the residuals to the batter. If a batter is expected to chase 20% of the time and he only chases 17%, that 3% improvement is all due to the batter’s skill. This is a strong assumption. While I do not think the batter should receive 100% of the credit, because it is impossible to be that correct and the underlying xChase model has natural uncertainty, I do think the batter should receive most of it. The two main factors that the pitcher has control over, which are deception and tunneling, apply to relatively few pitchers that I did not think would drastically affect any one hitter’s rating. However, there are some interesting articles on the drawbacks of “over-expected” ratings in football, and I would encourage readers to check them out. I linked to Robby Greer’s post below.

Conclusion

Overall, I think CROE is a good first step in evaluating hitter’s swing decisions based on the quality of the pitches seen. In the future, being able to control for a hitter’s swing path in evaluating swing decisions for all pitches against certain pitch characteristics would be an interesting thing to study. For instance, it may make sense for a hitter with a flat bat path to swing at more pitches up in the zone than a hitter with a steep bat path. In general, using pitch grade models to not just evaluate pitchers but hitters as well is an important next step in the Statcast era of sabermetrics.

Tom Tango post on defining “Chase Zone”

http://tangotiger.net/strikezone/zone%20chart.png

Robby Green post on “Over Expected” metrics

https://www.nfeloapp.com/analysis/over-expected-explained-what-are-cpoe-ryoe-and-yacoe/

How Much Trade Value Did the Rays Give Up in the 2022 Draft?

 While the Rangers shocked everyone by taking Kumar Rocker 3rd overall, the Rays made their own puzzling decision by taking Xavier Isaac, a high school first baseman ranked 113 by Pipeline and unranked by FanGraphs, in the first round. At the time of the pick, I assumed the Rays were planning on cutting an under slot deal for Isaac in the first, and going after another expensive prep player later. Instead, the Rays paid him full slot value and drafted mostly college players the rest of the draft. It’s hard to give a complete scouting report on Isaac; he was injured for the entire showcase circuit, but what I would like to focus on is how much did the Rays sacrifice (or gain) in trade value by making this pick. There are two ways to get value out of a drafted player, either by the player making the major leagues, or whatever you get out of trading the prospect. In this analysis, I want to focus on the latter, because the Rays have consistently been in the playoff hunt and will at least consider trading prospects to improve the major league team, so drafting with trade value in mind is fairly practical.

To start, I want to lay out what the Rays got on the first day of the draft. I will be quoting the prospects’ value in terms of FV given by FanGraphs. 

Round (Pick)	Name	FV (FanGraphs)
1 (29)	Xavier Isaac	35+
2 (65)	Brock Jones	35+
CB-B (70)	Chandler Simpson	40
CB-B (71)	Ryan Cermak	35+

In comparison, the 30-45 range on the FanGraphs board was valued as 45 FV, so the Rays passed up on drafting a good player to go off the board and select Isaac. One thing to note is that Eric seems to be a bit colder on Jones and Cermak than industry consensus, and therefore the Rays draft looks fairly weak. 

Let’s look at the 2022 trade deadline and see what type of players the Rays could get with 2 40 FV’s and 2 35+ FV’s (rounding up on Jones) and compare that to what they could get with a 45 FV, 2 40 FV’s and a 35+ FV. Looking at how FanGraphs broke down the deadline, with the 45 FV, the Rays could get someone like Gallo, a fallen star with an expiring contract, and a couple relievers/platoon bats with the rest of the class, or packaged 45 and the 40’s for Tyler Mahle, a mid rotation starter with 1.5 years of control left. In comparison, the best the Rays could have done with the current class is get a player like Syndergaard, a 4th starter on an expiring contract or Jorge Lopez, a pop up reliever this year who has been dominant, but probably settles into a 7th-8th inning role. As you can see, while measuring trade value is inexact, based on these assumptions there is a large difference in trade value that the Rays gave up to select Isaac.

Now, if Isaac has a strong first half of the 2023 season, the pre draft rankings on Isaac do not matter, especially since teams did not have much to evaluate off of and will be updating their beliefs quicker than usual on him. However, I think the opposite is in play here as well, where if Isaac is just mediocre or below average, teams will write him off quicker than usual. 

To give a hypothetical example, imagine a draft prospect’s rankings from each team as a distribution, so that for a 45 FV prospect, most teams rate the prospect as a 45 FV. Now imagine you are running a team. You are picking 15th, and can choose between Player A, the consensus 15th best player, and Player B, the consensus 50th best player. If you choose A, you are right in the middle of the distribution, whereas if you choose B, you are on the far right of the tail. When you are on the far right of the tail, very few teams want to trade for B for the same amount of value that you think he is worth, whereas that is not the case with A. Furthermore, it’s hard to benefit from variance when you take player B over A. For instance, if player B has a breakout performance, it brings the mean consensus rating to where the right tail used to be, and unless there are a few teams that suddenly value him higher than you pre draft, you haven’t gained any trade value from the breakout performance. In contrast, if player A breaks out and consensus rating increases, you get that increased value since you drafted him at consensus value.

In summary, it’s very difficult for the Rays to do well by drafting Xavier Isaac. A lot of things have to work out well for them to do better than the median value of the slot. However, if there’s one team that has made many people look stupid, it’s the Rays, and so I look forward to see how this plays out in the future and what there is to learn if this works out.

Sources:

https://blogs.fangraphs.com/ranking-the-prospects-traded-during-the-2022-deadline/

"Does Difference in Expected Spin Axis Affect Distance of Batted Balls for Sinkers?"

Full article

https://rpubs.com/dgerth5/936108

Statistical Inference vs Statcast Models

Since I am currently working on a Stuff model (done in Stan, and hopefully publishing a writeup in their case studies documentation), I have been thinking a lot about the philosophical differences in models that are done with Statcast data versus models built with game level data.

In general, I feel like there is an implicit assumption that models built from Statcast data are always going to be better than ones built with just statistics. I think that viewpoint typically makes sense. Statcast data is more granular, which makes it more feasible to give individuals credit. Fielding statistics as a whole have benefited from Statcast data. It's really hard to properly attribute skill to fielders without granular level data, and new statistics such as Catch Probability and OAA are really impressive pieces of work that show how important Statcast data is and how much we were missing it beforehand.

However, I want to push back on the idea that Statcast models are always better than statistical models. I think this can be especially true in pitching, which may be surprising. Two places where Statcast models can be beat by a well calibrated statistical model is in deception and pitch mix interaction. Deception is something inherently visual and means different things to different people, which makes it hard to quantify. Furthermore we run the risk of overfitting to small data based on this, which can make the overconfident in their perceived edge against others and lead to ruin.

Asking if you want a good stats only model versus a good Statcast model is like the beer or tacos question. You want both, and ideally want them to converge to the same prediction. However, acknowledging the strengths and weaknesses of both approaches and not defaulting to one approach is beneficial in the long run.

Losing Your Edge in Games Where You Can't Go Broke (and How Successful Teams Start Losing)

Every month, Kyle Boddy releases his/Driveline's internal xERA and Stuff farm system rankings, which have been very informative. The leaders are pretty much who you would expect, it's some version of the Dodgers, Rays, Astros, Yankees, or Baltimore, and some usual suspects like the Royals and Rockies at the bottom. What I find striking is that the A's and the Cardinals are consistently near the bottom of the list. Some teams have been penalized by either promotion or trades (or in the Astros case, forfeiting draft picks), which is not the fault of the organization per say, but the A's and Cardinals do not have this excuse. 

What I find so fascinating about this is that the A's and the Cardinals were at the top of the game in cutting edge analytics. Everyone knows about the A's and Moneyball, and the Cardinals were one of the first teams join the A's in having an analytics based approach. Adopting analytics and being bold was wildly successful for them, as the Cardinals won a couple World Series titles and the A's were very successful despite cheapskate ownership. Now, they seem to have fallen behind by not emphasizing new player development initiatives, which is where the cutting edge of baseball analytics is right now. One example is that Boddy notes how the A's have an extremely fastball heavy approach, which is an old school philosophy that is falling by the wayside. For the Cardinals, drafting Michael McGreevy in the first round feels like a pick that would have been sharp in 2005, but now that we have more than surface level college stats and can look at things such as spin rate and vertical movement, probably is not a great first round pick.

In professional gambling, it's extremely easy to know when you have lost your edge. You lose all your money. In professional baseball, it's not that black and white. In fact, it's super easy to ignore the warning signs by looking at surface level stats and dismissing criticism by saying Twitter isn't real. That's fine. The Cardinals are above .500 and the A's are explicitly rebuilding right now, so from a results based mindset things do not look too bad. This level of apathy can linger for a long time, far longer in comparison to a professional gambler losing his or her edge.

Hopefully both teams can reclaim their boldness and get out of the cellar of minor league xERA. As early adopters, the A's and Cardinals face a problem that many large businesses have the first time they reach growth slumps. It will be interesting to see if they can regain their startup magic.

Setting Informative Priors for Bayesian Mixed Effects Models

 https://rpubs.com/dgerth5/924572

Overoptimizing Drafts

As we head into the 2022 Draft, I have been thinking about draft trends and how different the hot new pitch archetype has changed over the past couple years. Nowadays, you can't scroll through baseball Twitter without someone talking about "sweep," but this was not always the case. A few years ago, pitching Twitter was fired up about guys throwing 4 seamers up in the zone, and having a 12-6 or gyro slider to go along with it. This was a very successful strategy, and still is. However, now that we know how good an east-west pitcher can be and how they can be developed effectively, there is less emphasis on drafting the vertically oriented guy. Passing over a guy with a ton of SSW on his sinker just because he doesn't have a carrying fastball is a bad idea, but the public did not have this data a few years ago and thus overreacted (slightly) on traits that could be measured easily and performed well.

My point in writing out this out is to say optimizing your draft for what is popular now does not guarantee future success. If you did nothing but draft pitchers with rising 4 seam shapes and vertical breaking balls, you would have done fairly well. However, if in the later rounds you were debating between a mediocre present stuff vertical oriented pitcher versus a slightly better present stuff east-west pitcher, and you take the vertical pitcher on the assumption that the prototype is more projectable, that probably was the wrong decision, given how good player development has gotten in developing horizontal stuff. Note that I am not suggesting we go back to the days of drafting guys with 88 MPH generic sinkers. These guys are not good.

The easy part is drafting someone and the hard part is maximizing their development. It is a fair point that if a team was not drafting vertically oriented pitchers, they were not also thinking about sweeping sliders or SSW on a sinker and generally did not know how to develop it. The main lesson is to draft the player, not the prototype. If you draft a player because he fits the in vogue prototype, not because you have faith in his tools or a development plan tailor made for him (and lets face it, most teams don't), you might get lucky and get a solid player, but eventually you will get adverse selected by teams pushing the envelope and drafting players based on what they think the game will be in a few years. It's hard to do this well, but the only way to have a robust drafting and developing strategy.