Pitchers Improving their Expected Results by Joe Douglas August 18, 2017 If you’ve followed baseball over the course of of the past few seasons, you’ve probably noticed the new data available to us with the advent of Statcast. This has led to the development of new metrics to measure player performance, with xwOBA being one of the most notable. If you’re familiar with xwOBA, you have likely seen it used to examine the quality of contact made or induced by hitters or pitchers. Today, I want to look at the pitcher side of things. While it is generally accepted that some pitchers are better at inducing weak contact than others, to this point, the baseball community is still working through the best ways to process the implications of the relatively new data available to us. As Craig Edwards wrote yesterday on the main site, there isn’t a strong relationship between weak contact year to year. Acknowledging all of this, I want to look at pitchers who have recently improved the quality of contact they have allowed. There are a couple assumptions to acknowledge here (included at the bottom of the following table). First, I am only looking at pitchers with over 1000 pitches in 2017 before the All-Star Game. Additionally, I am only including pitchers who have thrown 500 pitches since the All-Star Game. My intent with this is to try to get a better look at starting pitchers, who have made more than a couple of starts, and remove relief pitchers. I have also limited the group to players who’s post All-Star Game expected wOBA is less than the sample average at the time of the break (this works out to be around .315, for reference). The last stipulation I have included is that I am only showing pitchers who have seen an improvement of .010 or greater in their expected results (10 points or greater). The reason for this is simple, I would rather show 25 results than 45. As a mea culpa, we are looking at arbitrary endpoints and small samples. I acknowledge this. Certainly to bet in favor of every pitcher on this list based off of 500+ good pitches since the break would be folly, and I don’t recommend doing so. However, I would consider this a good starting point for pitchers to examine. Maybe this tips you off to the recent success of Trevor Williams or Gio Gonzalez, or affirms your belief in Gerrit Cole if you were considering selling. I guess what I am saying is, I don’t know how you will use this information, but personally, I would consider this a sampling of pitchers who may be worth your attention. xwOBA Improvements Since ASG Rk. Post ASG RK. Player xwOBA PostASG xwOBA Delta 110 2 Danny Salazar 0.340 0.231 -0.109 105 7 Masahiro Tanaka 0.336 0.261 -0.075 3 1 Chris Sale 0.249 0.183 -0.066 95 14 Gerrit Cole 0.331 0.272 -0.059 85 11 Charlie Morton 0.325 0.267 -0.058 115 31 Justin Verlander 0.346 0.291 -0.055 48 5 Gio Gonzalez 0.307 0.256 -0.051 103 28 Trevor Bauer 0.336 0.288 -0.048 119 42 Jason Hammel 0.348 0.303 -0.045 56 15 Sonny Gray 0.311 0.272 -0.039 43 9 Yu Darvish 0.305 0.267 -0.038 118 48 John Lackey 0.347 0.310 -0.037 102 36 Tanner Roark 0.334 0.298 -0.036 117 51 Zach Davies 0.346 0.311 -0.035 54 19 J.A. Happ 0.310 0.276 -0.034 61 24 Marco Estrada 0.312 0.282 -0.030 67 27 Danny Duffy 0.316 0.287 -0.029 24 6 Aaron Nola 0.286 0.257 -0.029 46 20 Jake Arrieta 0.306 0.278 -0.028 71 35 Patrick Corbin 0.319 0.296 -0.023 50 26 Trevor Williams 0.308 0.286 -0.022 73 37 German Marquez 0.319 0.298 -0.021 60 33 Lance Lynn 0.312 0.294 -0.018 21 10 Jacob deGrom 0.285 0.267 -0.018 41 29 Michael Wacha 0.303 0.289 -0.014 26 18 Carlos Martinez 0.288 0.274 -0.014 30 23 Chris Archer 0.295 0.281 -0.014 6 3 James Paxton 0.255 0.243 -0.012 SOURCE: Baseball Savant – 1000+ pitches in 2017, prior to ASG – 500+ pitches in 2017, post ASG – Post ASG xwOBA under sample average at time of All-Star Break – Improvement in xwOBA of 10 points or more So we are looking at a list of 28 pitchers who meet our criteria. For reference, I have include their rank relative to other pitchers who have met our pitch criteria for each point we are measuring. So for example “Rk.” is a players rank by xwOBA relative to all other pitchers who threw 1000 pitches pre-all-star game. “Post ASG Rk.” indicates a pitcher’s ranking among pitchers who have thrown 500 or more pitches since the break. Of this group, several are names you would expect. Perhaps most impressive is Chris Sale, who was third in the league in xwOBA prior to the break at .249, but since then has improved his xwOBA by 66 points to .183. Just absurd. However, for fantasy purposes, this is slightly irrelevant. Chris Sale is owned in every fantasy league. You aren’t going to find him on the wire and you likely aren’t going to trade for him. Same likely goes for James Paxton. We need to dig a little deeper. Since returning from the DL, Charlie Morton has been superb. His .267 xwOBA is fantastic, ranking him 11th among SP. For Ottoneu FG points leagues, he has run 5.42 P/IP. While he has been good when on the field, he is always an injury risk. There may be a buying opportunity if any teams are fearful of his injury history, and looking for a more assured source of innings. However, perhaps the cheapest acquisitions on this lists will be those who do not have rosy projections from zips/steamer. Let me be clear, the players I want to buy into most are those who have large improvements in their projections and expected results (xwOBA). However, it is likely that you play in any leagues that use projections as a template for player values. If this is the case, players like Morton, Nola, and Salazar will likely be held tightly because, despite injury risk, their expected performance from projections is great. In these instances, the cheapest bets you can make are likely those who have good expected results, but don’t stand out when looking at projections. A couple players who fit this mold include Gio Gonzalez (.256 xwOBA but projections put him near league average), Trevor Williams (5.20 P/IP over last 60 IP), German Marquez (.298 xwOBA in 3 starts at Coors and 3 on the road, while projections place his peripherals in the mid 4s), and JA Happ (.276 xwOBA since break with projected peripherals in the mid 4s). Players like this will likely be cheaper to trade for considering most of their good fortune recently is based off of inducing weak contact. In most fantasy formats, players are forced to make decisions based on small sample sizes. However, in these situations I would still be trying to gather as much information as possible. Does this mean that the results will always be great? Definitely not. However, my hope is that the above listing of players can at least give you a starting point as you try to discern which pitchers can be acquired cheaply, and have potential for a payoff.