Searching For Undervalued Pitchers

When looking to the future, there are countless ways to try and find undervalued pitchers.

One such way is to look at which pitchers’ FIPs outperformed their ERAs last year. This is a good approach, but it isn’t enough. For one, there will be players who consistently underachieve on their metrics, like the ever-teasing Michael Pineda. He sits second on the 2016 leaderboard in ERA-FIP, but his ERA is more than half a point greater than his FIP for his career and over a point greater each of the past two seasons.

The other problem with this approach is that FIP has become mainstream enough that everyone will be doing this same thing. Players who outperformed their FIP will be be common targets on draft day, driving up their prices and eliminating any sleeper potential that they had. This, too, is the downside of projections and other easily accessible data.

A different approach is then needed. In that spirit, I decided to create a linear regression model to predict a subsequent year’s ERA based on the difference in first- and second-half splits from the previous year, as well as that year’s ERA. This would help find the players who improved the most from the beginning of the year to the end, and perhaps players who are likely to carry over those improvements into the next season.

The model was generated using data from 2002 to 2015 obtained from FanGraphs’ splits leaderboard, with only pitchers with at least 50 IP in each half-season being considered so as to remove potential outliers. Non-significant variables were removed, and a final model was created. The resulting model was then used with 2016 data to predict ERA in 2017. The following graph shows those predictions, after being rescaled, plotted against 2016 ERA:

For the most part, the predictions line up with their 2016 counterparts. The labeled data points, though, are the ones I want to focus on. Based on this model, each of them are expected to see their ERA drop significantly from last season to this one and could help provide value in the latter rounds of drafts.

Jeff Samardzija
2016 ERA: 3.81
2017 Projected ERA: 3.40

The Shark has had a rough career. Since becoming a starter in 2012, he’s only had one season in which he’s beaten last year’s mark of a 3.81 ERA. He’s played for four different teams in those five years, he’s on the wrong side of 30 and his name is at the same spot on the pronunciation scale as Jedd Gyorko’s. But he does have a few things going for him. He’s struck out over 20 percent of the batters he’s faced in all but one year since 2011, and he’s pitching in a park where home runs go to die. His average fastball velocity is holding steady above 94mph and it was only two years ago where he had a sub-3 ERA with the estimators to back it up. He’s proven he can put up solid numbers, so the predicted improvement isn’t unreasonable. He had a 3.66 FIP in the second half of 2016 that exactly matched his ERA, a substantial drop from his first half numbers. The biggest contributors were his strikeout rate, which rose from 18.9 to 21.9 percent, and his HR/9, which dropped from 1.15 to 0.94. There’s no reason to think the rates are unsustainable either — his HR/FB dropped to a near-league average (in a normal year) 10.8 percent, and his strikeout rate improved almost directly with an increase in his O-Swing%:

Samardzija was able to get batters to swing at pitches out of the zone more frequently as the season went on, and consequently was able to produce more strikeouts. Steamer projects him for a 3.66 ERA, which isn’t all that far off this model’s prediction. If he can bring his strikeout rate back to what it used to be, and AT&T Park does its job, Samardzija could provide some sneaky value in 2017.

Ivan Nova
2016 ERA: 4.17
2017 Projected ERA: 3.72

Moving to the NL seemingly agreed with the former Yankees second-round pick. After posting an unsightly 4.90 ERA in 21 games (only 15 starts) in pinstripes, he turned his season around in Pittsburgh with a 3.06 ERA and 2.62 FIP in his final 11. Switching leagues undoubtedly helped, but there are more reasons behind his improvement. For one thing, he increased his strikeout rate while decreasing his walk rate — just doing those two would be reason to expect a lower ERA. Perhaps more significant, though, is that he halved his HR/9. Much of this is due to a change in scenery — his HR/FB dropped from 21.3 percent before his trade to just 7.8 percent afterward. Of course, he can’t be expected to repeat his performance. He walked just three batters in 64 2/3 innings, good for a 1.1 percent walk rate and a 17.33 K/BB. While Nova is probably better than Phil Hughes, it’s unlikely that even he can replicate that kind of walk rate. Look for Nova to improve on his ERA from last year, but don’t expect him to be as good as his second half. He’ll fall somewhere in the middle, but even that will be more than useful.

Wily Peralta
2016 ERA: 4.86
2017 Projected ERA: 4.35

Don’t look now (unless you promise to come back), but Peralta had a 2.92 ERA in the second half of 2016. Part of this was admittedly due to an inflated 81.7 percent strand rate, but even accounting for that, he managed a 3.75 FIP and 3.59 xFIP during that stretch. His success can be due largely in part to his increase in strikeout percentage, which jumped from 13.6 percent to 20.8 percent. It’s difficult to determine the exact reason behind this, but one explanation might be his increase in velocity. At the start of the year, his fastball was only averaging under 95 mph, a continuation of his 2015 trend and a disgrace to fireballers everywhere. By August, he was closing in on 97 mph, and presumably striking out batters as a consequence. Here’s his velocity by month since 2014, via BrooksBaseball:

Not only did Peralta see an increase in his strikeout rate, but his walk rate improved as well from 8.7 percent to 6.5 percent, which is the lowest to reasonably expect given his career numbers. His WHIP dropped from 1.88 to 1.15, his HR/9 from 1.64 to 1.02 and his wOBA against from .421 to .295 — seemingly everything improved except his age, but I’ll give him a pass on that account. The secret behind his success? His ability to limit hard-hit balls and induce soft contact. Take a look at the trends for each type of contact rate:

In case that doesn’t do it for you, here’s his Statcast exit velocity broken down by game date, via Baseball Savant (with a linear regression line added for those last few skeptics who aren’t convinced):


Peralta’s not an ace, but he has the potential to help out teams this season. Monitor his velocity during spring training, and buy him for a discount on draft day.

Clay Buchholz
2016 ERA: 4.78
2017 Projected ERA: 4.02

Of all pitchers who threw at least 50 innings in each half of the season, Buchholz improved his FIP the most — his first-half FIP was 6.02, so he gave himself quite an advantage, but he still brought it down to 3.74 following the Midsummer Classic. He’s already proven himself to be a capable pitcher, with four sub-3.50 ERA seasons in his past seven seasons, and now he goes to Philadelphia, where pitchers go to be reborn (see: Hellickson, Jeremy). Also, he moves from the AL East to its NL counterpart. Besides going up against a pitcher instead of a designated hitter, he will be facing the likes of the Braves and Marlins instead of the Blue Jays and Yankees.

Despite the difficulty of his former division, Buchholz still managed to improve as the 2016 season wore on. He marginally increased his strikeout and walk rates, doubling his K-BB% to a still-mediocre 9.3 percent in the second half of the season. While that’s not exactly comforting, it’s worth noting that his walk rate in the first half the season was higher than anything he’s put up since 2008, so it’s not likely to approach that number anytime soon. Furthermore, he was able to bring down his bloated HR/FB rate, despite the league’s general struggle to do so. In the first half of the season, 15.9 percent of Buchholz’s fly balls resulted in home runs, which would have been higher than any single season in his career. In the second half that number improved to 5.1 percent, which was much more reasonable given his average rate of 6.5 percent over the previous three seasons. Steamer projects him for a 4.07 ERA, but it’s not difficult to envision a scenario where he does better than even that.

With all that being said, not all of the pitchers on this list are going to live up to their projections. No model is perfect, and none of these guys have exactly had exemplary careers. But they all showed significant improvement over the course of last year, and that’s a strong indication for what to expect from them in 2017.



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evo34
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evo34

What formula did you end up using?

Were you able to show that is useful in predicting future ERA?