Finding LOB% Overachievers and Underachievers
There are many pitcher stats that we review and analyze to predict regression or breakout. While there is plenty of focus on BABIP (batting average on balls in play), HR/FB (home-run to flyball ratio) and ERA (earned run average), LOB% (Left On Base percentage), is a stat that I think is sorely overlooked in the fantasy baseball spectrum (and the baseball world at large). For those who need a refresher or aren’t aware, LOB% measures the percentage of base runners that a pitcher strands on base over the course of a season. It is calculated by this simple formula:
(H+BB+HBP-R)/(H+BB+HBP-(1.4*HR))
An average LOB% for a pitcher will land somewhere in the 70-80% range.
To get a good feel of what a good LOB% looks like, here were the top ten LOB%s in 2017, minimum 50 innings pitched.
| Name | Team | IP | LOB% |
| Craig Kimbrel | Red Sox | 69 | 93.9% |
| Matt Albers | Nationals | 61 | 92.4% |
| Corey Knebel | Brewers | 76 | 91.9% |
| Kenley Jansen | Dodgers | 68.1 | 91.3% |
| John Brebbia | Cardinals | 51.2 | 90.9% |
| David Robertson | Yankees | 68.1 | 89.4% |
| Zach McAllister | Indians | 62 | 89.3% |
| Brad Hand | Padres | 79.1 | 89.2% |
| Archie Bradley | Diamondbacks | 73 | 88.2% |
| Josh Fields | Dodgers | 57 | 88.1% |
And then we have the unfortunates- the ten lowest LOB% marks in 2017.
| Name | Team | IP | LOB% |
| T.J. McFarland | Diamondbacks | 54 | 53.6% |
| Roberto Osuna | Blue Jays | 64 | 59.5% |
| Wily Peralta | Brewers | 57.1 | 59.8% |
| Jesse Hahn | Athletics | 69.2 | 60.6% |
| Tyler Glasnow | Pirates | 62 | 60.7% |
| Brett Anderson | Blue Jays | 55.1 | 60.9% |
| Brandon Maurer | Royals | 59.1 | 61.1% |
| Fernando Rodney | Diamondbacks | 55.1 | 61.1% |
| Jordan Lyles | Padres | 69.2 | 61.2% |
| Bartolo Colon | Twins | 143 | 61.4% |
So exactly why is LOB% so important? As one can guess, it has a pretty strong correlation (r = -0.76) with ERA. Let’s take a look at Robbie Ray as an example. Without looking at ERA, you’d think he was basically the same pitcher in 2016 and 2017:
- 2016- 3.76 FIP, 3.07 K/BB, 1.24 HR/9
- 2017- 3.72 FIP, 3.07 K/BB, 1.28 HR/9
Dig deeper, however, and you’ll find that there was a major swing in his LOB% between the two years. That swing went from a below-average mark of 68.7% in 2016 (which led to a 4.90 ERA) all the way up to a mark of 84.5% in 2017, helping deliver a 2.89 ERA and all kinds of hype.
We can generally expect pitchers to veer towards the mean of 70-80%, but then in order to get an even clearer picture of this stat the concept of xLOB% comes in. Like every other “x-stat,” xLOB% is the expected rate of runners stranded. The great Mike Podhorzer went in-depth on this in a piece of his at TBT (The Hardball Times, for the unintiated), even going so far as to devising his own detailed xLOB% formula.
“xLOB% = 0.931 – (1B% * 0.877) – (2B% * 1.646) – (3B% * 2.164) – (HR% * 0.377) – (BB% * .380) – (HBP% * 0.640) – (HBP% * 0.640) – (WP% * 0.851) + (K% * 0.137) + (GDP% * 0.401) – (SB% * 0.142) + (CS% * 0.264) + (PKO% * 0.519) + (True IFFB% * 0.351)”
This gave me the spark of inspiration, so I decided to deep dive into some research of my own. Using a lot of the same techniques and abstractions as Podhorzer did, I developed a very similar formula of my own. And here’s what I found, starting with the top ten xLOB% marks.
| Name | Team | IP | Actual LOB% | xLOB% |
| Craig Kimbrel | Red Sox | 69 | 93.9% | 86.9% |
| Kenley Jansen | Dodgers | 68.1 | 91.3% | 86.1% |
| Sean Doolittle | Nationals | 51.1 | 70.3% | 85.0% |
| Andrew Miller | Indians | 62.2 | 87.1% | 85.0% |
| Chad Green | Yankees | 69 | 82.3% | 84.8% |
| Edwin Diaz | Mariners | 66 | 78.5% | 83.8% |
| Carl Edwards Jr. | Cubs | 66.1 | 78.3% | 83.5% |
| Josh Fields | Dodgers | 57 | 88.1% | 83.1% |
| Corey Knebel | Brewers | 76 | 91.9% | 83.1% |
| Matt Albers | Nationals | 61 | 92.4% | 83.0% |
And then the ten lowest…
| Name | Team | IP | LOB% | xLOB% |
| Vance Worley | Marlins | 71.2 | 64.5% | 61.8% |
| Brett Anderson | Blue Jays | 55.1 | 60.9% | 62.9% |
| Jhan Marinez | Rangers | 58.1 | 80.1% | 63.8% |
| Wily Peralta | Brewers | 57.1 | 59.8% | 64.2% |
| Homer Bailey | Reds | 91 | 64.8% | 64.5% |
| Tyler Glasnow | Pirates | 62 | 60.7% | 65.0% |
| Jordan Lyles | Padres | 69.2 | 61.2% | 65.5% |
| Chris Tillman | Orioles | 93 | 64.2% | 65.7% |
| Clayton Richard | Padres | 197.1 | 70.6% | 66.1% |
| Sam Dyson | Giants | 54.2 | 66.4% | 66.2% |
My biggest goal in this endeavor was finding the overachievers/underachievers in LOB%, and looking for regression candidates (as we do with BABIP, ERA, and HR/FB). By subtracting a pitcher’s xLOB% from their LOB%, I found the ten biggest LOB% underachievers among starting pitchers.
| Name | Team | IP | LOB% | xLOB% | Difference |
| Doug Fister | Red Sox | 90.1 | 63.3% | 72.6% | -9.3% |
| Joe Biagini | Blue Jays | 119.2 | 61.5% | 70.7% | -9.2% |
| Michael Fulmer | Tigers | 164.2 | 65.6% | 74.2% | -8.6% |
| Jharel Cotton | Athletics | 129 | 65.7% | 73.1% | -7.4% |
| Tanner Roark | Nationals | 181.1 | 66.3% | 73.1% | -6.8% |
| Dinelson Lamet | Padres | 114.1 | 69.2% | 76.0% | -6.8% |
| Jeremy Hellickson | Orioles | 164 | 66.3% | 72.8% | -6.5% |
| Ian Kennedy | Royals | 154 | 68.2% | 74.5% | -6.3% |
| Jake Odorizzi | Rays | 143.1 | 72.5% | 78.8% | -6.3% |
| Jeff Samardzija | Giants | 207.2 | 67.5% | 73.5% | -6.0% |
And then the biggest underachievers among relievers…
| Name | Team | IP | LOB% | xLOB% | Difference |
| Roberto Osuna | Blue Jays | 64 | 59.5% | 79.6% | -20.1% |
| Fernando Rodney | Diamondbacks | 55.1 | 61.1% | 75.8% | -14.7% |
| Sean Doolittle | Nationals | 51.1 | 70.3% | 85.0% | -14.7% |
| T.J. McFarland | Diamondbacks | 54 | 53.6% | 67.5% | -13.9% |
| Chase Whitley | Rays | 57.1 | 61.9% | 74.5% | -12.6% |
| Jim Johnson | Braves | 56.2 | 62.3% | 73.9% | -11.6% |
| Matt Belisle | Twins | 60.1 | 65.9% | 77.0% | -11.1% |
| Adam Warren | Yankees | 57.1 | 70.5% | 81.1% | -10.6% |
| Paul Sewald | Mets | 65.1 | 65.0% | 74.6% | -9.6% |
| Liam Hendriks | Athletics | 64 | 65.5% | 74.7% | -9.2% |
Some interesting names show up in these findings. The first one I noticed was Dinelson Lamet. Looking at some underlying numbers for him, such as a 10.9 K/9 and .207 BAA, one would think he’s better than the 4.57 ERA he put up in 2017. That’s a reasonable assumption, as some of the differences between his LOB% and xLOB% can be to blame. Moreover, Steamer projects him to get a bit closer to his xLOB% numbers (projected 73.5 LOB%), which is right in line with a projected improvement in ERA (4.17).
Another name that caught my eye was Roberto Osuna. 2017 was a one to forget for this young closer, as it seems he was bit hard by the ERA monster (which was one of the causes for his AL-leading 10 blown saves). However, one could easily point to his 59.5 LOB%, second worst rate in all of baseball (min. 50 IP), as the true culprit. His xLOB% of 79.6 indicates that one should expect some regression to the mean next year for Osuna.
Now time for the lucky ones. The overachievers. First with the starting pitchers…
| Name | Team | IP | LOB% | xLOB% | Difference |
| Trevor Cahill | Royals | 84 | 76.3% | 67.4% | 8.9% |
| Hyun-Jin Ryu | Dodgers | 126.2 | 81.4% | 72.6% | 8.8% |
| Kendall Graveman | Athletics | 105.1 | 75.1% | 68.0% | 7.1% |
| Adalberto Mejia | Twins | 98 | 76.0% | 69.0% | 7.0% |
| Madison Bumgarner | Giants | 111 | 82.8% | 75.9% | 6.9% |
| Clayton Kershaw | Dodgers | 175 | 87.4% | 81.0% | 6.4% |
| German Marquez | Rockies | 162 | 76.0% | 70.0% | 6.0% |
| Marcus Stroman | Blue Jays | 201 | 78.1% | 72.2% | 5.9% |
| Jose Urena | Marlins | 169.2 | 79.0% | 73.2% | 5.8% |
| Drew Pomeranz | Red Sox | 173.2 | 80.0% | 74.3% | 5.7% |
And relievers…
| Name | Team | IP | LOB% | xLOB% | Difference |
| Jhan Marinez | Rangers | 58.1 | 80.1% | 63.8% | 16.3% |
| John Brebbia | Cardinals | 51.2 | 90.9% | 75.9% | 15.0% |
| Cory Gearrin | Giants | 68 | 88.0% | 73.1% | 14.9% |
| Jared Hughes | Brewers | 59.2 | 79.0% | 66.8% | 12.2% |
| Tony Watson | Dodgers | 66.2 | 84.1% | 72.2% | 11.9% |
| Zach McAllister | Indians | 62 | 89.3% | 77.5% | 11.8% |
| Brian Duensing | Cubs | 62.1 | 84.8% | 74.4% | 10.4% |
| Keynan Middleton | Angels | 58.1 | 84.7% | 74.8% | 9.9% |
| Brad Hand | Padres | 79.1 | 89.2% | 79.6% | 9.6% |
| Hunter Strickland | Giants | 61.1 | 82.9% | 73.4% | 9.5% |
One case that I found interesting was Jon Brebbia. Brebbia put up a very unsustainable 90.9 LOB% last season, putting him 2nd among relievers in my metric. The numbers suggest that he should have been closer to league-average, and so I’ll call bluff on his 2.44 ERA. Steamer pins him at a much more normal 72.0 LOB% for the upcoming season, along with projecting him to consequently regress to a 4.25 ERA.
Now that we know who over-performed and under-performed on their LOB%, it is very important to understand the predictive value in this metric. Once I matched a player’s xLOB% from years past with his LOB% the following year, I found that the correlation was 0.32- neither strong nor insignificant. This is interesting, but to tell the truth, it isn’t the prettiest predictor. I think this is a great avenue for further investigation and refinement, with a chance to turn it into a predictor with a greater correlation.
No matter what the season-to-season correlation is as a whole, LOB% and xLOB% are stats that can be used to identify prime regression and progression candidates.
The Author
9 Comments
Can the same idea be done with hitters?
What do you mean? I’m all about hitters already
Your comment is for me?….I am new to this site ….did you write this?….thanks
[…] searches for LOB% overachievers and underachievers to target and avoid on draft […]
It seems you refer to underachievers as overachievers in this article which took me re-reading the article to figure out what was going on. I think the table with Osuna needs to be renamed, unless I misunderstand the table. Interesting to see Doolittle here, since I was expecting some regression but a sub 3 ERA might be much more likely than I initially thought.
Thanks for noticing that… fixed it.
Excellent stuff Patrick. I didn’t realize we could go so in-depth on LOB%. I use the stat a lot but will have to include this newer version in my comparisons.
I use LOB/s-stk %s to rank players after #100
I’m thinking a teams defense may impact LOB% and maybe why the Shark is on there, but then see MadBum on the other chart. HR allowed by Shark also contributes. Osuna 2017 makes much more sense now.