wRC+ by Leverage: the Good, the Bad, and the Funky
So I got a little carried away with the new splits leaderboard when I was looking up some wRC+ data. I was curious about which players performed the best/worst in high-leverage situations and one thing led to another and it led me to looking at top performers across the three leverage situations (low, medium, and high). If you want to know more about how leverage is calculated there is an old article in The Hardball Times here.
I used the splits leaderboards to gather 2016 hitter data by leverage situation and I only included players who had a minimum of 20 PA per split. Once I gathered all the data I converted each player’s wRC+ by leverage situation to a percentile and calculated each player’s mean percentile rank along with the variation around the mean using standard deviation to produce the following plot.
The blue line is just a LOESS line showing the general trend of the data. What the line is telling us is that players on the extreme end of the percentile ranks also seem to have the lowest variation or, more simply put, good players seem to be consistently good and bad players seem to perform poorly across all leverage situations. Using that plot as my baseline, I started exploring the data to answer some question about player performances in 2016. I included the top 10 players in ordered tables going from from least interesting to most interesting, at least in my opinion. First, let’s look at the top performers from this year.
| Leverage Rank | |||||
|---|---|---|---|---|---|
| Name | Low | Medium | High | Mean Rank | SD |
| Mike Trout | 98 | 97 | 99 | 98 | 1 |
| Freddie Freeman | 97 | 88 | 94 | 93 | 4.6 |
| Josh Donaldson | 97 | 94 | 88 | 93 | 4.6 |
| Anthony Rizzo | 93 | 88 | 97 | 92.7 | 4.5 |
| Joey Votto | 96 | 98 | 84 | 92.7 | 7.6 |
| David Ortiz | 96 | 99 | 77 | 90.7 | 11.9 |
| Matt Carpenter | 91 | 82 | 94 | 89 | 6.2 |
| Paul Goldschmidt | 88 | 86 | 88 | 87.3 | 1.2 |
| Tyler Naquin | 93 | 81 | 87 | 87 | 6 |
| Ryan Schimpf | 80 | 86 | 93 | 86.3 | 6.5 |
Boring, Mike Trout leads the way as the top performer. Apparently it doesn’t matter when he comes up to the plate; he is going to smash the ball. But I’m not going to focus on Trout, as I’m not qualified to write about him and he’s above my pay grade, so let’s leave him to the professionals. Like I said before, least interesting first and hopefully it’ll get more exciting as we go. Here’s a fun fact to keep you going: In high-leverage situations among players with a minimum of 20 PA, Ryan Howard led the league in ISO with a 0.640 mark. Ryan Schimpf was second with an ISO of 0.542. And Howard did that with a 0.118 BABIP, too.
Second, let’s take a look at the worst performers of the season.
| Leverage Rank | |||||
|---|---|---|---|---|---|
| Name | Low | Medium | High | Mean Rank | SD |
| Yan Gomes | 17 | 23 | 0 | 13.3 | 11.9 |
| A.J. Pierzynski | 17 | 21 | 9 | 15.7 | 6.1 |
| J.B. Shuck | 25 | 16 | 11 | 17.3 | 7.1 |
| Nick Ahmed | 15 | 35 | 3 | 17.7 | 16.2 |
| Jake Marisnick | 37 | 19 | 6 | 20.7 | 15.6 |
| Ramon Flores | 21 | 20 | 21 | 20.7 | 0.6 |
| Gerardo Parra | 33 | 29 | 1 | 21 | 17.4 |
| Juan Uribe | 19 | 38 | 11 | 22.7 | 13.9 |
| Adeiny Hechavarria | 20 | 34 | 15 | 23 | 9.8 |
| Alex Rodriguez | 19 | 30 | 22 | 23.7 | 5.7 |
After a pretty impressive career, although it also came with its fair share controversy, we see A-Rod make this list. And it doesn’t look like he is going to be playing again this year, which casts some doubt on whether he is going to make it to 700 career home runs (he’s currently at 696). But more importantly, our poorest performer of 2016 looks to be Yan Gomes. I was inclined to say A.J. Pierzynski should actually be considered the poorest performer of the year since his standard deviation was about half of Gomes’, but then I noticed that Yan Gomes was in the 0th percentile in high-leverage situations — literally the worst. Not all-time worst, but still pretty bad! And I guess if you want to argue that the worst percentile should actually be 1, as in the 1st percentile, then you could make that argument, but the value was rounded to 0 when Yan Gomes registered a whopping -72 wRC+ in high-leverage situations. The second-worst was Gerardo Parra at a -59 wRC+; that’s a pretty significant gap between first and second. Fun-fact time: In high-leverage situations, Mike Zunino ran a 30.8% walk rate, although he also struck out 30.8% of the time too. Yasmani Grandal had a 30.4% walk rate to go with a much smaller 13% K%.
Everyone always seems to be looking for players who are on the extreme ends of the leaderboards, but let’s give some love to the unsung heroes of the world, the completely average performers! I wasn’t sure if I simply wanted to use mean percentile rank as a measure for averageness, so I decided to go with what I called Deviation in the table. Deviation is calculated by adding the standard deviations (SD) of a players percentile ranks to the Δ50 column. The Δ50 column is calculated as the absolute value of a players mean rank minus 50.
| Leverage Rank | |||||||
|---|---|---|---|---|---|---|---|
| Name | Low | Medium | High | Rank | SD | Δ50 | Deviation |
| Scooter Gennett | 55 | 46 | 49 | 50 | 4.6 | 0 | 4.6 |
| Ezequiel Carrera | 46 | 44 | 51 | 47 | 3.6 | 3 | 6.6 |
| Leonys Martin | 44 | 54 | 47 | 48.3 | 5.1 | 1.7 | 6.8 |
| Matt Duffy | 41 | 49 | 49 | 46.3 | 4.6 | 3.7 | 8.3 |
| Avisail Garcia | 45 | 51 | 42 | 46 | 4.6 | 4 | 8.6 |
| Howie Kendrick | 46 | 59 | 52 | 52.3 | 6.5 | 2.3 | 8.8 |
| Johnny Giavotella | 40 | 44 | 42 | 42 | 2 | 8 | 10 |
| Jason Castro | 47 | 49 | 62 | 52.7 | 8.1 | 2.7 | 10.8 |
| Jonathan Schoop | 62 | 53 | 54 | 56.3 | 4.9 | 6.3 | 11.2 |
| Brandon Phillips | 55 | 48 | 63 | 55.3 | 7.5 | 5.3 | 12.8 |
And Scooter Gennett comes away as the most average performer of the season! He also ran a 0.149 ISO on the season and I think 0.150 is usually considered average. Look how wonderfully average these guys were; we should all take a minute to enjoy the little things in life. I realize this may not be the sexiest table, but it’s still interesting. You might not be getting a whole lot out of these guys over an entire season, but they are going to go up there and do average things whether you like it or not.
Two tables left — hopefully you’re still with me here. Let’s look at consistency. People always say consistency is key. I guess that’s good advice except when you’re on the terrible end on the spectrum.
| Leverage Rank | |||||
|---|---|---|---|---|---|
| Name | Low | Medium | High | Mean Rank | SD |
| Ramon Flores | 21 | 20 | 21 | 20.7 | 0.6 |
| Ivan De Jesus | 32 | 32 | 33 | 32.3 | 0.6 |
| Mike Trout | 98 | 97 | 99 | 98 | 1 |
| Paul Goldschmidt | 88 | 86 | 88 | 87.3 | 1.2 |
| Johnny Giavotella | 40 | 44 | 42 | 42 | 2 |
| Yunel Escobar | 66 | 69 | 65 | 66.7 | 2.1 |
| Hunter Pence | 79 | 76 | 80 | 78.3 | 2.1 |
| Wilson Ramos | 81 | 80 | 85 | 82 | 2.6 |
| Alexei Ramirez | 26 | 32 | 28 | 28.7 | 3.1 |
| Austin Jackson | 43 | 38 | 37 | 39.3 | 3.2 |
Ramon Flores and Ivan De Jesus both had extremely consistent seasons; it’s just too bad they are on the wrong end of the spectrum. But I have to say Ramon Flores beats out Ivan De Jesus as he registered on average 12 percentile ranks poorer. In third we see Mike Trout showing incredible consistency while being the top performer in the league, followed closely by Paul Goldschmidt. It’s interesting see the top four players on this list from opposite ends of the spectrum, but the rest of this list bounces back and forth as well.
And here we are, the last one or as the title says “the Funky”. I found that volatility was the most interesting question, or which players showed the most boom or bust in 2016. Most of the players in this list performed best in low- and medium-leverage situations, often above the 90th percentiles.
| Leverage Rank | |||||
|---|---|---|---|---|---|
| Name | Low | Medium | High | Mean Rank | SD |
| Sandy Leon | 96 | 84 | 2 | 60.7 | 51.2 |
| David Peralta | 95 | 29 | 1 | 41.7 | 48.3 |
| Dansby Swanson | 23 | 99 | 15 | 45.7 | 46.4 |
| Yangervis Solarte | 93 | 73 | 5 | 57 | 46.1 |
| Mac Williamson | 59 | 95 | 4 | 52.7 | 45.8 |
| Alex Avila | 36 | 99 | 12 | 49 | 44.9 |
| Jarrod Saltalamacchia | 41 | 9 | 97 | 49 | 44.5 |
| Pedro Alvarez | 86 | 85 | 9 | 60 | 44.2 |
| Ryan Zimmerman | 21 | 85 | 1 | 35.7 | 43.9 |
| Kris Bryant | 98 | 91 | 19 | 69.3 | 43.7 |
After perusing though the list, one of the most interesting names that jumps out should be Jarrod Saltalamacchia and his 97th percentile rank in high-leverage situations last year. And here’s another twist, would it surprise you to hear that in 2016 Miguel Cabrera was the least-clutch hitter among all Tigers qualified hitters? Check out the Tigers leaderboard here. But the 2016 volatility award goes to Sandy Leon, who absolutely mashed balls in low-leverage situations, was no slouch in medium-leverage spots, but dropped off the map in high-leverage situations. I have no idea how BABIP relates to wRC+, but with Sandy Leon it looks like his BABIP reflects what was happening in the different situations (0.434, 0.393 and 0.190). There is probably some combinations of descriptive stats that would explain some of the variance, and BABIP may very well be included, but I’m not going to go into that here.
Hope you enjoyed this. If anyone wants a copy of the R code I used to make the graph and tables, leave a comment below and I’ll pass it along. I ended up finding a pretty cool library to create html tables in R so you don’t have to mess around with formatting and manual inputs. As long as you’re willing to put a little work into understanding css you can basically customize the look of your tables.