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.

Players who ranked highest in wRC+ across all leverage situations

	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.

Players who rated as the worst performers across all leverage situations

	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.

The most average performers of 2016 in wRC+

	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.

Table looking at the most consistent performers based on percentile rank
across the 3 leverage situation (low, medium and high)

	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.

Looking at players who showed the highest volatility based on percentile rank
across the 3 leverage situation (low, medium and high)

	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.