Pitch Type Run Values, as found on Fangraphs (using BIS classifications) and other locations, attempt to use linear weights to determine how good, or how bad, specific pitches are during each season for individual pitchers. Fooling around with these measures on fangraphs can tell us who has the best fastball, change-up, etc and can let us know when we should (seemingly) be angry with our favorite pitchers' choice of pitches during certain individual games. Thus these values can easily lead to arguments where someone argues Pitcher A should use Pitch Type 1 more often at the expense of Pitch Type 2 to get better results.
However, these run values have numerous problems when used in this way:
First, the results of each individual pitch type is NOT independent of the results/specifics of other pitches a pitcher can throw; in other words, a pitcher's curveball's results are to some extent affected by the results and properties of the same pitcher's fastball (and other pitches). Thus run values can't tell whether a pitcher would be better off using certain pitches more often and others less - a pitcher reducing the use of his worst pitches could lead to that same pitchers' other "better" pitches getting worse results.
Second, pitch type run values are heavily dependent upon the count in which pitches are thrown - strikes on 0-2 counts have a naturally higher value than on 3-0 counts, for example. So if a pitcher throws Pitch A in early counts, or counts where he's behind (say, a fastball), and throws Pitch B (say, a curveball) only when he's ahead or on 2 strike counts, Pitch B is almost certainly likely to look better with run values, even though the pitch may in fact be worse than Pitch A without context.
Third, and perhaps most importantly, pitch type run values do not attempt to correct for luck or defense. If a pitcher gives up 8 line drives off of a pitch and all are right at a fielder, the run values indicate that the pitch is really a great pitch (it got all 8 balls in play out!) even though we wouldn't expect this performance to necessarily continue. The end result of this is that the run values you see on fangraphs may tell you that a pitch is good, only for the reality to be that the pitch is just REALLY lucky (or the converse is true).
Now it's easy for people these days to make a guesstimate as to how "lucky" or "unlucky" a pitcher overall has been during the year, whether by looking at BABIP, LOB%, or just comparing pitchers' ERAs to more advanced ERA estimators (Whether these things actually tell us exactly how much of a pitcher's ERA this year is due to luck is another question, but they do give a decent indication of one way or the other). However, nowhere is there a metric easily listed (on fangraphs or something else) that can show the amount luck/defense has affected an individual pitch's results during a given year.
EXPECTED RUN VALUES attempt to do just that: strip away the results of luck and defense from pitch type run values so that the only thing remains is a true evaluation of how good a pitch's results have been.*
*Feel free to ignore the next two paragraphs if you don't care how Expected Run Values work.
Expected Run Values are the same as regular run values for pitches that aren't put into play (Called Strikes, Called Balls, Foul Balls, Hit by Pitches, etc.). However instead of using the actual results of pitches put into play, Expected Run Values simply assume that each ball hit into play has an average result based upon the batted ball type of the ball in play.
In other words: if Expected Run Values see that a ball in play is a ground ball, it calculates the result of that individual pitch as if it was an average ground ball. The same is true of line drives, fly balls, and pop-ups. So if a pitcher's in play results are all pop-ups, they'll result in really good Expected Run Values, whereas if they're all line drives, his Expected Run Values will be terrible.
Using this metric, we can look at who has the luckiest pitches in baseball so far in 2011. After the jump we'll do exactly that.
*Minor Housekeeping Note: The Pitch classifications for the data in this post come from MLBAM classifications and involve me combining different pitch types (Splitters and change-ups in particular) at times due to how the algorithm works. This may result in slightly different numbers than on Fangraphs or elsewhere.
NOTE THAT IN ALL OF THE TABLES BELOW, NEGATIVE RUN VALUES ARE GOOD FOR PITCHERS, WHILE POSITIVE RUN VALUES ARE BAD FOR PITCHERS (The Reverse of how Fangraphs does it)
The Top 5 Luckiest Four-Seam Fastballs
| Pitcher Name | Expected Run Value Per 100 Pitches (RVe100) | Run Value Per 100 Pitches (RV100) | Total Expected Run Value | Total Run Value | Difference between RV100 and RVe100 | Difference between Total Run Value and Expected Run Value |
|---|---|---|---|---|---|---|
|
Heath Bell |
-0.95 |
-3.13 |
-4.69 | -15.45 | -2.18 | -10.76 |
| Henry Rodriguez |
+0.56 |
-1.52 |
+3.29 |
-8.89 |
-2.08 |
-12.19 |
|
Anthony Swarzak |
-0.43 |
-2.47 |
-2.61 |
-14.94 |
-2.03 |
-12.33 |
|
Guillermo Moscoso |
-0.37 |
-2.01 |
-3.04 |
-16.51 |
-1.64 |
-13.47 |
| Henry Rodriguez |
+0.58 |
-0.99 |
+3.15 |
-5.38 |
-1.58 |
-8.53 |
Table 1: The Luckiest Four-Seam Fastballs and their Expected and Actual Run Values
The top 5 luckiest fastballs according to expected run values include 4 relievers and one starter - with relievers taking the top 3 spots by a good margin per 100 pitches. I'd thus note that a bit of this is likely NOT LUCK, as relievers are known to have lower BABIPs than starters (the Expected Run Values I use does not differentiate between the two types of pitchers). Thus Heath Bell may not be super-lucky as much as this would say.
That said, Guillermo Moscoso, the 5th starter for the Oakland As, has undoubtedly been lucky on his fastball - by run values, the pitch is really really good - saving over 13 runs more above average - but the expected run values shows that his pitch has really only been slightly better than average. It's obvious from Moscoso's overall numbers that he's due for regression somewhere and here it seems pretty clear that his regression is mainly going to come from his fastball
The Top 5 Luckiest Two-Seam Fastballs/Sinkers
(Note that Fangraphs doesn't differentiate between two-seam and four-seam fastballs)
| Pitcher Name | Expected Run Value Per 100 Pitches (RVe100) | Run Value Per 100 Pitches (RV100) | Total Expected Run Value | Total Run Value | Difference between RV100 and RVe100 | Difference between Total Run Value and Expected Run Value |
|---|---|---|---|---|---|---|
| Kevin Corriea |
-0.85 |
-3.15 |
-3.29 | -12.24 | -2.31 | -8.95 |
|
Doug Fister |
-1.07 |
-3.13 |
-6.59 |
-19.31 |
-2.06 |
-12.72 |
|
Ross Detwiler |
+0.48 |
-1.54 |
+1.69 |
-5.48 |
-2.02 |
-7.16 |
|
Roy Oswalt |
-0.75 |
-2.59 |
-2.59 |
-8.95 |
-1.84 |
-6.36 |
|
Alfredo Aceves |
-0.85 |
-2.49 |
-3.79 |
-11.14 |
-1.64 |
-7.34 |
Table 2: The Luckiest Two-Seam Fastballs and their Expected and Actual Run Values
Here we get 5 guys who have started this year, though Aceves has mainly functioned as a reliever. Kevin Correia's two-seamer has been really good this year - mostly due to things beyond his control. The same is true, to an even greater extent, with Doug Fister - his two-seamer is actually good, but it's not as insanely amazing as the results would show this year.
Ross Detwiler is impressive for two counts - one the fact that he threw enough pitches classified as two-seamers or sinkers in 31 innings to qualify for this list (355 sinkers thrown) and two, his fastball would have been WORSE THAN AVERAGE if not for things beyond his control. He will certainly not be happy to see the inevitable regression on this pitch.
Then we come to the highest profile pitcher so far, Mr. Roy Oswalt. His two-seamer also seems to have met a good deal of fortune this year. However, it should be noted that Oswalt has a four-seamer that has faced a good deal of bad luck which evens things out a little bit (though not completely) for his fastballs.
The Top 5 Luckiest Cutters/Cut-Fastballs (using MLBAM Classifications)*
*MLBAM has special issues with cutters - it has a hard time telling them apart from sliders or fastballs at times, so take the numbers here with a little grain of salt.
| Pitcher Name | Expected Run Value Per 100 Pitches (RVe100) | Run Value Per 100 Pitches (RV100) | Total Expected Run Value | Total Run Value | Difference between RV100 and RVe100 | Difference between Total Run Value and Expected Run Value |
|---|---|---|---|---|---|---|
|
Cole Hamels |
-1.63 |
-3.94 |
-5.62 | -13.59 | -2.31 | -7.98 |
|
Shaun Marcum |
-0.76 |
-2.97 |
-2.97 |
-11.56 |
-2.21 |
-8.59 |
|
Josh Beckett |
-1.10 |
-3.08 |
-5.15 |
-14.43 |
-1.98 |
-9.28 |
|
Mark Buehrle |
-0.85 |
-2.36 |
-2.48 |
-6.90 |
-1.51 |
-4.42 |
|
Dustin Moseley |
-0.09 |
-1.56 |
-0.35 |
-5.99 |
-1.47 |
-5.64 |
Table 3: The Luckiest Four-Seam Cutters and their Expected and Actual Run Values
Wow that's a lot of big names! A consequence of this is that the top 4 pitchers, though very lucky with the results on their cutter, actually have good to really good cutters even after you take out the influence of luck. Cole Hamel's cutter is by expected run values the 7th best cutter in the majors on a per pitch basis - but factors out of his control have made the pitch look like the best cutter in the majors this year (it's not - the top 5 are guys named Haren, Lester, Halladay, Rivera, and Lee - you may have heard of them).
Really the only one of these guys surviving entirely on luck has been Dustin Mosely, whose cutter has essentially been average once luck is taken into account.
The Top 5 Luckiest Splitters or Chnage-ups (using MLBAM Classifications)*
*Due to the similarities in these pitches and how they're classified, I'm combining these two pitch types, which may result in some odd results for pitchers who have both a change-up and a splitter.
| Pitcher Name | Expected Run Value Per 100 Pitches (RVe100) | Run Value Per 100 Pitches (RV100) | Total Expected Run Value | Total Run Value | Difference between RV100 and RVe100 | Difference between Total Run Value and Expected Run Value |
|---|---|---|---|---|---|---|
|
Kyle Lohse |
-0.09 |
-3.39 |
-0.39 | -13.42 | -3.29 | -13.03 |
|
Edward Mujica |
-1.78 |
-4.73 |
-5.82 |
-15.47 |
-2.95 |
-9.65 |
|
Cristhian Martinez |
-1.55 |
-4.18 |
-4.12 |
-11.13 |
-2.63 |
-7.00 |
|
Ricky Nolasco |
+0.58 |
-1.57 |
+1.59 |
-4.28 |
-2.16 |
-5.87 |
|
Jeremy Hellickson |
-1.12 |
-2.95 |
-7.48 |
-19.71 |
-1.83 |
-12.23 |
Table 4: The Luckiest Change-ups/Splitters and their Expected and Actual Run Values
Once again we see a few recognizable names here in Lohse, Nolasco, and Tampa Bay's Jeremy Hellickson as well as two relievers in Mujica and Martinez. Lohse has benefited by far the most from factors he can't control both on a per pitch and total level. In reality, Lohse's change-up is poor (it has above average results per pitch, but below average for a change-up). But thanks to luck/defense/other-things, it's looked like an elite change-up this year. Ricky Nolasco's change-up is in reality actually worse than Lohse's pitch - but a large amount of luck has let the pitch still look fine.
The other 3 pitchers here, Hellickson, Mujica, and Martinez, all have change-ups that are solid pitches, but they look a lot better due to luck.
The Top 5 Luckiest Sliders (using MLBAM Classifications)
| Pitcher Name | Expected Run Value Per 100 Pitches (RVe100) | Run Value Per 100 Pitches (RV100) | Total Expected Run Value | Total Run Value | Difference between RV100 and RVe100 | Difference between Total Run Value and Expected Run Value |
|---|---|---|---|---|---|---|
| Clay Mortenson |
-1.94 |
-5.25 |
-4.53 | -12.23 | -3.31 | -7.70 |
| Josh Johnson |
-1.66 |
-4.49 |
-3.77 |
-10.20 |
-2.83 |
-6.43 |
|
Sam LeCure |
-0.11 |
-2.93 |
-0.22 |
-6.06 |
-2.82 |
-5.84 |
|
Francisco Cordero |
-0.80 |
-3.33 |
-2.28 |
-9.46 |
-2.53 |
-7.18 |
|
Vance Worley |
-0.11 |
-2.36 |
-0.31 |
-6.46 |
-2.25 |
-6.09 |
Table 5: The Luckiest Sliders and their Expected and Actual Run Values
Once again we have a mix of guys who actually have good sliders (Mortenson, Johnson) who have had luck/defense propel their sliders into the ranks of the elite and those who have average (Cordero) or essentially worse (LeCure, Worley) sliders that look pretty good or better due to luck.
The Top 5 Luckiest Curveballs (using MLBAM Classifications)
| Pitcher Name | Expected Run Value Per 100 Pitches (RVe100) | Run Value Per 100 Pitches (RV100) | Total Expected Run Value | Total Run Value | Difference between RV100 and RVe100 | Difference between Total Run Value and Expected Run Value |
|---|---|---|---|---|---|---|
|
Josh Tomlin |
+0.40 |
-3.30 |
+1.36 | -11.09 | -3.70 | -12.45 |
|
Randy Wolf |
+0.77 |
-1.71 |
+3.55 |
-7.84 |
-2.48 |
-11.39 |
|
Phil Coke |
-1.49 |
-3.65 |
-4.39 |
-10.78 |
-2.17 |
-6.39 |
|
Jamey Wright |
+0.83 |
-1.18 |
+1.86 |
-2.63 |
-2.01 |
-4.49 |
|
Aaron Harang |
+0.16 |
-1.69 |
+0.34 |
-3.42 |
-1.85 |
-3.76 |
Table 6: The Luckiest Curveballs and their Expected and Actual Run Values
Yikes these are an ugly top 5: four of these pitches have below average curves - and in the case of Wolf and Wright, VERY below average curveballs - but have came ahead thanks to luck/defense and other external factors. Phil Coke is the only one of these guys who has much business throwing a curveball for a purpose other than to set up other pitches.
And that's it for now. Join me next time for: The Unluckiest Pitches in Baseball.