## The True Dickey Effect

Most people that try to analyze this Dickey effect tend to group all the pitchers that follow in to one grouping with one ERA and compare to the total ERA of the bullpen or rotation. This is a simplistic and non-descriptive way of analyzing the effect and does not look at the how often the pitchers are pitching not after Dickey.

I decided to determine if there truly is an effect on pitchers’ statistics (ERA, WHIP, K%, BB%) who follow Dickey in relief and the starters of the next game against the same team. I went through every game that Dickey has pitched and recorded the stats (IP, TBF, H, ER, BB, K) of each reliever individually and the stats of the next starting pitcher if the next game was against the same team. I did this for each season. I then took the pitchers’ stats for the whole year and subtracted their stats from their following Dickey stats to have their stats when they did not follow Dickey. I summed the stats for following Dickey and weighted each pitcher based on the batters he faced over the total batters faced after Dickey. I then calculated the rate stats from the total. This weight was then applied to the not after Dickey stats. So for example if Francisco faced 19.11% of batters after Dickey, it was adjusted so that he also faced 19.11% of the batters not after Dickey. This gives an effective way of comparing the statistics and an accurate relationship can be determined. The not after Dickey stats were then summed and the rate stats were calculated as well. The two rate stats after Dickey and not after Dickey were compared using this formula (afterDickeySTAT-notafterDickeySTAT)/notafterDickeySTAT. This tells me how much better or worse relievers or starters did when following Dickey in the form of a percentage.

I then added the stats after Dickey for starters and relievers from all three years and the stats not after Dickey and I applied the same technique of weighting the sample so that if Niese’12 faced 10.9% of all starter batters faced following a Dickey start against the same team, it was adjusted so that he faced 10.9% of the batters faced by starters not after Dickey (only the starters that pitched after Dickey that season). The same technique was used from the year to year technique and a total % for each stat was calculated.

Here is the weighted year by year breakdown of the starters’ statistics following Dickey and a total (- indicates a decrease which is desired for all stats except K%):

2012:

ERA: -46.94% with 5/5 starters seeing a decrease

WHIP: -16.16% with 4/5 seeing a decrease

K%: 47.04% with 4/5 seeing an increase

BB%: 6.50% with 3/5 seeing a decrease

HR%: -50.53% with 5/5 seeing a decrease

BABIP: -14.08% with 4/5 seeing a decrease

FIP: -25.17% with 5/5 seeing a decrease

2011:

ERA: 17.92% with 0/3 seeing a decrease

WHIP: -9.63% with 2/3 seeing a decrease

K%: -2.64% with 2/3 seeing an increase

BB%: -15.94% with 2/3 seeing a decrease

HR%: -9.21% with 2/3 seeing a decrease

BABIP: -15.14% with 2/3 seeing a decrease

FIP: -5.58% with 2/3 seeing a decrease

2010:

ERA: -23.82% with 5/7 seeing a decrease

WHIP: 1.68% with 5/7 seeing a decrease

K%: -22.91% with 1/7 seeing an increase

BB%: -2.34% with 5/7 seeing a decrease

HR%: -43.61% with 5/7 seeing a decrease

BABIP: -3.61% with 4/7 seeing a decrease

FIP: -10.61% with 5/7 seeing a decrease

Total:

ERA: -17.21% with 10/15 seeing a decrease

WHIP: -8.10% with 11/15 seeing a decrease

K%: -3.38% with 7/15 seeing an increase

BB%: -5.17% with 10/15 seeing a decrease

HR%: -32.96% with 12/15 seeing a decrease

BABIP: -11.04% with 10/15 seeing a decrease

FIP: -13.34% with 12/15 seeing a decrease

So for starters that pitch in games following Dickey against the same team, it can be concluded that there is an effect on ERA, WHIP, BABIP, and FIP and a slight effect on BB% and on K%. There is also a large effect on HR rates which we can attribute the ERA effect to. This also tells us that batters are making worse contact the day after Dickey.

So a starter (like Morrow) who follows Dickey against the same team can expect to see around a 17.2% reduction in his ERA that game compared to if he was not following Dickey against the same opponent. For example if Morrow had a 3.00 ERA in games not after Dickey he can expect a 2.48 ERA in games after Dickey.

So if in a full season where Morrow follows Dickey against the same team 66% of the time (games 2 and 3 of a series) in which he normally would have a 3.00 ERA without Dickey ahead of him, he could expect a 2.66 ERA for the season. This seams to be a significant improvement and would equate to a 7.6 run difference (or 0.8 WAR) over 200 innings.

Here is a year by year breakdown of relievers after Dickey (these are smaller sample sizes so I will not include how many relievers saw an increase or decrease):

2012:

ERA: -25.51%

WHIP: -1.57%

K%: 27.04%

BB%: -49.25%

HR%: -34.66%

BABIP: 30.23%

FIP: -38.34%

2011:

ERA: -17.43%

WHIP: 8.45%

K%: 6.74%

BB%: -5.14%

HR%: 7.34%

BABIP: 9.75%

FIP: -2.05%

2010:

ERA: -2.55%

WHIP: 7.69%

K%: -9.28%

BB%: 10.84%

HR%: 2.11%

BABIP: 4.23%

FIP: 9.43%

Total:

ERA: -16.61%

WHIP: 5.38%

K%: 7.50%

BB%: -12.65%

HR%: -8.53%

BABIP: 13.38%

FIP: -10.40%

As expected there was a good effect on the relievers’ ERA, FIP, K%, and BB%, but the WHIP and BABIP were affected negatively. This tells me that the batters were more free swinging after just seeing Dickey (more hits, less walks, more strikeouts).

So in a season where there are 55 IP after Dickey in games (like in 2012) there would be a 16.6% reduction in runs given up in those 55 innings. If the bullpen’s ERA is 4.20 without Dickey it can be expected to be 3.50 after Dickey. Over 55 IP this difference would save 4.3 runs (or 0.4 WAR).

Combine this with the saved starter runs and you get 11.9 runs saved or (1.2 WAR). This is Dickey’s underlying value with the team that he creates by baffling hitters. This 1.2 WAR is if Morrow has a 3.00 ERA normally and the bullpen has a 4.00 ERA. If Morrow normally had a 4.00 ERA than his ERA would reduce to 3.54 over the season with 10.2 runs saved for 200 innings (1.0 WAR) and if the bullpen has a 4.00 ERA normally as well, 4.1 runs would be saved there, equating to 14.3 runs saved or a 1.4 WAR over a season.

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I’m curious about the bias in the sample of bullpen usage after Dickey.

If, on the whole, Dickey keeps the score close more often than your average starter, the bullpen would be more inclined to use high leverage relievers for a shorter amount of time.

The bias was effectively removed by weighting the bullpen. See the example about Fransisco. I weighted the entire bullpen not after Dickey so that for the entire sample, each pitcher pitched to the same percentage of batters whether they were after Dickey or not.

Looks like the Dicky Effect is now indisputable! Nice work Graydon.

Any idea what the standard deviation is like on numbers like this?

Can you clarify this a bit — if a starter pitched to 80% of the team’s batters faced in starts after Dickey starts, that starter’s batters faced in not-after-Dickey starts would be changed to dominate 80% of the overall team’s non-Dickey numbers in your method? What if only 10% of the batters that starter faced were in not-after-Dickey starts? The team’s non-Dickey numbers would then be dominated by the results of a mere 20 innings or so, which would leave a strong possibility that the results are due to chance. Am I off-base here?

That is correct, but the not after Dickey starts were a lot larger than you would think due to multiple pitchers rotating in and out of the spot. Sample sizes: 1551 batters or 357 IP after Dickey, 5441 batters after or 1262 IP. There was never a starter that got more than 35% after Dickey other than Capuano in ’11 who had 51.6% batters faced after Dickey of the team but had 69 IP after Dickey, and 116 IP not after. So this was never an issue.

OK thanks — that helped get rid of a lot of my concerns. The other thing I have to wonder is how special Dickey really is in this regard; how many other pitchers show a similar or greater effect? That would be a huge pain to figure out, I’m sure, but would make this a lot more convincing to skeptics like me who suspect this could have been a fluke.

I have actually started to look at some other pitchers (4 so far) and the ERA’s fluctuate a lot but the FIPs all remain within around 5% of 0. Dickey’s FIP change is the largest by far. I’ve looked at Price, Kershaw, Verlander, and Buehrle.

I have a similar question to Steve here. Do all elite pitchers cause a similar effect?

To me, it looks like the ‘Dickey Effect’ was most obvious in 2012 (coincidentally his best season?).

Also, there is some work showing evidence that good hitting really is ‘contagious’. http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0051367

I’m leaning on the side that poor hitting is also contagious, and I’m guessing that you will find a similar ‘Dickey Effect’ with all elite pitchers.

Without context, it’s tough to draw any conclusions from what you have shown. Great start though.

If there is such a thing as the “Dickey Effect,” one possibility is that it has to do with his knuckleballs throwing off hitters. I’d want to look at Wakefield, Hough, Candiotti, the Niekros, etc. for something similar.

My concern is mainly that if you really looked at all the pitchers in the league, you might find a bunch of mediocre pitchers who, by pure chance, appear to have had a similar or greater “effect” on the pitchers after them.

I think if you do a thorough search, and the only pitchers you find with similar effects happen to be great or unusual pitchers, that would support the idea that the “Dickey Effect” is due to his greatness or uniqueness. If the pattern seems to be random, though… I’d lean towards the effect being a fluke.

Awesome multi-year analysis. Weighing the bullpen was important. Great job documenting “the Dickey Effect”. As a Yankee fan, has me even more worried for this year. Like Steve, I hope someone can follow-up this work with analysis for the Niekros, Wakefield, etc.

I really do like the analysis you did here, and initially I was completely on board with the Dickey Effect. However, as I thought about it some more, I came across one issue… That being the difference between a teams number 5 starter and their ace. I made a simple assumption that After the number 5 pitcher throws, the ace is going to pitch. Being the ace of the staff, he is going to put up better than average stats, or at least better than the guy at the end of e rotation. According to the Dickey Effect, then, the back of the rotation guy would be partly responsible for the Ace’s better than average statistics. This would lead me to the assumption that in some cases it would benefit the team to have a mediocre pitcher at the end of the rotation to maximize the output of the teams ace. Am I completely wrong about this? My guess would be that more analysis would need to be done on other pitchers and the effect they have on pitchers throwing after them to compare to “pitchers after Dickey” stats before we can say the Dickey effect is real.

Sorry, I know I’m making a lot of assumptions here, but I’m an economist, that’s what I do. Please clarify this for me, thanks!

N. James Turner:

I think the Dickey Effect is already operating under the suspicion that because Dickey’s pitch repertoire varies wildly from a conventional pitcher, that the subsequent pitcher might face a more “confused/frazzled/etc” team that has grown temporarily accustomed to Dickey’s knuckleballs. The ace on the staff should be expected to put up great numbers regardless of who he follows in the rotation. Trying to apply this effect is clearly out of context. What you would show is correlation and certainly not causation. You already explained what the correlation is due to: the #5 pitcher pitches before the ace. That’s all there is to it.

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