## The R.A. Dickey Effect – 2013 Edition

It is widely talked about by announcers and baseball fans alike, that knuckleball pitchers can throw hitters off their game and leave them in funks for days. Some managers even sit certain players to avoid this effect. I decided to analyze to determine if there really is an effect and what its value is. R.A. Dickey is the main knuckleballer in the game today, and he is a special breed with the extra velocity he has.

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%, HR%, and FIP) 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 Janssen 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 four 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.

The most important stat to look at is FIP. This gives a more accurate value of the effect. Also make note of the BABIP and ERA, and you can decide for yourself if the BABIP is just luck, or actually better/worse contact. Normally I would regress the results based on BABIP and HR/FB, but FIP does not include BABIP and I do not have the fly ball numbers.

The size of the sample was also included, aD means after Dickey and naD is not after Dickey. Here are the results for starters following Dickey against the same team.

It can be concluded that starters after Dickey see an improvement across the board. Like I said, it is probably better to use FIP rather than ERA. Starters see an approximate 18.9% decrease in their FIP when they follow Dickey over the past 4 years. So assuming 130 IP are pitched after Dickey by a league average set of pitchers (~4.00 FIP), this would decrease their FIP to around 3.25. 130 IP was selected assuming ⅔ of starter innings (200) against the same team. Over 130 IP this would be a 10.8 run difference or around 1.1 WAR! This is amazingly significant and appears to be coming mainly from a reduction in HR%. If we regress the HR% down to -10% (seems more than fair), this would reduce the FIP reduction down to around 7%. A 7% reduction would reduce a 4.00 FIP down to 3.72, and save 4.0 runs or 0.4 WAR.

Here are the numbers for relievers following Dickey in the same game.

Relievers see a more consistent improvement in the FIP components (K, BB, HR) between each other (11.4, 8.1, 4.9). FIP was reduced 10.3%. Assuming 65 IP (in between 2012 and 2013) innings after Dickey of an average bullpen (or slightly above average, since Dickey will likely have setup men and closers after him) with a 3.75 FIP, FIP would get reduced to 3.36 and save 3 runs or 0.3 WAR.

Combining the un-regressed results, by having pitchers pitch after him, Dickey would contribute around **1.4 WAR **over a full season. If you assume the effect is just 10% reduction in FIP for both groups, this number comes down to around 0.9 WAR, which is not crazy to think at all based off the results. I can say with great confidence, that if Dickey pitches over 200 innings again next year, he will contribute above 1.0 WAR just from baffling hitters for the next guys. If we take the un-regressed 1.4 WAR and add it to his 2013 WAR (2.0) we get 3.4 WAR, if we add in his defence (7 DRS), we get 4.1 WAR. Even though we all were disappointed with Dickey’s season, with the effect he provides and his defence, he is still all-star calibre.

Just for fun, lets apply this to his 2012. He had 4.5 WAR in 2012, add on the 1.4 and his 6 DRS we get 6.5 WAR, wow! Using his RA9 WAR (6.2) instead (commonly used for knucklers instead of fWAR) we get 7.6 WAR! That’s Miguel Cabrera value! We can’t include his DRS when using RA9 WAR though, as it should already be incorporated.

This effect may even be applied further, relievers may (and likely do) get a boost the following day as well as starters. Assuming it is the same boost, that’s around another 2.5 runs or 0.25 WAR. Maybe the second day after Dickey also sees a boost? (A lot smaller sample size since Dickey would have to pitch first game of series). We could assume the effect is cut in half the next day, and that’d still be another 2 runs (90 IP of starters and relievers). So under these assumptions, Dickey could effectively have a 1.8 WAR after effect over a full season! This WAR is not easy to place, however, and cannot just be added onto the teams WAR, it is hidden among all the other pitchers’ WARs (just like catcher framing).

You may be disappointed with Dickey’s 2013, but he is still well worth his money. He is projected for 2.8 WAR next year by Steamer, and adding on the 1.4 WAR Dickey Effect and his defence, he could be projected to really have a true underlying value of almost 5 WAR. That is well worth the $12.5M he will earn in 2014.

*For more of my articles, head over to Breaking Blue where we give a sabermetric view on the Blue Jays, and MLB. Follow on twitter @BreakingBlueMLB and follow me directly @CCBreakingBlue.*

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Brilliant research. I think this means that we would need to normalize the stats of the following pitchers to account for this, as well. No need to give these pitchers undue credit.

So cool! This is exactly the kind of baseball axiom I *wouldn’t* expect the data to support.

Great work! As David said I would not expect this to actually be true, but the large percentages certainly strongly suggests that the effect not only exists but also that it’s rather large.

Quantifying hidden value is so much fun.

Great work!

Brilliant.

Just, so brilliant.

This is really cool. I’m all for trying to find new things that we can add to WAR to make it more of an all-encompassing statistic (e.g. this, pitcher defense, pitch framing) as long as it is shown to be subject to more than luck. I really wouldn’t expect this to be supported by data, but it is, so I wonder if this is also the case for some other pitchers who are not kuckleballers.

Fantastic research.

I wonder if Toronto or NYM ever shifted him around in the rotation so that he wouldn’t pitch the last game in a series (so that they would reap the full benefits of him pitching).

Very nicely done.

Thanks everyone! Be sure to check out http://www.breakingblue.ca where I’ve even created my own ERA estimator which has been circling amongst saberists.

Playing on the PS2, my friend once brought in Randy Johnson in relief of Steve Sparks against me. That friend is an asshole.

Fantastic stuff. Just wondering if your WAR used includes infield pop-flies, along with FIP. I would assume higher pop-ups the next day from hitters trying to re-adjust their swing. This might further improve the WAR for next day pitchers.

I have expanded on the research and looked at starters after Tim Wakefield. Here are the results!

http://www.breakingblue.ca/2013/11/26/the-dickey-effect-part-ii-wakefield/

Great stuff ! My next move would be to see if there is a correlation to average fastball velocity of the aD pitchers – my hypothesis would be that throwing harder increases the aD effect.

Nice work! Might I suggest working out the uncertainties in the future, to give a sense of thei significance of your findings? It seems clear from the year-to-year variation that there is a high significance, as you have stated, but it seems as though you have enough data to generate errors from bootstraps, particularly for the pitchers following Dickey. But yeah, keep up the great work!

I’m not exactly sure how I would go about this, do you mind elaborating?

I definitely am interested in furthering and bettering the findings.

Sure. Imagine you had an infinitely large data set, you could split it up into an infinite number of sub-samples, re-do your analysis on each, and estimate the uncertainly from the variance (assuming a Gaussian distribution) of your output.

But for individuals (baseball players) you rarely have the luxury of large data sets. In those cases the bootstrap is a convenient estimator of uncertainty (a great reference for astronomers is Jasper Wall). Basically, you construct new, random samples from the original data set, which are the same size as the original data set. This is known as sampling with replacement, i.e., if you sample randomly from a sample you will inevitably choose the same piece of data twice, and some not at all. Do that as many times as is reasonable, and voila, quick and dirty uncertainties.

Let me know if that makes sense or not…

This is good stuff!

It would be interesting to figure out just what type of pitches have the best effect after a Dickey start. Everyone assumes its just the fastball and so we try to slot a flame thrower in after his starts. But what if it’s not?

Nice article Christopher! I wrote a similar article last week and after I wrote it someone sent me a link to your article so I included a reference to your article at the end of mine. Whereas yours is a more statistical or the value of a knuckleball pitcher approach, my article is more from the perspective of a Toronto Blue Jays fan and also talks about where is the best spot to pitch Dickey in the rotation to maximize this effect and how well the team did this last season. You can find my article here: http://www.battersbox.ca/article.php?story=20131125191541265

I hope you give it a read.

This is a superb piece of research. Another factor to bear in mind is that Dickey’s pre-2013 training schedule was significantly disrupted by pitching in the World Baseball Classic, which won’t be an issue in 2014.

Awesome stuff. My inclination is to think that you’ve hit on a larger trend than just Dickey/Wakefield/knuckleballers. For the same reason hitters do better the third time against a pitcher after seeing them twice previously, it would only make sense that hitters do worse against new pitchers, simply because last time they were up, the pitches looked very different. So to the extent a starter is very different from the reliever that’s coming next (aka Dickey followed by a traditional style reliever), or a more traditional starter is followed by a more different or specialized reliever (i.e. a side-armer), that’s where the effect would be most extreme and maybe provide the only noise-free-enough data to attempt to quantify the effect.

My speculation is that the numbers you’ve come up with for Dickey (1 WAR / 200 IP) could be the top end of this spectrum. So a change less drastic than knuckleballer starter to 95+mph fastball reliever could still provide some meaningful value. Relievers are so often put in the game based on handedness, but what if this effect were meaningful enough, even in the case of non-knuckleballer transitions, that it makes more sense to choose the most different type of reliever from the previous pitcher the batter faced. Again, not sure how to quantify all of this middle ground, but getting a handle on the most extreme versions is incredibly helpful and really well done.

This is really interesting. But, since I don’t fully understand your method of analysis/normalization, I’d like to see you run this with a random pitcher filling the role of Dickey, to see what kind of numbers come out. Maybe that would give some feeling for the errors/variations in the method. For example, use the guy right before Dickey in the rotation, or go with a totally different team.

Knuckle ballers are probably the best most extreme example of this, but I wonder how much it holds true for any pitcher throwing something funky. Randy Johnson throwing from that insane arm slot, or a current guy like Samuel Deduno who pitches with a lot of movement and weird sequences while have little command over his stuff. I would think the reliever or next days starter would benefit, at least for a bit, from following any different kind of pitcher from themselves. Funk has always had a home in the bullpen with side armers and all the like of un conventional pitchers maybe it is an un tapped market inefficiency to have some funk in the rotation.