# The R.A. Dickey Effect

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, 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.

November 24, 2013

Good stuff! You might want to extend this research to other knucklers like Wakefield.

FWIW, I looked at the same thing WRT relievers in the same game and also found a significant “hangover” effect (I don’t remember by how much).

Also, there really is no particular reason to regress the effect that you found. I should say that the amount of regression is unclear. It sort of depends on what your probabilities (gut feeling?) are going in (the Bayesian approach).

If you are completely agnostic as far as the probabilities going in (a frequentist approach), I do not know how much to regress.

November 26, 2013

Interesting stuff Chris!

One thing I was wondering about is whether you need to control for quality of the pitchers with regard to ceiling effects. What I mean is that if you’re already a fantastic pitcher, there may not be a lot of room for improvement. For instance, imagine a fictional reliever that has only thrown strikeouts in all his appearances, no walks, no hits, nothing else. In this case, the reliever would not appear to gain any effect from following Dickey simply because he’s so damn good anyway. On the other hand, a middling reliever or starter has much more potential to benefit from this. Thus, the perceived Dickey effect might be larger when the pitching staff is weaker.

I will be honest, I do not completely understand how some stats, such as FIP are calculated, thus this sort of issue may already be accounted for in those stats. But it’s something to consider.

Also, is it possible for you or anyone else to explain what exactly it means to regress the results. What is the purpose of this? Thanks a bunch and best of luck in this endeavor!

November 26, 2013

Thanks for the comment. What you say is very true, and it is why I used percentages! So a guy with a 1.00 ERA will only see 0.18 runs/ 9 IP improvement where as someone with a 5.00 ERA would see a 0.9 runs/ 9 IP improvement theoretically.

FIP is calculated as 3*BB/IP – 2*K/IP + 13*HR/IP + C (3.05). FIP takes out the defense from the equation.

As far as regressing, the point is to attempt to take luck out of the equation. Starters after Dickey might just give up less home runs partially due to luck and partially due to Dickey. You could kind of say that FIP is a regressed version of ERA that takes out the luck of balls in play going for hits or not.

November 26, 2013

I’m in agreement with most of your assumptions and methods here, but one that is not so comforting is the assumption that it’s a knuckleball baffle effect.

I wouldn’t be surprised to see similar results from, say, a Pedro Martinez circa 2000.

Maybe we see some sort of backward results if you look at pitchers following a turdballer… who knows.

November 26, 2013

Standard Errors?

November 28, 2013

Got the tip to check this article out from DJF comments section and you were even getting love in Dave Cameron’s fangraphs chat.

I must say, some outstanding work here. Concise, well thought out but very detailed. I like it. Good luck, I will be reading more!