Phantom Cold Streaks

In my last two articles on streaky hitters, I came up with a method to identify when hitters entered and exited hot and cold streaks. However, to really make a difference for fantasy players, streaks must both be identifiable and persistent. There’s no point in benching a hitter in a cold streak if that streak is just as likely to be over with as not when you realize it is happening. And so, for this article, I decided to look at hitter performances in the days following the recognition of streaks.

Previously, I defined a streak as a period of at least seven days where a hitter’s wOBA was 110 or more points above or below their seasonal wOBA. I reused a lot of those standards here, but rather than allow streaks to be different lengths, as soon as a hitter hit the seven-day minimum, I claimed that a streak was identified. Then, I evaluated his performance in the subsequent days, whether or not the streak continued according to the 110-point rule.

Meanwhile, I didn’t look at the streaks of every hitter. I looked only at cold streaks because, for fantasy-relevant players, they would more likely result in a start-or-bench decision. And, I looked only at the hitters who were most prone to cold streaks between 2014 and 2016, which I defined by rate of cold games over total games played. My theory there is that those hitters might have characteristics that make them particularly susceptible to cold streaks, which will hopefully show up more clearly in any results. You’ll notice several players on that leaderboard with reputations for streakiness, such as Jay Bruce and Colby Rasmus, as well as a fair number of power hitters, which aligns with my previous finding that home runs tend to drive these streaks.

Hitters Who Were Cold 30%+ of their PAs, 2014-16
Batter Games Cold Games Cold%
Steve Pearce 279 107 38.4%
Brandon Moss 420 161 38.3%
Yasmani Grandal 369 141 38.2%
Jung Ho Kang 229 86 37.6%
Hanley Ramirez 380 142 37.4%
Maikel Franco 248 92 37.1%
Giancarlo Stanton 338 121 35.8%
Jay Bruce 441 154 34.9%
Colby Rasmus 348 120 34.5%
Gregory Polanco 386 132 34.2%
Curtis Granderson 462 155 33.5%
David Murphy 261 86 33.0%
Chris Coghlan 372 122 32.8%
Seth Smith 409 134 32.8%
Mark Trumbo 389 126 32.4%
Paulo Orlando 214 68 31.8%
Freddie Freeman 438 138 31.5%
Carlos Gomez 381 119 31.2%
Scooter Gennett 387 119 30.7%
Danny Espinosa 389 119 30.6%
Matt Adams 320 97 30.3%
Yasiel Puig 331 100 30.2%
Kris Bryant 306 92 30.1%
Yangervis Solarte 392 118 30.1%
Melky Cabrera 448 135 30.1%

I’ll start with Bruce by himself to help illustrate the test I created. Over those three seasons, he had 18 distinct cold streaks—that is, they did not overlap with other cold streaks and were the earliest identified of each set of overlapping streaks. The first of those streaks was between 5/21 and 5/28 in 2014. Bruce produced a wOBA that was 178 points below his seasonal wOBA of .288 over that seven-day period. My test started with the day after the identification of the streak, which was 5/28, and then calculated his ensuing wOBA in the first day after, and then in the first and second days after, and then in the first, second, and third days after, and so on.

Here is the full breakdown of all of Bruce’s streaks and his ensuing wOBA in the days that followed:

Jay Bruce’s wOBA in the Days After a Cold Streak is Identified
Date 1 2 3 4 5 6 7
5/28/2014 .000 .127 .227 .227 .227 .311 .312
7/4/2014 .000 .300 .463 .445 .437 .418 .359
7/25/2014 .230 .226 .189 .189 .189 .189 .226
8/28/2014 .223 .127 .127 .127 .353 .318 .318
9/10/2014 .297 .254 .162 .191 .191 .157 .148
4/24/2015 .000 .695 .575 .610 .502 .440
5/10/2015 .223 .223 .148 .154 .154 .273 .374
6/15/2015 .920 .889 .889 .775 .652 .629 .629
8/8/2015 .223 .374 .249 .288 .262 .220 .212
9/16/2015 .698 .409 .659 .504 .508 .427
9/30/2015 .318 .343 .294 .313 .313 .313 .313
5/10/2016 .748 .748 .427 .415 .563 .539 .546
5/26/2016 .000 .198 .352 .324 .492 .497 .510
7/3/2016 .223 .409 .437 .437 .312 .313 .337
8/2/2016 .138 .310 .336 .409 .335 .335 .344
8/13/2016 .345 .329 .415 .373 .332 .285 .299
8/23/2016 1.270 1.270 .254 .254 .240 .216 .264
9/10/2016 .395 .252 .243 .243 .226 .175
All .304 .365 .343 .362 .369 .360 .356

With all of his streaks combined, Bruce was noticeably worse in his first day after the identification of a cold streak, but then his numbers increased and jumped around a bit. If cold streaks tended to persist—which would make them actionable in fantasy—then we would expect the wOBA trend to increase over the subsequent days. And when you look at all 25 players in total, that increasing trend does not exist.

Combined wOBA After Identifying Cold Streaks
Days After Identification PA wOBA
1 950 .366
2 1957 .360
3 2973 .355
4 3925 .343
5 4817 .343
6 5707 .340
7 6649 .336

In fact, these hitters’ wOBAs peak in the first day after the identification of a cold streak and then decline the rest of the way. No reason jumps out to me as to why there would be a declining trend, but the fact that there is no increasing trend makes it clear that you shouldn’t use a player’s current cold streak as a reason to bench him when you normally would not. Chances are, he’ll perform to his expected level of performance going forward.





Scott Spratt is a fantasy sports writer for FanGraphs and Pro Football Focus. He is a Sloan Sports Conference Research Paper Competition and FSWA award winner. Feel free to ask him questions on Twitter – @Scott_Spratt

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sabrtooth
7 years ago

This result seems to agree with Tango, MGL, and Dolphin’s findings in The Book: Hitter streaks weren’t predictive of upcoming performance. (I remember a pitcher’s last few games being mildly predictive, but don’t have the book on me at the moment.)