More On Spring Stats And Power, With An Eye on Chris Heisey

Title: More On Spring Stats And Power, With An Eye on Chris Heisey
Date: March 27, 2014
Original Source: Rotographs
Synopsis: This article attempted once again to improve on earlier methods for predicting breakouts based on spring training statistics.

On Tuesday, I wrote about wrinkles that I tried to add to the so-called Dewan Rule, hoping to leverage spring training statistics to help predict breakouts. It didn’t work, as was somewhat expected. In fact, Dewan himself admitted on Wednesday that the rule no longer seems to work.

However, a suggestion in the comments section led me to study the same data from a different angle, and it now seems a bit more promising.

In Tuesday’s piece, I looked for players who had a spring training isolated slugging of .070 greater than their career and prior year marks, hoping that those players – rather than just those with a slugging gain – would have been breakout candidates. Of the 44 players predicted for breakouts under this method, 21 saw an ISO increase, 22 did not, and one did not qualify.

Here’s how that success rate compares to the entire qualified player sample:

Method ISO Gain ISO drop % Gain
ISO sample 134 223 37.54%
All players 21 22 48.84%

It turns out that, whether due to declining offensive production, one-year randomness or other confounding variables, the method used (henceforth called Dewan Plus) did slightly better than just random selection.

Commenters suggested a different take though: the goal of a “breakout predictor” shouldn’t necessarily be to find gainers and losers, it should be to find, well, “breakouts,” so what if we looked at ISO changes in percentage terms instead of raw gains and losses?

Going back to last spring, we find that of the 21 players that improved, 13 saw at least a 25 percent increase in ISO, and five saw at least a 50 percent increase. Here’s how the numbers compare to the league rates:

Method ISO +25% ISO +50% ISO +100% ISO -25% ISO -50% ISO -100% N
ISO sample # 13 5 2 8 1 0 43
ISO Sample % 30.23% 11.63% 4.65% 18.60% 2.33% 0.00%
All players # 55 31 9 89 14 0 358
All players % 15.36% 8.66% 2.51% 24.86% 3.91% 0.00%

Now we might be on to something. While we’re only talking about a single season here – I’ll go back later and extend the data set to as many seasons as I can find spring stats for – the Dewan Plus grouping does a better job than random selection at picking breakouts at various thresholds and selects players who fail spectacularly less often.

In short, while this doesn’t necessarily make the method a slam dunk, looking at players who see a spring isolated slugging spike of .070 or greater may actually have some value.

As a refresher, here are the candidates using this methodology through Monday’s games:

Age Tm OppQual PA Spring ISO 2013 ISO CareerISO Spring-2013 Spring-Career
Chris Heisey 28 CIN 9.1 46 0.543 0.178 0.179 0.365 0.364
Brad Miller 23 SEA 9 54 0.5 0.153 0.153 0.347 0.347
Jordan Danks 26 CWS 9.1 45 0.405 0.138 0.115 0.267 0.29
Carlos Gomez 27 MIL 9.1 45 0.342 0.222 0.151 0.12 0.191
Jose Bautista 32 TOR 9 55 0.422 0.239 0.233 0.183 0.189
Brandon Phillips 32 CIN 9.1 48 0.334 0.135 0.158 0.199 0.176
Conor Gillaspie 25 CWS 9 46 0.296 0.145 0.14 0.151 0.156
Nolan Arenado 22 COL 9.2 45 0.293 0.138 0.138 0.155 0.155
Hunter Pence 30 SF 9.2 54 0.346 0.2 0.191 0.146 0.155
Mike Carp 27 BOS 9.1 40 0.325 0.227 0.177 0.098 0.148
Jed Lowrie 29 OAK 9.4 47 0.309 0.156 0.163 0.153 0.146
Junior Lake 23 CHC 9.1 43 0.275 0.144 0.144 0.131 0.131
Anthony Rizzo 23 CHC 9.2 41 0.29 0.186 0.174 0.104 0.116
Mike Aviles 32 CLE 9 42 0.231 0.116 0.128 0.115 0.103
Dan Uggla 33 ATL 9 57 0.311 0.183 0.212 0.128 0.099
Ian Kinsler 31 DET 9.1 56 0.271 0.136 0.181 0.135 0.09
Skip Schumaker 33 CIN 9.2 40 0.177 0.069 0.087 0.108 0.09
Martin Prado 29 ARI 9 41 0.225 0.135 0.139 0.09 0.086
Jimmy Rollins 34 PHI 9.1 44 0.236 0.096 0.157 0.14 0.079

And that leads me to a minor talking point for today – Cincinnati Reds outfielderChris Heisey, who has gone all Mike Bucci (read: Super Nova) this spring. The righty has clobbered six home runs in just 55 at bats, adding five doubles and a triple for good measure. He’s the biggest ISO gainer using Dewan Plus, and it has me moderately excited for his 2014 season.

The issue with Heisey through four seasons has been playing time, as he’s yet to total more than 375 plate appearances in a season. This obviously limits his counting stats, which could see him as a viable 20-10 candidate with a full workload. The reason he doesn’t get significant playing time is pretty simple: while he can put a charge into the ball, he rarely walks and strikes out quite a bit. He’s also shown a strong platoon split the past two seasons, though it was much the opposite early in his career.

There’s value in a fourth outfielder with a wRC+ of 92, especially one who can seemingly hold his own in all three outfield spots (I’m being generous with centerfield, though he’s logged 568.1 major league innings there). But his profile to this point doesn’t scream for more playing time, rather, it suggests he is what he is, a capable fourth outfielder.

However, this season you can craft two easy narratives that see him get more playing time: Billy Hamilton struggles, opening up an outfield spot, or Ryan Ludwick’s shoulder issues linger (or sap his production, as they did in a small-sample 2013). Our man-made Steamer projections currently peg Heisey for 56 plate appearances in left, 42 in right, 70 in center and 90 as a designated hitter, but he becomes an interesting name in deeper leagues if circumstance pushes him to 400 plate appearances.

With that kind of run, he may offer 15 homers, perhaps more if his spring stats are any indication. The OBP may be unsightly and he’s unlikely to hit far north of .250, but the power-speed combo is enticing, and it will cost you literally your final draft pick or auction dollar to take a stab. There are worse plays than spending a dollar to believe in a faulty model I adjusted a bit to maybe make kind of work. Right?


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