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Exploring Three True Outcome Quality


So there’s been a lot of attention paid to Three True Outcome guys recently. The subject was touched upon in a recent article by Craig Edwards, as well as in this community blog by Brian Reiff. These articles brought attention to guys who are notable for putting 7 of the 9 defensive players to sleep. However, what caught my attention the most was a comment on Craig’s piece by “steex” who proposed a hypothesis about these sluggers:

I think this makes selecting TTO players strictly by the numbers difficult. For me, the spirit of TTO is a player that does enough good (HR+BB) to balance out for a lot of bad (K). Harper and Votto don’t really fit that definition in the intended way, but rather show up on the list because they do SO much good (HR+BB) that their total HR+BB+K makes the cut despite having not as much of the bad (K).

I wonder if a better list of players comparable to one another would be obtained by first sorting by TTO%, then subdividing that by the percentage that Ks represent from the TTO events (i.e., K/[HR+BB+K]). That provides a lot of separation between guys like Harper, Votto, and Goldschmidt who have strikeouts represent less than 50% of their TTO events and guys like Carter, LaRoche, and Belt who have strikeouts as more than 65% of their TTO events.

This was also supported by follow up comments speaking about how they differentiate the players into two groups, those who strike out at a higher clip and those who have BB% and HR% compensate for a reduced K%. My goal was to figure out whether the quality aspect of the Three True Outcomes was different between these high-K% players and the low-K% players, beyond the walks and strikeouts.



First, let me define how I picked out my sample, and how I classified the players into two groups, and then I’ll begin to discuss the details of the study. I pulled all the data from 2010-2014 for player seasons who qualified for the batting title (minimum 502 Plate Appearances). This gave me a sample of 723 player-seasons (where a single player may be listed as a qualifier separately for up to five seasons). Of these 723 player-seasons, I set the Three True Outcome bar at 40%. Why 40%? Well the simple average (weighted to PA) was 29% Three True Outcome (I’ll abbreviate to TTO from now on), with a standard deviation of approximately 8%. So that would make 40% TTO somewhere around 1.5 standard deviations above the mean, which seemed like a reasonable line to draw in the sand.

There is now a sample of 52 player-seasons (7.2% of the qualifiers). From here, I had to draw a new line, and I wanted to go by “steex”’s suggestion of using the proportion of strikeouts to TTO% as the barrier. The key was getting a decent number of player-seasons on either side. I started off with 50% (using the formula K/[HR+BB+K]), but that would have left me with only two player-seasons (2011 Bautista, 2013 Votto, for those who are curious). I bumped it up continually until I reached a 60% ratio, which seemed to be reasonable. That placed 11 player-seasons in the low-K TTO group (which will be referred to as TTO-L) and 41 player-seasons in the high-K TTO group (which will be shown as TTO-H).

The whole TTO population is now divided into two groups, TTO-L (with 11 player-seasons) and TTO-H (with 41 player-seasons). Now what? I was truly curious about how these two groups differed in their hitting abilities. It seems fairly obvious that those who have lower K% and higher BB% will have higher (better) wOBAs, wRC+s, and the like (just due to trading strikeouts for walks). As Craig showed in his article, the average TTO player is an above average hitter due to a typically lumbering stature and a penchant for not being great at defense. Those who aren’t above average hitters and are bad at defense usually find themselves riding minor league buses around the country. But I’m not trying to compare TTO hitters to non-TTO hitters, rather comparing the two halves based on TTO quality.



I decided to compare them using  statistics that might glean differences between good and bad hitters. I looked at batted ball distributions to start. I compiled the GB%, LD%, FB% and IFFB%, as well as the PULL%, CENTER% and OPPO% from the leaderboards (plus HR/FB for good measure), and computed the mean, standard deviation, and p-value based on a two-tailed T-Test. The results are in TABLE ONE:


(legend)Statistical Significance
p < 0.1
p < 0.05
p < 0.01


TABLE ONE: Batted Ball Distributions

TTO-H TTO-L t-test
Measure     mean-H     StDev-H     mean-L     StDev-L     p-val  
COUNT 41 plyr-sea 11 plyr-sea
GB% 38.1% 5.5% 38.9% 3.9% 0.654
LD% 19.6% 3.0% 19.0% 3.5% 0.572
FB% 42.3% 5.3% 42.2% 5.5% 0.956
IFFB% 8.3% 4.1% 9.8% 5.2% 0.314
Pull% 43.9% 5.1% 45.8% 7.7% 0.332
Cent% 33.6% 3.7% 31.4% 2.5% 0.070
Oppo% 22.6% 3.7% 22.9% 6.9% 0.846
HR/FB 19.4% 4.9% 19.4% 4.0% 1.000


Interestingly enough, the batted ball distributions are very similar between the two groups. The groups are pretty much interchangeable, with the only thing close to being statistically significant is the percentage of balls hit to center field. However, when looking at that in the bigger picture of pull/center/opposite, the numbers are nearly identical. So far, the two groups are relatively indistinguishable from one another.



At this point, my mind went in another direction: do TTO-L player strike the ball better than their TTO-H counterparts? If you’ve got a good eye and can take a walk more easily, then you’re probably able to see the ball better, and therefore are able to drive the ball harder. So, even though it may not have manifested itself in the GB/LD/FB numbers, perhaps these “elite” players in the low-K group have better pop. To evaluate this, I pulled the HARD%, MED%, and SOFT% of balls by each group, along with BABIP for good measure, summarized in TABLE TWO:


TABLE TWO: Batted Ball Authority

TTO-H TTO-L t-test
  Measure     mean-H     StDev-H     mean-L     StDev-L     p-val  
COUNT 41 plyr-sea 11 plyr-sea
Soft% 15.5% 3.5% 16.0% 3.4% 0.925
Med% 48.6% 4.2% 46.6% 4.9% 0.182
Hard% 35.9% 4.0% 37.5% 3.4% 0.231
BABIP 0.297 0.034 0.307 0.043 0.417


Again, a little surprising to me. There’s no statistically significant difference between these low-K guys and high-K guys in terms of batted ball authority. Each group hits roughly the same, with the low-K guys trading a few medium hit balls for some hard hit ones (albeit not enough to differentiate the groups). BABIP would manifest itself in these guys striking the ball harder, and it comes out roughly even. One note that BABIP would control itself here more than in most hitter studies because the subset of TTO players typically have similar builds and are not artificially increasing BABIP by beating out infield hits (neither group would have a distinct advantage).



So where do these two groups separate? Something has to cause the disparity between the groups and show a differential in ability. And that something is at the plate in their selectivity – which only makes sense. Players who draw walks are those who lay off bad pitches out of the zone, and those who strike out typically struggle to identify strikes from balls, or lack the ability to contact balls when they swing (usually not both, or else they wouldn’t be in the majors). The data is summarized below in TABLE THREE:


TABLE THREE: Batting Selectivity & Contact

TTO-H TTO-L t-test
Measure   mean-H     StDev-H     mean-L     StDev-L     p-val  
COUNT 41 plyr-sea 11 plyr-sea
Z-Swing% 68.0% 5.0% 65.9% 4.2% 0.208
O-Swing% 29.5% 5.2% 25.5% 3.3% 0.020
Swing% 46.1% 4.1% 42.5% 2.1% 0.007
O-Contact% 53.9% 5.3% 57.5% 6.7% 0.065
Z-Contact% 79.4% 3.6% 83.3% 3.3% 0.002
Contact% 70.1% 3.6% 74.3% 4.6% 0.002
SwStr% 13.6% 2.4% 10.7% 2.3% 0.001



Here’s all that red you’ve been waiting for. Starting with the first three rows, there’s a statistically significant difference (p < 0.05) between the two groups in swinging at balls (O-Swing%), which goes to show the selectivity of the TTO-L group is better than the TTO-H group. In rows four to six, we see that for swings on pitches both in and out of the zone, the TTO-L group makes contact more often, with in-the-zone contact being statistically significant at the p < 0.01 level. To summarize this table, the TTO-L hitters don’t swing as often, but when they do they are better at making contact with the pitch as compared to the TTO-H batters.



The final table, TABLE FOUR, summarizes the groups for anybody who was curious. 

TABLE FOUR: Group Summary

TTO-H TTO-L t-test
  Measure     mean-H     StDev-H     mean-L     StDev-L     p-val  
COUNT 41 plyr-sea 11 plyr-sea
HR% 4.8% 1.3% 4.9% 1.2% 0.819
K% 29.2% 2.8% 24.0% 3.5% 0.000
BB% 11.0% 2.0% 15.2% 2.5% 0.000
wOBA 0.341 0.031 0.379 0.034 0.001
TTO% 45.0% 4.3% 44.0% 2.8% 0.470


Obviously above you see that the K-rates and BB-rates are statistically significant, which only makes sense because that’s how we divided the groups, so that was artificially implanted. And, of course, you’ll always have a better wOBA if you walk more and strike out less, because walks count for approximately 0.7 runs based on linear weights each.



Of the 723 player seasons between 2010 and 2014, inclusive, 52 were deemed to be Three True Outcome seasons (with 40% of the plate appearances ending in BB, K or HR). From there, the group was subdivided into two by the relative amount of K’s compared to total TTO% (with [K%/TTO%]>60% as TTO-H, and [K%/TTO%]<=60% as TTO-L.

The groups were compared against one another on Batted Ball Distributions, Batted Ball Authority, and Batting Selectivity & Contact. The vast majority of the statistically significant differences between the groups appeared in the third table, with the TTO-L group displaying a better eye for strikes, while also contacting the ball better when they decided to swing. Perhaps the most interesting finding of the study was that this increased contact did not manage to create better authority when hitting the ball, nor did it change the batted ball distribution significantly. Just because the TTO-L group made contact more often on their swings did not mean they were able to drive the ball better than the TTO-H players.

Just a quick thank you to end this, to the FG community comments that inspire people to write things like this and make my last college summer a little more (less?) exciting.