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Another Way to Look at the Twins’ Lifeless Bats

A metric for ideal plate appearances confirms this offense is bad

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MLB: JUN 28 Twins at Braves
Byron Buxton (25) swings at a pitch at his neck against Atlanta
Photo by Jeff Robinson/Icon Sportswire via Getty Images

“We’re not going to win unless we make adjustments at the plate and, ultimately, barrel a lot of balls and hit.”

– Rocco Baldelli, yesterday

The Twins were swept in Atlanta, outscored 13-3 in three games. Minnesota stranded 20 runners on base, hit 0-for-23 with runners in scoring position, and struck out 31 times.

After the game, Rocco Baldelli closed the clubhouse for a players’ only meeting and put his offense on blast in the media, publicly lamenting the team’s record-setting rate of strikeouts and inability to make adjustments to the pitching they are facing for the third time in less than a month.

Baldelli’s frustration finally bubbled over after the Twins struck out 8 times in less than five innings (14 total in the game) against Atlanta left-hander Kolby Allard, a soft-tossing spot starter who has never averaged even a strikeout per inning in his career.

The Twins management’s patient approach and belief that a good process will portend better future results haven’t come to fruition, and Baldelli indicated he is finally tired of waiting and sticking to the same, one-size-fits-all plans.

The Exceptions that Prove the Rule

Dan Hayes noted on Twitter that the Twins’ offense has scored 118 of their 343 total runs (34.4%) in just 12 games (14.6%) of their 82 played.

The large outburst of runs every 10 days or so, and the Statcast metrics that show the Twins more favorably, like hard-hit rate (40.9%, 14th), barrel rate (9.9%, 3rd), and expected production on contact (xwOBAcon, .399, 4th) haven’t proven to be indicators of better things to come.

They’ve shown to be the exceptions that prove the rule about their three true outcomes-oriented booms or busts approach. The results consistently have not been there.

While it generally pays to be process-oriented instead of results-oriented, it’s also important to understand the limitations of the measurements being used. Stats like the ones I listed just above from Statcast use a denominator of batted ball events, not all plate appearances.

That makes them prone to being misleading. Sure, the Twins have tended to hit the ball hard when they hit it, but they also don’t hit it very much, thanks to the league’s worst swing-and-miss rate (29.3%). If we adjust their barrel rate to be on a per-PA basis, it falls to 6th. Where they rank 8th in wOBAcon, they are just 21st in wOBA. You get the idea.

Measuring the Ideal Approach with IPA%

Most of Baldelli’s complaints have been about the Twins’ approach at the plate. What constitutes a good approach? When I wrote about the Twins’ three-true-outcomes orientation earlier this month, we had a good discussion in the comments about how approaches should be adjustable based on situations and how we don’t really have any metrics that give us insight into this.

We discussed that amateur baseball coaches have long made popular the idea of the “Quality At Bat” or QAB. The exact criteria vary from coach to coach, but typically include things like walks, hit-by-pitches, reaching on errors, hard-hit balls, advancing runners into scoring position, making the pitcher throw a lot of pitches (6 or more), driving in a run, and sacrifice flies and bunts. I kept charts of such things when I played in high school and college.

A couple of writers at Pitcher List, Johnathan Metzelaar and Christian Mack, explored the idea of “Ideal Plate Appearances” at the Major League Level over the past couple of years. Metzelaar based his version on Statcast’s most productive contact quality bins — barrels, solid, and flares/burners.

MLB Glossary

Add up those events and divide by plate appearances to get IPA%, a metric that is hosted on Pitcher List’s site at the player level.

Mack built on that to include walks in the numerator, a sort of obvious improvement given that so much of the Moneyball revolution was driven by recognizing the value of a walk and getting on base.

Earlier this week, I expanded on their earlier work and proposed the addition of sac flies, hit-by-pitches, and sac bunts to the numerator, and explored whether hard-hit batted balls (95+ mph exit velocity) were a useful alternative to the more complicated barrels, solid contact, and flares and burners.

In the end, I found that the following formula has a strong positive relationship with offensive production (as measured by wOBA) at a player level, based on qualified player data in the Statcast era (since 2015).

  • IPA% = (Barrels + Solid Contact + Flares & Burners + BB + HBP + SF + SH) divided by PA

The correlation of the above metric with wOBA across 968 player seasons in my data set was r = 0.736. That gives an r2 of 0.542, which is a stronger relationship than exists using the ideal contact quality bins (r2 = .2005) or hard-hit rate (r2 = .2765).

Replacing the ideal contact bins with hard-hit batted balls in the formula (so, IPA% = (Hard Hit Balls + BB + HBP + SF + SH) / Plate Appearances) reduced the strength of the relationship to r2 = 0.427, but also came with the advantage that it becomes reliable (via Cronbach’s alpha) in just 190 PAs, which is faster than the earlier formula (440 PAs), hard-hit rate (210 PAs), and expected weighted on-base average (xwOBA, 340 PAs).

This metric is a more complete representation of the good things a batter can do in a plate appearance than what we can often see via individual statistics like walk rate, strikeout rate, or hard-hit batted balls.

The 2023 Twins and IPA%

Here’s how the 2023 Twins stack up by this metric:

Team IPA%, Through June 28

Team IPA% PA BB HBP SF SH ICR IPA
Team IPA% PA BB HBP SF SH ICR IPA
LAD 39.61% 3057 325 33 33 1 819 1211
ATL 39.25% 3060 273 28 20 0 880 1201
TEX 39.04% 3153 281 29 24 5 892 1231
LAA 38.81% 3149 282 45 28 1 866 1222
STL 38.78% 3022 269 34 16 5 848 1172
BAL 38.38% 2986 264 21 24 10 827 1146
NYM 37.96% 2998 264 49 23 8 794 1138
TOR 37.86% 3098 257 27 16 2 871 1173
PIT 37.53% 2950 279 28 24 15 761 1107
CIN 37.50% 3120 293 43 21 12 801 1170
SDP 37.45% 3020 331 25 21 12 742 1131
SFG 37.37% 3080 276 39 21 8 807 1151
SEA 37.24% 2981 266 42 22 1 779 1110
TBR 37.22% 3133 273 44 22 5 822 1166
BOS 36.88% 3102 265 37 16 7 819 1144
KCR 36.56% 2951 214 38 21 6 800 1079
CHC 36.50% 2981 290 34 21 7 736 1088
WSN 36.47% 3008 203 37 18 8 831 1097
ARI 36.45% 3089 261 23 27 15 800 1126
HOU 36.45% 3018 255 32 22 7 784 1100
MIA 36.43% 3033 227 27 20 15 816 1105
NYY 36.25% 2916 239 23 18 3 774 1057
COL 36.02% 3068 236 27 25 4 813 1105
DET 35.95% 2965 259 19 17 5 766 1066
MIN 35.93% 3089 272 43 12 8 775 1110
PHI 35.54% 3011 247 24 22 4 773 1070
CLE 35.46% 2975 242 23 24 6 760 1055
OAK 34.14% 3049 270 46 13 17 695 1041
MIL 33.84% 2943 274 29 14 2 677 996
CHW 33.74% 3050 197 34 16 8 774 1029
Data from FanGraphs and Baseball Savant

Here’s how the Twins’ individual players show up with this metric:

Twins Players IPA% Through June 28

Name PA IPA IPA% BB HBP SF SH ICR
Name PA IPA IPA% BB HBP SF SH ICR
Matt Wallner 25 12 48.0% 3 3 0 0 6
Donovan Solano 223 99 44.4% 26 5 0 0 68
Alex Kirilloff 171 72 42.1% 20 4 0 0 48
Trevor Larnach 177 72 40.7% 23 0 2 0 47
Ryan Jeffers 141 57 40.4% 15 6 0 3 33
Christian Vazquez 183 73 39.9% 18 1 1 0 53
Kyle Farmer 171 64 37.4% 10 4 1 0 49
Jorge Polanco 127 46 36.2% 7 0 0 0 39
Jose Miranda 142 50 35.2% 9 1 0 0 40
Max Kepler 192 67 34.9% 15 3 2 0 47
Carlos Correa 303 104 34.3% 29 0 1 0 74
Willi Castro 194 66 34.0% 10 6 1 1 48
Edouard Julien 133 45 33.8% 15 1 2 1 26
Kyle Garlick 30 10 33.3% 2 0 0 0 8
Nick Gordon 93 31 33.3% 1 0 0 1 29
Royce Lewis 92 30 32.6% 3 1 0 0 26
Byron Buxton 259 84 32.4% 28 3 1 0 52
Joey Gallo 210 64 30.5% 28 2 0 0 34
Michael A. Taylor 223 64 28.7% 10 3 1 2 48
Data from FanGraphs and Baseball Savant

This isn’t surprising, given what we know about this offense. It confirms all the things we’ve observed while watching them and that Baldelli is complaining about.

The approach at the plate just isn’t very good.

It’s not poor luck or something that will turn around on its own. This offense needs a significant adjustment or it will sink this season just like the lack of pitching depth sank last season.


John is a writer for Twinkie Town and Pitcher List with an emphasis on analysis. He is a lifelong Twins fan and former college pitcher. You can follow him on Twitter @JohnFoley_21.