To read Brian Cartwright’s Unfiltered post following up on one of the audience’s suggested topics, surf here.
Former Pirates‘ pitching coach Ray Miller had a simple motto: ‘Work Fast, Throw Strikes, Change Speeds’, but there’s more to not walking batters than just throwing strikes as the rate those strikes get put into play also has a large influence in determining bases on balls.
Back in college I had dreams of designing the next great baseball simulation, one even better that Strat-o-Matic. Many hours of Statistics and Economics classes were spent doodling in my baseball notebook, which is sitting on my lap as I type. One of my favorite ideas was modeling the batter-pitcher match up pitch by pitch, calculating the walks, strikeouts and balls in play as a function of two things:
- What percent of the pitches were thrown for strikes?
- What percent of those strikes were put in play?
Walks and strikeouts are interconnected. They both are a function of the strike percentage and the contact rate. In projecting a specific batter/pitcher match up, as in a simulation game, or in general how any observed batter or pitcher would project in a new environment (major league equivalencies) I believed it was not possible to make an adjustment to a player’s strikeout rate without a complementary change in the walk rate, and vice versa. I felt it necessary to do this analysis at the pitch level.
In the early 1980’s I didn’t have the data to verify that my mathematical models were accurate, so the notebook got shoved in a box. Unfortunately, 25 years later I struggle to understand those notes I myself wrote, but now I have something better-Retrosheet!
To find this relationship between balls and strikes and walk and strikeouts, I extracted all the plate appearances from the Retrosheet database where pitch records are present, generally all those beginning in 1988. For each batter and pitcher in each season, I counted the number of plate appearances, balls and strike, and walks, strikeouts and balls in play. The strike percentage (called strikes+swinging strikes+fouls+balls in play/pitches) and the contact rate (balls in play/all strikes) were rounded to decimal places, with the walk and strikeouts per plate appearance calculated for each combination of Str% and Con%.
Modern box scores provide pitches and strikes for each pitcher, allowing calculation of the strike percentage. Subtract hit batters, walks and strikeouts from batters faced to determine balls in play, and divide that by strikes to get the contact rate.
These graphs illustrate the walk and strikeout rates for a low strike% (0.57), average (0.62) and high (0.67), as the contact rate ranges from 0.23 to 0.40.
As the strike rate increases, as would be expected the walk rate decreases. Notice that for each given Str%, as the contact rate increases, the walk rate decreases as well. Without throwing any higher of a percentage of strikes, a pitcher can greatly lower his walk rate by pitching more to contact.
Pitchers who throw a high percentage of strikes have a very flat change in their walk rate as their contact rate is changed. If the pitcher is throwing strikes, whether with a high or low contact rate, he will not be walking many batters, and will have a higher strikeout rate than a pitcher with the same contact rate but a lower strike percentage.
Conversely, free swinging batters who advance a level will face pitchers who allow lower contact rates, ballooning the batter’s strikeouts while leaving his walks low. A patient batter with the same contact rate will not only keep more of his walk percentage, but also have less of an increase in strikeouts.
From 1988 through 2000, the major league average strike% was 0.615 and the contact rate 0.324, although the contact rate had been 0.335 to 0.336 up until 1990, then began a downward slide that accelerated in 1994 and 1995 (as home run rates rose), then settled into 0.314 to 0.316 from 1997 to 2000.
The strike zone was enlarged in 2001, with the strike% increasing from 0.615 to 0.627 and the contact rate dropping from 0.315 the previous four seasons to 0.309.
Here are individual pitching leaders and trailers in strike% from 2001 to 2008
Str% Con% BB% SO% Schilling, Curt 0.697 0.272 0.038 0.250 Rivera, Mariano 0.689 0.268 0.039 0.242 Byrd, Paul 0.688 0.346 0.040 0.122 Radke, Brad 0.687 0.331 0.031 0.141 Wells, David 0.684 0.343 0.036 0.131 Lieber, John 0.682 0.345 0.031 0.150 Smoltz, John 0.681 0.286 0.048 0.231 Towers, Josh 0.680 0.356 0.035 0.124 Oswalt, Roy 0.679 0.298 0.052 0.200 Maddux, Greg 0.669 0.362 0.032 0.148 Str% Con% BB% SO% Zambrano, Victor 0.573 0.311 0.123 0.167 Cabrera, Daniel 0.580 0.299 0.123 0.173 Rueter, Kirk 0.581 0.377 0.071 0.077 Romero, J.C. 0.582 0.297 0.114 0.187 Ishii, Kazuhisa 0.583 0.294 0.138 0.175 Estes, Shawn 0.586 0.336 0.105 0.140
Notice that a difference of only 12% from best to worst in strikes thrown results in roughly three times as many bases on balls. The pitchers with the best control are able to maintain excellent walk rates regardless of their contact rate, but this is not so for the pitchers with poor control.
Kirk Rueter never had a reputation for poor control, as his 0.071 walk rate is below the MLB average of .078, but he the same low percentage of strikes as Daniel Cabrera or Kaz Ishii. The difference is that Rueter allowed balls in play at a very high rate of 37.7% of the strikes he did throw.
The pitching leaders and trailers in contact rate
Str% Con% BB% SO% Prior, Mark 0.644 0.242 0.077 0.274 Kazmir, Scott 0.624 0.244 0.104 0.253 Wood, Kerry 0.619 0.247 0.102 0.271 Johnson, Randy 0.663 0.251 0.057 0.280 Harden, Rich 0.627 0.255 0.100 0.243 Perez, Oliver 0.618 0.257 0.118 0.235 Young, Chris 0.637 0.257 0.086 0.216 Martinez, Pedro 0.657 0.259 0.061 0.265 Santana, Johan 0.667 0.260 0.063 0.261 Peavy, Jake 0.644 0.262 0.073 0.242 Str% Con% BB% SO% Cook, Aaron 0.628 0.384 0.060 0.093 Wang, Chien-Ming 0.620 0.379 0.065 0.108 Rueter, Kirk 0.581 0.377 0.071 0.077 Silva, Carlos 0.655 0.377 0.039 0.099 Mays, Joe 0.625 0.369 0.065 0.102 Anderson, Brian 0.634 0.368 0.051 0.106 Maddux, Greg 0.669 0.362 0.032 0.148 Maroth, Mike 0.609 0.361 0.064 0.111 Duke, Zach 0.632 0.359 0.059 0.118
Oliver Perez at 0.618 and Chien-Ming Wang at 0.620 both have nearly average rate of throwing strikes, but because they are at opposite ends of the contact rate list have widely varying results, with Perez walking 83% more and striking out 118% more batters than Wang. Missing as many bats as Perez does, he can not afford to be anywhere near below average in his rate of throwing strikes. His best season, in 2004 with Pittsburgh, Perez was above average at 64.4%. Battling a knee injury this season, his strike percentage has fallen to 57% and the walks have skyrocketed.
A low contact pitcher must pound the strike zone to keep his walk rate manageable. A pitcher who relies on batters chasing pitches outside the strike zone (such as Oliver Perez or Ian Snell) is likely to be far less consistent, dependent on the skill of the batters in laying off those pitches. This is an important concept to consider when projecting minor league pitchers and batters. Minor league hitters with poor strike zone judgment such as Brad Eldred will likely under perform standard minor league projections.
I want to continue this research by looking at how strike percentage and contact rate change for both batters and pitchers when comparing minor to major league performances. Pitch locations and results are available via Gameday for the Texas and Southern leagues in Double-A and for all of Triple-A for the past few seasons.
- Are major league equivalent walk and strikeout rates calculated from str% and con% more accurate than directly converting walks and strikeouts?
- Can str% and con% be used to identify players less likely to survive a promotion?
- Where pitches are not available, can a regression equation be used to reliably estimate str% and con% from walks and strikeouts?
- This might also allow a pitch count estimator that considers the interaction between walks and strikeouts.
Walks and strikeouts cannot be analyzed independently of one another. Pitch analysis offers a simple tool to better model this interaction when studying batter/pitcher match ups.