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Advanced Analytics: Two-Strike% - Quantifying the Effects of Staying Ahead in the Count

Coaches have been teaching their pitchers to throw strikes and stay ahead in the count for decades. They’ve known that pitchers who can stay ahead in the count last longer in games and are better able to shut down opponents, but they have been unable to measure the quantitative impact.1 Now, using data from more than ten million Plate Appearances and two million innings scored by High School level teams on GameChanger in 2017, we can quantify the effects of staying ahead in the count, and give coaches quantifiable proof to support what they’ve been doing so well for so long.2

The first piece we wanted to quantify is the effect of the First Pitch Strike, and how teams whose pitchers fall behind in the count 1-0 fare compare to those that tend to get ahead 0-1. The following graph displays the correlation between First Pitch Strike Percentage (FPS%) and the overall team ERA for more than 15,000 High School level teams. The teams in red were 2017 High School State Champions (according to MaxPreps):

FPS% vs Runs.png

According to the graph, the higher a team’s FPS%, the lower their ERA will be. A correlation coefficient or R2 value of a graph measures how close the data falls to the regression line (the blue line). The correlation coefficient of 0.183 for this graph means that FPS% explains about 18% of teams’ ERAs, which is evident by the cluster of points around the regression line (a correlation coefficient of 1 would have all the points in a straight line on the regression line).

Since we know the correlation is linear, in order to calculate the effect of staying ahead, we grouped the teams by those with an FPS% greater than the median of 57% and those below the median and found the following:

  • Teams that tend to get ahead 0-1 (FPS% above the median) have a team ERA of 3.86, compared to an ERA of 5.72 for those that fall behind 1-0.
  • Teams that get ahead 0-1 hold opponents to a 0.245 Batting Average while those teams that tend to fall behind saw their opponent’s Batting Average rise to 0.267.
  • While just 50% of the teams got ahead 1-0, 81% of the 2017 High School State Champions got ahead in the count.3

Now that we understand the importance of getting the First Pitch Strike, we want to see if getting the second strike is more or less important. The graph below shows the correlation between the percentage of counts that get to two-strikes (Two-Strike%) and team ERA:4

Two-Strike% vs ERA.png

Like with FPS%, the correlation between Two-Strike% and ERA is also linear. As a team’s Two-Strike% increases, their team ERA will likely decrease. The correlation coefficient of 0.244 signifies that Two-Strike% is a stronger predictor of ERA than FPS% (which had an R2 of 0.183). The fact that the points hug the regression line much more closely than in the previous visualization confirms that assertion.

Once again, in order to quantify the differences between teams that tend to get ahead and those that fall behind, we divided the data set into teams with a Two-Strike% above the median of 43% and those whose mark is below that number. The notable differences are below:

  • Teams that get to two-strikes more than 43% of the time (i.e above the median Two-Strike%) have an ERA of 3.83, whereas teams below the median have an ERA of 5.79.
  • Batters struggle in a two-strike count, hitting just 0.234, while batters that manage to stay ahead in the count hit 0.280. This differential of 0.046 is more than double the differential of 0.022 for FPS%.
  • 83% of 2017 High School State Champions get to two-strikes, significantly more than the 50% of teams overall.

Now that we have quantified the differences for teams when it comes to getting First Pitch Strikes and getting to Two-Strike counts, we will try and quantify success in as many different counts as possible. In order to do this, we split the teams into three different groups:

  • Group A: Teams with both a FPS% and Two-Strike% above the median. These teams often stay ahead in the count and are finding themselves in 0-2 or 1-2 counts more often than not — strong pitcher counts.
  • Group B: Teams with a FPS% above the median and a Two-Strike% less than the median or teams with a FPS% below the median and a Two-Strike% greater than the median. These teams either get ahead and fall behind, or fall behind and get ahead, ending up in 1-1, 2-1, 2-2, or 3-2 counts most often — fairly even counts.
  • Group C: Teams with both a FPS% and a Two-Strike% less than the median. These teams often fall behind in the count and end up in hitter’s counts of 2-0, 3-0, and 3-1.

The following table quantifies the varying levels of effectiveness for these groups, showing the differences in median FPS%, Two-Strike%, ERA, and opponent Batting Average between the teams that comprise the groups:

Group (Counts)




 Opponent AVG 

Group A (0-2, 1-2)

60% 48% 3.36 0.229

Group B (1-1, 2-1, 2-2, 3-2) 

57% 44% 4.65 0.255

Group C (2-0, 3-0, 3-1)

54% 39% 6.67 0.286

As you can see, just a few percentage points in FPS% and Two-Strike% can take your team from amongst the worst to an above-average team. Teams that fail to get ahead allow runs at close to double the rate of those who get ahead, and hitters tee off on those teams as well, hitting with a Batting Average almost 0.050 higher than against teams that get ahead.

Moreover, while getting the First Pitch Strike is important, a pitcher’s ability to stay ahead in the count consistently can significantly alter the story of your season. By training your team to throw six First Pitch Strikes every ten at-bats rather than five, your team will get into more pitcher’s counts, dominate hitters, and win more games.

From Michael Model of GameChanger.

1 ERA = (Earned Runs/Innings Pitched)*9

2 Data is from teams composed of players between the ages of 13 and 18, on High School and Travel/Select teams. 

3 According to maxpreps.com

4 A two-strike count includes any of 0-2, 1-2, 2-2 or 3-2

Baseball, Baseball Stats & Scorekeeping, Advanced Analytics