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The Machine That Goes "Ping": Flattened seams and raised offense in college baseball

The NCAA changed to a flat-seam baseball to increase home runs. What unintended consequences has the switch had?

The Arizona Wildcats (shown here at the 2012 College World Series) currently lead Division I with a .342 batting average.
The Arizona Wildcats (shown here at the 2012 College World Series) currently lead Division I with a .342 batting average.
Bruce Thorson-USA TODAY Sports

In an effort to increase scoring, the NCAA has switched to a flat-seamed baseball this season. A recent study by Washington State's Sports Science Laboratory showed that flat-seamed baseballs traveled significantly farther than the raised-seam balls the NCAA had been using when launched at the same velocity and angle.

The new ball was introduced to combat the NCAA's previous change, limiting the types of legal aluminum bats to those that produced the same batted ball velocities as wood bats. As a result, the offensive explosion of the late 90s and 2000s disappeared, and 2014 had fewer home runs per game than any NCAA season since aluminum bats were introduced in the 1970s.

One month into the season, the effects of the new ball are already visible. The NCAA was quick to trumpet the apparent power surge: despite a cold February, home runs per game jumped from 0.33 in the first month of 2014 to 0.47 HR per game so far in 2015. But observers claimed a number of other effects as well. On his March 2 podcast, Kendall Rogers said,

From talking to coaches and just my opinion from following games, I don't think there's any doubt that there's at least the ability to come back ... there's more power production, and I feel like there's a lot more runs being scored.

And in an article on Collegiate Baseball News, former player Dan Blewett said the flat-seamed ball could have surprising effects on pitcher performance.

Pitchers in the college game will suddenly find their fastballs doing things they've never done before, and they'll be scrambling for answers. But, the issue comes down to pitch slippage, predominantly on breaking balls.

We pulled the data from the first month of the 2015 season; let's see which of those claims hold water.

Offensive Numbers

The first step is to check the NCAA's claim about the power surge. We pulled data from the first month of the previous three Division 1 baseball seasons, focusing only on those games between D1 opponents. And the early returns supports the conclusion: home runs are up, back to around 2012 levels. And the return of offense shows up in other statistics too: isolated power is back up to 2012 levels, and runs per game have increased from 5.1 R/G to 5.3.

2012 173,216 151,186 1.69% 0.105 5.47
2013 170,783 149,051 1.35% 0.095 5.25
2014 194,840 170,671 1.27% 0.091 5.09
2015 166,670 145,939 1.79% 0.107 5.31

Control of Pitches

It's not quite as simple to check whether pitchers are having a hard time gripping the ball. There certainly isn't any COMMANDf/x data from the college game; a lot of games don't even have balls and strikes recorded! As proxies for control, let's look at walks, hit batters, wild pitches, and strikeouts* per plate appearance.

* - Yes, strikeouts, because you never know: Maybe wilder pitches are harder to track for the hitters.

But actually, none of these statistics are out of line with the past few seasons. Strikeouts are up a tick (which bears watching, but could be caused by any number of factors), and wild pitches and passed balls have risen as well. But walks are flat and hit batsmen are actually down through one month. The new ball might be harder for pitchers to control, but so far, it doesn't seem to be having too much of an effect on the game.

Year PA AB K rate BB rate HBP rate
2012 173,216 151,186 17.7% 9.8% 2.9%
2013 170,783 149,051 17.4% 9.8% 2.9%
2014 194,840 170,671 17.2% 9.5% 2.8%
2015 166,670 145,939 18.9% 9.8% 2.6%


The last thing to check is the frequency of comebacks using win expectancy. In The Book, Tom Tango et al used Markov chains to compute theoretical win expectancy (the probability that a team wins given a certain base/out state, score differential, and inning). But we'll use a simpler, observation-based measure of win expectancy, essentially counting the number of times each state appears in our play-by-play database, and the number of times the home team ended up winning. Here's an example from last season's data, for example. Note that, in order to get the largest possible sample size, these win expectancy numbers were derived from the entire seasons in question, not just the first month.

NCAA D1 win expectancy by score differential

But we don't see any real difference in the win expectancy curves above. If comebacks were more likely, we'd expect home teams to lose more games when up big and win more games when down big. What we're actually seeing is home teams consistently winning more games regardless of score, but this is easy to explain: This early in the season, most games will be played at schools in warmer climates, which are (for obvious reasons) typically stronger programs. In other words, home teams are winning more so far in 2015 because home teams are (as a rule) better than their guests.


We're just about a month into the season, and it's possible that offense will jump even further once the weather improves. But at least we can say the new ball is doing a little something -- but not too much! -- to bring runs back into the college game. That's good, because the next step is relaxing those bat restrictions, and that could mean dangerous liners back at pitchers and (just as bad) the return of those hideous football-looking scores in Omaha.

. . .

All statistics courtesy of NCAA. These statistics are freely available in MySQL-compatible format through Bryan's GitHub page. Special thanks to Christopher D. Long and Meredith Wills for their web scraping code.

Bryan Cole is a featured writer for Beyond the Box Score and a Tulane Green Wave fan. You can follow him on Twitter at @Doctor_Bryan.