The adage of “if you get a hit 3 times out of 10 you will be in the Hall of Fame” is something that is said constantly throughout baseball communities. Though likely true, it lends credibility to the idea that the mainstream take on offensive statistics is that batting average is king. Amongst parents, players, and even coaches it is the most talked about statistic.
Despite the popularity around batting average, the most revealing statistic in baseball is OPS (On-Base Plus Slugging).
In 2019 Courtney Pergio posted a blog to Towards Data Science where he did a deep dive on OPS being the most important offensive statistic. I pulled a few pieces from his blog below (Link to his blog post is here.)
Dictionary.com defines baseball in this way:
(Baseball is) a game of ball between two nine-player teams played usually for nine innings on a field that has as a focal point a diamond-shaped infield with a home plate and three other bases, 90 feet (27 meters) apart, forming a circuit that must be completed by a base runner in order to score, the central offensive action entailing hitting of a pitched ball with a wooden or metal bat and running of the bases, the winner being the team scoring the most runs.
Courtney Pergio - Stats for Baseball Fans: The Single Metric for Offense is OPS.
We set off on this analysis by calculating some statistics that you’ll find projected on scoreboards throughout MLB stadiums. Potential metrics include batting average (AVG), slugging percentage (SLG), on-base percentage (OBP), on-base percentage plus slugging percentage (OPS) as well as other common hitting metrics (AB, H, 2B, 3B, HR, etc.)
Some of these metrics we had to create from scratch and their definitions are shown below:
SLG = (hits+ doubles + (triples2) + (HRs3)) / at bats
AVG = hits /at bats
OBP = (hits + walks + hit by pitch) / (at bats + walks + hit by pitch + sacrifice flies)
OPS = OBP + SLG
Earlier, I mentioned we normalized our data by using team seasonal totals rather than individual player totals. I further normalized by removing some outlier seasons. Below are full details of my team outlier removal:
- Removed teams before 1970: Several key metrics weren’t tracked prior to the 1970 season (including sacrifice flies, hit by pitch and others.)
- Removed team seasons where the number of games played was below 156: This would remove seasons that were cut short by strikes and other schedule oddities. (Goodbye 1994 season.)
- Removed teams that do not play in the National or American Leagues: We don’t care about the minor leagues or spring training leagues for this analysis.
Once we have clean data, we run the analysis that correlates common hitting metrics with runs. The results of the analysis show that OPS is the single most important metric with a correlation of 0.95 (extremely correlated to runs.)
(Stats as of May 28th)
With the information above we wanted to see how OPS as a team statistic influenced the Top 25 Teams in NCAA DI baseball. Below we have the rankings as of May 28th, 2024 from d1baseball.com. Find a detailed breakdown below:
Quick takeaways:
Most of us spent our careers looking to batting average as the most important statistic, when in reality OPS is. As you climb the ladder in baseball there is a premium placed on “damage”. Which really means “can you hit extra base hits?”.
With OPS reaping rewards for players and teams at higher levels it begs the question of “should OPS become the go to statistic at a younger age?”.