#Fangraphs swing and miss percentage driver#

A player with good on-base skills but limited power works at the top or bottom of a lineup, but if you want to score runs in today’s game, you need guys who can slug. The second question is, what does this mean? Well, I suppose we shouldn’t look at on-base percentage in a vacuum, because OBP alone isn’t the best descriptor of scoring. Again, this isn’t a really strong relationship, but you can kind of see it. When runs are harder to come by - Deadball II, or the current game - it’s harder to bring around a runner to score without the longball. When there are a lot runs being scored - the 1930s, the Steroid Era - all you need to do is get guys on base, because the batters behind them stand a good chance of driving them in. The negative number means that the more runs scored per game, the more on-base percentage, rather than slugging percentage, correlates to scoring. Here’s runs per game, with a correlation coefficient of -0.35. I tossed out the four clear outliers on the left side of the graph (1914-16, 1915-17, 1916-18, 1917-19), and the best correlations I got were still less than 0.40. To try to answer that question, I ran another set of correlations, comparing the slugging percentage minus on-base percentage correlations to various per-game measures: runs, hits, home runs, doubles, triples, etc. The first one is: Why? The graph isn’t random there are somewhat distinct periods during which either on-base percentage or slugging percentage is better correlated to scoring. Ol’ Billy Beane, he knew what he was doing. The one notable exception: the years 1995-1997 through 2000-2002, during which on-base percentage ruled.Beginning with 1946-1948, there have been 68 three-year spans, and in only 19 of them (28%) did on-base percentage do a better job of explaining run scoring than slugging percentage.

#Fangraphs swing and miss percentage driver#

Since World War II, slugging percentage has been, pretty clearly, the more important driver of offense.Three Blue Jays matched or beat that number last year. The Giants led the majors with 39 home runs in 1917. There were dilution-of-talent issues through 1915, when the Federal League operated. The Deadball years were extreme outliers.I looked at three-year periods (to smooth out the data) from 1914 to 2015, so on the graph below, the label 1916 represents the years 1914-1916. A positive number means that slugging percentage did a better job of explaining scoring, and a negative number means that on-base percentage did better. I calculated the correlation coefficient between slugging percentage and scoring, minus the correlation coefficient between on-base percentage and scoring. Slugging percentage, not on-base percentage, is most closely linked to run scoring in modern baseball. But slugging percentage explains scoring better in the period 1939-2015 and every subsequent span ending in the present. On-base percentage has a higher correlation coefficient to scoring than slugging percentage for the period 1914-2015. Slugging percentage, not on-base percentage, is on a 14-year run as the best predictor of offense.Īnd it turns out that the choice of endpoints matter. And 2012, and 2011, and 2010, and 2009, and every single year starting in the Moneyball season of 2002. The correlation coefficient between on-base percentage and runs per game for the 30 teams last year was just 0.644, compared to 0.875 for slugging percentage. Batting average, unsurprisingly, is worse (0.812), while OPS, also unsurprisingly, is better (0.944).īut that difference doesn’t mean that OBP>SLG is an iron rule. Slugging percentage is close behind, at 0.867. That means, roughly, that you can explain nearly 80% of a team’s scoring by looking at its on-base percentage. And, it turns out, it’s pretty high - 0.890. I calculated the correlation coefficient between a team’s on-base percentage and its runs per game. To check, I looked at every team from 1914 through 2015 - the entire Retrosheet era, encompassing 2,198 team-seasons. You got on base, you mess with the pitcher’s windup and the fielders’ alignment, and good things can happen, scoring-wise. The second reason is that it makes intuitive sense. Michael Lewis demonstrated how there was a market inefficiency in valuing players with good on-base skills in 2002. I think there are two reasons for on-base percentage’s popularity.