wOBA is considered one of the best stats to quantify batting performance. But what is most likely to contribute to wOBA? Is it more important to have a strong BB/K rate or to produce on balls thrown in the strike zone that are put into play? Is production when hitting fly balls more important than hitting ground balls and if so by how much? In order to determine this, I used 2013-2015 data from ESPN Stats and Information and Pitch FX to see what correlates best with overall wOBA.
The first test I ran used data from ESPN Stats and Information. I measured players wOBA based on balls into play that were thrown in the strike zone, wOBA on balls put into play that were thrown out of the strike zone and wOBA based on balls not put into play at all …show more content…
The next most influential variable was wOBA on pitches which weren’t put into play. On average 85% of all at bats end in one of the two above scenarios. There is a weaker relationship between actual wOBA rank and the percentage of balls in each of the three categories suggesting that this has some relevance but not a huge amount. In general, the percent of pitches resulted in strikes or balls put into play had a positive impact on total wOBA (obviously, putting strikes into play is better than putting balls into play) while failing to put a ball into play had a negative …show more content…
As a result, it should not be surprising that how a player does against strikes has the largest impact on his wOBA by a significant margin. In general, players will live and die based on how they do against the best pitches.
As with the ESPN data, a players’ year-to-year profile stays reasonably static. There’s a strong correlation of about .73 between a players current “In Play Percentile Ranks” and his rankings the following year. There is a smaller correlation of .516 between a players’ current “wOBA against strikes” percentile rank and his rank in the following year. All in all, it shows that we can be reasonably certain that a player that swings at bad pitches will continue to do so in the future but that this dataset is better used to describe what happened rather than to predict what will happen. This chart can be found below:
The third dataset that I looked at was also PitchF/x data from 2013-2015 based on players that faced at least 1000 pitches in a year. For this dataset, I determined their wOBA percentile rank based on batted ball type (fly ball, ground ball, line drive and pop
The first test I ran used data from ESPN Stats and Information. I measured players wOBA based on balls into play that were thrown in the strike zone, wOBA on balls put into play that were thrown out of the strike zone and wOBA based on balls not put into play at all …show more content…
The next most influential variable was wOBA on pitches which weren’t put into play. On average 85% of all at bats end in one of the two above scenarios. There is a weaker relationship between actual wOBA rank and the percentage of balls in each of the three categories suggesting that this has some relevance but not a huge amount. In general, the percent of pitches resulted in strikes or balls put into play had a positive impact on total wOBA (obviously, putting strikes into play is better than putting balls into play) while failing to put a ball into play had a negative …show more content…
As a result, it should not be surprising that how a player does against strikes has the largest impact on his wOBA by a significant margin. In general, players will live and die based on how they do against the best pitches.
As with the ESPN data, a players’ year-to-year profile stays reasonably static. There’s a strong correlation of about .73 between a players current “In Play Percentile Ranks” and his rankings the following year. There is a smaller correlation of .516 between a players’ current “wOBA against strikes” percentile rank and his rank in the following year. All in all, it shows that we can be reasonably certain that a player that swings at bad pitches will continue to do so in the future but that this dataset is better used to describe what happened rather than to predict what will happen. This chart can be found below:
The third dataset that I looked at was also PitchF/x data from 2013-2015 based on players that faced at least 1000 pitches in a year. For this dataset, I determined their wOBA percentile rank based on batted ball type (fly ball, ground ball, line drive and pop